While the robotics research community has driven recent advances that enable robots to grasp a wide range of rigid objects, less research has been devoted to developing algorithms that can handle deformable objects. One of the challenges in deformable object manipulation is that it is difficult to specify such an object's configuration. For example, with a rigid cube, knowing the configuration of a fixed point relative to its center is sufficient to describe its arrangement in 3D space, but a single point on a piece of fabric can remain fixed while other parts shift. This makes it difficult for perception algorithms to describe the complete “state” of the fabric, especially under occlusions. In addition, even if one has a sufficiently descriptive state representation of a deformable object, its dynamics are complex. This makes it difficult to predict the future state of the deformable object after some action is applied to it, which is often needed for multi-step planning algorithms.
In "Learning to Rearrange Deformable Cables, Fabrics, and Bags with Goal-Conditioned Transporter Networks," to appear at ICRA 2021, we release an open-source simulated benchmark, called DeformableRavens, with the goal of accelerating research into deformable object manipulation. DeformableRavens features 12 tasks that involve manipulating cables, fabrics, and bags and includes a set of model architectures for manipulating deformable objects towards desired goal configurations, specified with images. These architectures enable a robot to rearrange cables to match a target shape, to smooth a fabric to a target zone, and to insert an item in a bag. To our knowledge, this is the first simulator that includes a task in which a robot must use a bag to contain other items, which presents key challenges in enabling a robot to learn more complex relative spatial relations.
The DeformableRavens Benchmark DeformableRavens expands our prior work on rearranging objects and includes a suite of 12 simulated tasks involving 1D, 2D, and 3D deformable structures. Each task contains a simulated UR5 arm with a mock gripper for pinch grasping, and is bundled with scripted demonstrators to autonomously collect data for imitation learning. Tasks randomize the starting state of the items within a distribution to test generality to different object configurations.
Specifying goal configurations for manipulation tasks can be particularly challenging with deformable objects. Given their complex dynamics and high-dimensional configuration spaces, goals cannot be as easily specified as a set of rigid object poses, and may involve complex relative spatial relations, such as “place the item inside the bag”. Hence, in addition to tasks defined by the distribution of scripted demonstrations, our benchmark also contains goal-conditioned tasks that are specified with goal images. For goal-conditioned tasks, a given starting configuration of objects must be paired with a separate image that shows the desired configuration of those same objects. A success for that particular case is then based on whether the robot is able to get the current configuration to be sufficiently close to the configuration conveyed in the goal image.
Goal-Conditioned Transporter Networks To complement the goal-conditioned tasks in our simulated benchmark, we integrated goal-conditioning into our previously released Transporter Network architecture — an action-centric model architecture that works well on rigid object manipulation by rearranging deep features to infer spatial displacements from visual input. The architecture takes as input both an image of the current environment and a goal image with a desired final configuration of objects, computes deep visual features for both images, then combines the features using element-wise multiplication to condition pick and place correlations to manipulate both the rigid and deformable objects in the scene. A strength of the Transporter Network architecture is that it preserves the spatial structure of the visual images, which provides inductive biases that reformulate image-based goal conditioning into a simpler feature matching problem and improves the learning efficiency with convolutional networks.
An example task involving goal-conditioning is shown below. In order to place the green block into the yellow bag, the robot needs to learn spatial features that enable it to perform a multi-step sequence of actions to spread open the top opening of the yellow bag, before placing the block into it. After it places the block into the yellow bag, the demonstration ends in a success. If in the goal image the block were placed in the blue bag, then the demonstrator would need to put the block in the blue bag.
Results Our results suggest that goal-conditioned Transporter Networks enable agents to manipulate deformable structures into flexibly specified configurations without test-time visual anchors for target locations. We also significantly extend prior results using Transporter Networks for manipulating deformable objects by testing on tasks with 2D and 3D deformables. Results additionally suggest that the proposed approach is more sample-efficient than alternative approaches that rely on using ground-truth pose and vertex position instead of images as input.
For example, the learned policies can effectively simulate bagging tasks, and one can also provide a goal image so that the robot must infer into which bag the item should be placed.
We encourage other researchers to check out our open-source code to try the simulated environments and to build upon this work. For more details, please check out our paper.
Future Work This work exposes several directions for future development, including the mitigation of observed failure modes. As shown below, one failure is when the robot pulls the bag upwards and causes the item to fall out. Another is when the robot places the item on the irregular exterior surface of the bag, which causes the item to fall off. Future algorithmic improvements might allow actions that operate at a higher frequency rate, so that the robot can react in real time to counteract such failures.
Another area for advancement is to train Transporter Network-based models for deformable object manipulation using techniques that do not require expert demonstrations, such as example-based control or model-based reinforcement learning. Finally, the ongoing pandemic limited access to physical robots, so in future work we will explore the necessary ingredients to get a system working with physical bags, and to extend the system to work with different types of bags.
Acknowledgments This research was conducted during Daniel Seita's internship at Google’s NYC office in Summer 2020. We thank our collaborators Pete Florence, Jonathan Tompson, Erwin Coumans, Vikas Sindhwani, and Ken Goldberg.
Learning good visual and vision-language representations is critical to solving computer vision problems — image retrieval, image classification, video understanding — and can enable the development of tools and products that change people’s daily lives. For example, a good vision-language matching model can help users find the most relevant images given a text description or an image input and help tools such as Google Lens find more fine-grained information about an image.
To learn such representations, current state-of-the-art (SotA) visual and vision-language models rely heavily on curated training datasets that require expert knowledge and extensive labels. For vision applications, representations are mostly learned on large-scale datasets with explicit class labels, such as ImageNet, OpenImages, and JFT-300M. For vision-language applications, popular pre-training datasets, such as Conceptual Captions and Visual Genome Dense Captions, all require non-trivial data collection and cleaning steps, limiting the size of datasets and thus hindering the scale of the trained models. In contrast, natural language processing (NLP) models have achieved SotA performance on GLUE and SuperGLUE benchmarks by utilizing large-scale pre-training on raw text without human labels.
In "Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision", to appear at ICML 2021, we propose bridging this gap with publicly available image alt-text data (written copy that appears in place of an image on a webpage if the image fails to load on a user's screen) in order to train larger, state-of-the-art vision and vision-language models. To that end, we leverage a noisy dataset of over one billion image and alt-text pairs, obtained without expensive filtering or post-processing steps in the Conceptual Captions dataset. We show that the scale of our corpus can make up for noisy data and leads to SotA representation, and achieves strong performance when transferred to classification tasks such as ImageNet and VTAB. The aligned visual and language representations also set new SotA results on Flickr30K and MS-COCO benchmarks, even when compared with more sophisticated cross-attention models, and enable zero-shot image classification and cross-modality search with complex text and text + image queries.
Creating the Dataset Alt-texts usually provide a description of what the image is about, but the dataset is “noisy” because some text may be partly or wholly unrelated to its paired image.
In this work, we follow the methodology of constructing the Conceptual Captions dataset to get a version of raw English alt-text data (image and alt-text pairs). While the Conceptual Captions dataset was cleaned by heavy filtering and post-processing, this work scales up visual and vision-language representation learning by relaxing most of the cleaning steps in the original work. Instead, we only apply minimal frequency-based filtering. The result is a much larger but noisier dataset of 1.8B image-text pairs.
ALIGN: A Large-scale ImaGe and Noisy-Text Embedding For the purpose of building larger and more powerful models easily, we employ a simple dual-encoder architecture that learns to align visual and language representations of the image and text pairs. Image and text encoders are learned via a contrastive loss (formulated as normalized softmax) that pushes the embeddings of matched image-text pairs together while pushing those of non-matched image-text pairs (within the same batch) apart. The large-scale dataset makes it possible for us to scale up the model size to be as large as EfficientNet-L2 (image encoder) and BERT-large (text encoder) trained from scratch. The learned representation can be used for downstream visual and vision-language tasks.
The resulting representation can be used for vision-only or vision-language task transfer. Without any fine-tuning, ALIGN powers cross-modal search – image-to-text search, text-to-image search, and even search with joint image+text queries, examples below.
Evaluating Retrieval and Representation The learned ALIGN model with BERT-Large and EfficientNet-L2 as text and image encoder backbones achieves SotA performance on multiple image-text retrieval tasks (Flickr30K and MS-COCO) in both zero-shot and fine-tuned settings, as shown below.
ALIGN is also a strong image representation model. Shown below, with frozen features, ALIGN slightly outperforms CLIP and achieves a SotA result of 85.5% top-1 accuracy on ImageNet. With fine-tuning, ALIGN achieves higher accuracy than most generalist models, such as BiT and ViT, and is only worse than Meta Pseudo Labels, which requires deeper interaction between ImageNet training and large-scale unlabeled data.
Zero-Shot Image Classification Traditionally, image classification problems treat each class as independent IDs, and people have to train the classification layers with at least a few shots of labeled data per class. The class names are actually also natural language phrases, so we can naturally extend the image-text retrieval capability of ALIGN for image classification without any training data.
On the ImageNet validation dataset, ALIGN achieves 76.4% top-1 zero-shot accuracy and shows great robustness in different variants of ImageNet with distribution shifts, similar to the concurrent work CLIP. We also use the same text prompt engineering and ensembling as in CLIP.
Application in Image Search To illustrate the quantitative results above, we build a simple image retrieval system with the embeddings trained by ALIGN and show the top 1 text-to-image retrieval results for a handful of text queries from a 160M image pool. ALIGN can retrieve precise images given detailed descriptions of a scene, or fine-grained or instance-level concepts like landmarks and artworks. These examples demonstrate that the ALIGN model can align images and texts with similar semantics, and that ALIGN can generalize to novel complex concepts.
Multimodal (Image+Text) Query for Image Search A surprising property of word vectors is that word analogies can often be solved with vector arithmetic. A common example, "king – man + woman = queen". Such linear relationships between image and text embeddings also emerge in ALIGN.
Specifically, given a query image and a text string, we add their ALIGN embeddings together and use it to retrieve relevant images using cosine similarity, as shown below. These examples not only demonstrate the compositionality of ALIGN embeddings across vision and language domains, but also show the feasibility of searching with a multi-modal query. For instance, one could now look for the "Australia" or "Madagascar" equivalence of pandas, or turn a pair of black shoes into identically-looking beige shoes. Also, it is possible to remove objects/attributes from a scene by performing subtraction in the embedding space, shown below.
Social Impact and Future Work While this work shows promising results from a methodology perspective with a simple data collection method, additional analysis of the data and the resulting model is necessary before the responsible use of the model in practice. For instance, considerations should be made towards the potential for the use of harmful text data in alt-texts to reinforce such harms. With regard to fairness, data balancing efforts may be required to prevent reinforcing stereotypes from the web data. Additional testing and training around sensitive religious or cultural items should be taken to understand and mitigate the impact from possibly mislabeled data.
Further analysis should also be taken to ensure that the demographic distribution of humans and related cultural items, such as clothing, food, and art, do not cause skewed model performance. Analysis and balancing would be required if such models will be used in production.
Conclusion We have presented a simple method of leveraging large-scale noisy image-text data to scale up visual and vision-language representation learning. The resulting model, ALIGN, is capable of cross-modal retrieval and significantly outperforms SotA models. In visual-only downstream tasks, ALIGN is also comparable to or outperforms SotA models trained with large-scale labeled data.
Acknowledgement We would like to thank our co-authors in Google Research: Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc V. Le, Yunhsuan Sung, Zhen Li, Tom Duerig. This work was also done with invaluable help from other colleagues from Google. We would like to thank Jan Dlabal and Zhe Li for continuous support in training infrastructure, Simon Kornblith for building the zero-shot & robustness model evaluation on ImageNet variants, Xiaohua Zhai for help on conducting VTAB evaluation, Mingxing Tan and Max Moroz for suggestions on EfficientNet training, Aleksei Timofeev for the early idea of multimodal query retrieval, Aaron Michelony and Kaushal Patel for their early work on data generation, and Sergey Ioffe, Jason Baldridge and Krishna Srinivasan for the insightful feedback and discussion.
Eye movement has been studied widely across vision science, language, and usability since the 1970s. Beyond basic research, a better understanding of eye movement could be useful in a wide variety of applications, ranging across usability and user experience research, gaming, driving, and gaze-based interaction for accessibility to healthcare. However, progress has been limited because most prior research has focused on specialized hardware-based eye trackers that are expensive and do not easily scale.
In “Accelerating eye movement research via accurate and affordable smartphone eye tracking”, published in Nature Communications, and “Digital biomarker of mental fatigue”, published in npj Digital Medicine, we present accurate, smartphone-based, ML-powered eye tracking that has the potential to unlock new research into applications across the fields of vision, accessibility, healthcare, and wellness, while additionally providing orders-of-magnitude scaling across diverse populations in the world, all using the front-facing camera on a smartphone. We also discuss the potential use of this technology as a digital biomarker of mental fatigue, which can be useful for improved wellness.
Model Overview The core of our gaze model was a multilayer feed-forward convolutional neural network (ConvNet) trained on the MIT GazeCapture dataset. A face detection algorithm selected the face region with associated eye corner landmarks, which were used to crop the images down to the eye region alone. These cropped frames were fed through two identical ConvNet towers with shared weights. Each convolutional layer was followed by an average pooling layer. Eye corner landmarks were combined with the output of the two towers through fully connected layers. Rectified Linear Units (ReLUs) were used for all layers except the final fully connected output layer (FC6), which had no activation.
The unpersonalized gaze model accuracy was improved by fine-tuning and per-participant personalization. For the latter, a lightweight regression model was fitted to the model’s penultimate ReLU layer and participant-specific data.
Model Evaluation To evaluate the model, we collected data from consenting study participants as they viewed dots that appeared at random locations on a blank screen. The model error was computed as the distance (in cm) between the stimulus location and model prediction. Results show that while the unpersonalized model has high error, personalization with ~30s of calibration data led to an over fourfold error reduction (from 1.92 to 0.46cm). At a viewing distance of 25-40 cm, this corresponds to 0.6-1° accuracy, a significant improvement over the 2.4-3° reported in previous work [1, 2].
Additional experiments show that the smartphone eye tracker model’s accuracy is comparable to state-of-the-art wearable eye trackers both when the phone is placed on a device stand, as well as when users hold the phone freely in their hand in a near frontal headpose. In contrast to specialized eye tracking hardware with multiple infrared cameras close to each eye, running our gaze model using a smartphone’s single front-facing RGB camera is significantly more cost effective (~100x cheaper) and scalable.
Using this smartphone technology, we were able to replicate key findings from prior eye movement research in neuroscience and psychology, including standard oculomotor tasks (to understand basic visual functioning in the brain) and natural image understanding. For example, in a simple prosaccade task, which tests a person’s ability to quickly move their eyes towards a stimulus that appears on the screen, we found that the average saccade latency (time to move the eyes) matches prior work for basic visual health (210ms versus 200-250ms). In controlled visual search tasks, we were able to replicate key findings, such as the effect of target saliency and clutter on eye movements.
For complex stimuli, such as natural images, we found that the gaze distribution (computed by aggregating gaze positions across all participants) from our smartphone eye tracker are similar to those obtained from bulky, expensive eye trackers that used highly controlled settings, such as laboratory chin rest systems. While the smartphone-based gaze heatmaps have a broader distribution (i.e., they appear more “blurred”) than hardware-based eye trackers, they are highly correlated both at the pixel level (r = 0.74) and object level (r = 0.90). These results suggest that this technology could be used to scale gaze analysis for complex stimuli such as natural and medical images (e.g., radiologists viewing MRI/PET scans).
We found that smartphone gaze could also help detect difficulty with reading comprehension. Participants reading passages spent significantly more time looking within the relevant excerpts when they answered correctly. However, as comprehension difficulty increased, they spent more time looking at the irrelevant excerpts in the passage before finding the relevant excerpt that contained the answer. The fraction of gaze time spent on the relevant excerpt was a good predictor of comprehension, and strongly negatively correlated with comprehension difficulty (r = −0.72).
Digital Biomarker of Mental Fatigue Gaze detection is an important tool to detect alertness and wellbeing, and is studied widely in medicine, sleep research, and mission-critical settings such as medical surgeries, aviation safety, etc. However, existing fatigue tests are subjective and often time-consuming. In our recent paper published in npj Digital Medicine, we demonstrated that smartphone gaze is significantly impaired with mental fatigue, and can be used to track the onset and progression of fatigue.
A simple model predicts mental fatigue reliably using just a few minutes of gaze data from participants performing a task. We validated these findings in two different experiments — using a language-independent object-tracking task and a language-dependent proofreading task. As shown below, in the object-tracking task, participants’ gaze initially follows the object’s circular trajectory, but under fatigue, their gaze shows high errors and deviations. Given the pervasiveness of phones, these results suggest that smartphone-based gaze could provide a scalable, digital biomarker of mental fatigue.
Beyond wellness, smartphone gaze could also provide a digital phenotype for screening or monitoring health conditions such as autism spectrum disorder, dyslexia, concussion and more. This could enable timely and early interventions, especially for countries with limited access to healthcare services.
Another area that could benefit tremendously is accessibility. People with conditions such as ALS, locked-in syndrome and stroke have impaired speech and motor ability. Smartphone gaze could provide a powerful way to make daily tasks easier by using gaze for interaction, as recently demonstrated with Look to Speak.
Ethical Considerations Gaze research needs careful consideration, including being mindful of the correct use of such technology — applications should obtain explicit approval and fully informed consent from users for the specific task at hand. In our work, all data was collected for research purposes with users’ explicit approval and consent. In addition, users were allowed to opt out at any point and request their data to be deleted. We continue to research additional ways to ensure ML fairness and improve the accuracy and robustness of gaze technology across demographics, in a responsible, privacy-preserving way.
Conclusion Our findings of accurate and affordable ML-powered smartphone eye tracking offer the potential for orders-of-magnitude scaling of eye movement research across disciplines (e.g., neuroscience, psychology and human-computer interaction). They unlock potential new applications for societal good, such as gaze-based interaction for accessibility, and smartphone-based screening and monitoring tools for wellness and healthcare.
Acknowledgements This work involved collaborative efforts from a multidisciplinary team of software engineers, researchers, and cross-functional contributors. We’d like to thank all the co-authors of the papers, including our team members, Junfeng He, Na Dai, Pingmei Xu, Venky Ramachandran; interns, Ethan Steinberg, Kantwon Rogers, Li Guo, and Vincent Tseng; collaborators, Tanzeem Choudhury; and UXRs: Mina Shojaeizadeh, Preeti Talwai, and Ran Tao. We’d also like to thank Tomer Shekel, Gaurav Nemade, and Reena Lee for their contributions to this project, and Vidhya Navalpakkam for her technical leadership in initiating and overseeing this body of work.
The past decade has seen remarkable progress on automatic image captioning, a task in which a computer algorithm creates written descriptions for images. Much of the progress has come through the use of modern deep learning methods developed for both computer vision and natural language processing, combined with large scale datasets that pair images with descriptions created by people. In addition to supporting important practical applications, such as providing descriptions of images for visually impaired people, these datasets also enable investigations into important and exciting research questions about grounding language in visual inputs. For example, learning deep representations for a word like “car”, means using both linguistic and visual contexts.
Image captioning datasets that contain pairs of textual descriptions and their corresponding images, such as MS-COCO and Flickr30k, have been widely used to learn aligned image and text representations and to build captioning models. Unfortunately, these datasets have limited cross-modal associations: images are not paired with other images, captions are only paired with other captions of the same image (also called co-captions), there are image-caption pairs that match but are not labeled as a match, and there are no labels that indicate when an image-caption pair does not match. This undermines research into how inter-modality learning (connecting captions to images, for example) impacts intra-modality tasks (connecting captions to captions or images to images). This is important to address, especially because a fair amount of work on learning from images paired with text is motivated by arguments about how visual elements should inform and improve representations of language.
To address this evaluation gap, we present "Crisscrossed Captions: Extended Intramodal and Intermodal Semantic Similarity Judgments for MS-COCO", which was recently presented at EACL 2021. The Crisscrossed Captions (CxC) dataset extends the development and test splits of MS-COCO with semantic similarity ratings for image-text, text-text and image-image pairs. The rating criteria are based on Semantic Textual Similarity, an existing and widely-adopted measure of semantic relatedness between pairs of short texts, which we extend to include judgments about images as well. In all, CxC contains human-derived semantic similarity ratings for 267,095 pairs (derived from 1,335,475 independent judgments), a massive extension in scale and detail to the 50k original binary pairings in MS-COCO’s development and test splits. We have released CxC’s ratings, along with code to merge CxC with existing MS-COCO data. Anyone familiar with MS-COCO can thus easily enhance their experiments with CxC.
Creating the CxC Dataset If a picture is worth a thousand words, it is likely because there are so many details and relationships between objects that are generally depicted in pictures. We can describe the texture of the fur on a dog, name the logo on the frisbee it is chasing, mention the expression on the face of the person who has just thrown the frisbee, or note the vibrant red on a large leaf in a tree above the person’s head, and so on.
The CxC dataset extends the MS-COCO evaluation splits with graded similarity associations within and across modalities. MS-COCO has five captions for each image, split into 410k training, 25k development, and 25k test captions (for 82k, 5k, 5k images, respectively). An ideal extension would rate every pair in the dataset (caption-caption, image-image, and image-caption), but this is infeasible as it would require obtaining human ratings for billions of pairs.
Given that randomly selected pairs of images and captions are likely to be dissimilar, we came up with a way to select items for human rating that would include at least some new pairs with high expected similarity. To reduce the dependence of the chosen pairs on the models used to find them, we introduce an indirect sampling scheme (depicted below) where we encode images and captions using different encoding methods and compute the similarity between pairs of same modality items, resulting in similarity matrices. Images are encoded using Graph-RISE embeddings, while captions are encoded using two methods — Universal Sentence Encoder (USE) and average bag-of-words (BoW) based on GloVe embeddings. Since each MS-COCO example has five co-captions, we average the co-caption encodings to create a single representation per example, ensuring all caption pairs can be mapped to image pairs (more below on how we select intermodality pairs).
The next step of the indirect sampling scheme is to use the computed similarities of images for a biased sampling of caption pairs for human rating (and vice versa). For example, we select two captions with high computed similarities from the text similarity matrix, then take each of their images, resulting in a new pair of images that are different in appearance but similar in what they depict based on their descriptions. For example, the captions “A dog looking bashfully to the side” and “A black dog lifts its head to the side to enjoy a breeze” would have a reasonably high model similarity, so the corresponding images of the two dogs in the figure below could be selected for image similarity rating. This step can also start with two images with high computed similarities to yield a new pair of captions. We now have indirectly sampled new intramodal pairs — at least some of which are highly similar — for which we obtain human ratings.
Last, we then use these new intramodal pairs and their human ratings to select new intermodal pairs for human rating. We do this by using existing image-caption pairs to link between modalities. For example, if a caption pair example ij was rated by humans as highly similar, we pick the image from example i and caption from example j to obtain a new intermodal pair for human rating. And again, we use the intramodal pairs with the highest rated similarity for sampling because this includes at least some new pairs with high similarity. Finally, we also add human ratings for all existing intermodal pairs and a large sample of co-captions.
The following table shows examples of semantic image similarity (SIS) and semantic image-text similarity (SITS) pairs corresponding to each rating, with 5 being the most similar and 0 being completely dissimilar.
Evaluation MS-COCO supports three retrieval tasks:
MS-COCO’s pairs are incomplete because captions created for one image at times apply equally well to another, yet these associations are not captured in the dataset. CxC enhances these existing retrieval tasks with new positive pairs, and it also supports a new image-image retrieval task. With its graded similarity judgements, CxC also makes it possible to measure correlations between model and human rankings. Retrieval metrics in general focus only on positive pairs, while CxC’s correlation scores additionally account for the relative ordering of similarity and include low-scoring items (non-matches). Supporting these evaluations on a common set of images and captions makes them more valuable for understanding inter-modal learning compared to disjoint sets of caption-image, caption-caption, and image-image associations.
We ran a series of experiments to show the utility of CxC’s ratings. For this, we constructed three dual encoder (DE) models using BERT-base as the text encoder and EfficientNet-B4 as the image encoder:
From the results on the retrieval tasks, we can see that DE_I2T+T2T (yellow bar) performs better than DE_I2T (red bar) on the image-text and text-image retrieval tasks. Thus, adding the intramodal (text-text) training task helped improve the intermodal (image-text, text-image) performance. As for the other two intramodal tasks (text-text and image-image), DE_I2T+T2T shows strong, balanced performance on both of them.
For the correlation tasks, DE_I2T performs the best on SIS and DE_I2T+T2T is the best overall. The correlation scores also show that DE_I2T performs well only on images: it has the highest SIS but has much worse STS. Adding the text-text loss to DE_I2T training (DE_I2T+T2T) produces more balanced overall performance.
The CxC dataset provides a much more complete set of relationships between and among images and captions than the raw MS-COCO image-caption pairs. The new ratings have been released and further details are in our paper. We hope to encourage the research community to push the state of the art on the tasks introduced by CxC with better models for jointly learning inter- and intra-modal representations.
Acknowledgments The core team includes Daniel Cer, Yinfei Yang and Austin Waters. We thank Julia Hockenmaier for her inputs on CxC’s formulation, the Google Data Compute Team, especially Ashwin Kakarla and Mohd Majeed for their tooling and annotation support, Yuan Zhang, Eugene Ie for their comments on the initial versions of the paper and Daphne Luong for executive support for the data collection.
*All the images in the article have been taken from the Open Images dataset under the CC-by 4.0 license. ↩
Sequence-to-sequence (seq2seq) models have become a favoured approach for tackling natural language generation tasks, with applications ranging from machine translation to monolingual generation tasks, such as summarization, sentence fusion, text simplification, and machine translation post-editing. However these models appear to be a suboptimal choice for many monolingual tasks, as the desired output text often represents a minor rewrite of the input text. When accomplishing such tasks, seq2seq models are both slower because they generate the output one word at a time (i.e., autoregressively), and wasteful because most of the input tokens are simply copied into the output.
Instead, text-editing models have recently received a surge of interest as they propose to predict edit operations – such as word deletion, insertion, or replacement – that are applied to the input to reconstruct the output. However, previous text-editing approaches have limitations. They are either fast (being non-autoregressive), but not flexible, because they use a limited number of edit operations, or they are flexible, supporting all possible edit operations, but slow (autoregressive). In either case, they have not focused on modeling large structural (syntactic) transformations, for example switching from active voice, “They ate steak for dinner,” to passive, “Steak was eaten for dinner.” Instead, they've focused on local transformations, deleting or replacing short phrases. When a large structural transformation needs to occur, they either can’t produce it or insert a large amount of new text, which is slow.
In “FELIX: Flexible Text Editing Through Tagging and Insertion”, we introduce FELIX, a fast and flexible text-editing system that models large structural changes and achieves a 90x speed-up compared to seq2seq approaches whilst achieving impressive results on four monolingual generation tasks. Compared to traditional seq2seq methods, FELIX has the following three key advantages:
In short, FELIX is designed to derive the maximum benefit from self-supervised pre-training, being efficient in low-resource settings, with little training data.
Overview To achieve the above, FELIX decomposes the text-editing task into two sub-tasks: tagging to decide on the subset of input words and their order in the output text, and insertion, where words that are not present in the input are inserted. The tagging model employs a novel pointer mechanism, which supports structural transformations, while the insertion model is based on a Masked Language Model. Both of these models are non-autoregressive, ensuring the model is fast. A diagram of FELIX can be seen below.
The Tagging Model The first step in FELIX is the tagging model, which consists of two components. First the tagger determines which words should be kept or deleted and where new words should be inserted. When the tagger predicts an insertion, a special MASK token is added to the output. After tagging, there is a reordering step where the pointer reorders the input to form the output, by which it is able to reuse parts of the input instead of inserting new text. The reordering step supports arbitrary rewrites, which enables modeling large changes. The pointer network is trained such that each word in the input points to the next word as it will appear in the output, as shown below.
The Insertion Model The output of the tagging model is the reordered input text with deleted words and MASK tokens predicted by the insertion tag. The insertion model must predict the content of MASK tokens. Because FELIX’s insertion model is very similar to the pretraining objective of BERT, it can take direct advantage of the pre-training, which is particularly advantageous when data is limited.
Results We evaluated FELIX on sentence fusion, text simplification, abstractive summarization, and machine translation post-editing. These tasks vary significantly in the types of edits required and dataset sizes under which they operate. Below are the results on the sentence fusion task (i.e., merging two sentences into one), comparing FELIX against a large pre-trained seq2seq model (BERT2BERT) and a text-editing model (LaserTager), under a range of dataset sizes. We see that FELIX outperforms LaserTagger and can be trained on as little as a few hundred training examples. For the full dataset, the autoregressive BERT2BERT outperforms FELIX. However, during inference, this model takes significantly longer.
Conclusion We have presented FELIX, which is fully non-autoregressive, providing even faster inference times, while achieving state-of-the-art results. FELIX also minimizes the amount of required training data with three techniques — fine-tuning pre-trained checkpoints, learning a small number of edit operations, and an insertion task that mimics masked language model task from the pre-training. Lastly, FELIX strikes a balance between the complexity of learned edit operations and the percentage of input-output transformations it can handle. We have open-sourced the code for FELIX and hope it will provide researchers with a faster, more efficient, and more flexible text-editing model.
Acknowledgements This research was conducted by Jonathan Mallinson, Aliaksei Severyn (equal contribution), Eric Malmi, Guillermo Garrido. We would like to thank Aleksandr Chuklin, Daniil Mirylenka, Ryan McDonald, and Sebastian Krause for useful discussions, running early experiments and paper suggestions.
A common practice to improve a neural network’s performance and tailor it to available computational resources is to adjust the architecture depth and width. Indeed, popular families of neural networks, including EfficientNet, ResNet and Transformers, consist of a set of architectures of flexible depths and widths. However, beyond the effect on accuracy, there is limited understanding of how these fundamental choices of architecture design affect the model, such as the impact on its internal representations.
In “Do Wide and Deep Networks Learn the Same Things? Uncovering How Neural Network Representations Vary with Width and Depth”, we perform a systematic study of the similarity between wide and deep networks from the same architectural family through the lens of their hidden representations and final outputs. In very wide or very deep models, we find a characteristic block structure in their internal representations, and establish a connection between this phenomenon and model overparameterization. Comparisons across models demonstrate that those without the block structure show significant similarity between representations in corresponding layers, but those containing the block structure exhibit highly dissimilar representations. These properties of the internal representations in turn translate to systematically different errors at the class and example levels for wide and deep models when they are evaluated on the same test set.
Comparing Representation Similarity with CKA We extended prior work on analyzing representations by leveraging our previously developed Centered Kernel Alignment (CKA) technique, which provides a robust, scalable way to determine the similarity between the representations learned by any pair of neural network layers. CKA takes as input the representations (i.e., the activation matrices) from two layers, and outputs a similarity score between 0 (not at all similar) and 1 (identical representations).
We apply CKA to a family of ResNets of varying depths and widths, trained on common benchmark datasets (CIFAR-10, CIFAR-100 and ImageNet), and use representation heatmaps to illustrate the results. The x and y axes of each heatmap index the layers of the model(s) in consideration, going from input to output, and each entry (i, j) is the CKA similarity score between layer i and layer j.
Below is an example of the resulting heatmap when we compare representations of each layer to every other layer within a single ResNet of depth 26 and width multiplier 1. In the design convention used here, the stated depth only refers to the number of convolutional layers in the network, but we analyze all layers present, and the width multiplier applies to the number of filters in each convolution. Notice the checkerboard pattern in the heatmap, which is caused by skip connections (shortcuts between layers) in the architecture.
The Emergence of the Block Structure What stands out from the representation heatmaps of deeper or wider networks is the emergence of a large set of consecutive layers with highly similar representations, which appears in the heatmaps as a yellow square (i.e., a region with high CKA scores). This phenomenon, which we call the block structure, suggests that the underlying layers may not be as efficient at progressively refining the network’s representations as we expect. Indeed, we show that the task performance becomes stagnant inside the block structure, and that it is possible to prune some underlying layers without affecting the final performance.
With additional experiments, we show that the block structure has less to do with the absolute model size, than with the size of the model relative to the size of the training dataset. As we reduce the training dataset size, the block structure starts to appear in shallower and narrower networks:
Through further analysis, we are also able to demonstrate that the block structure arises from preserving and propagating the dominant principal components of its underlying representations. Refer to our paper for more details.
Comparing Representations Across Models Going further, we study the implications of depth and width on representations across models of different random initializations and different architectures, and find that the presence of block structure makes a significant difference in this context as well. Despite having different architectures, wide and deep models without the block structure do exhibit representation similarity with each other, with corresponding layers broadly being of the same proportional depth in the model. However, when the block structure is present, its representations are unique to each model. This suggests that despite having similar overall performance, each wide or deep model with the block structure picks up a unique mapping from the input to the output.
Error Analysis of Wide and Deep Models Having explored the properties of the learned representations of wide and deep models, we next turn to understanding how they influence the diversity of the output predictions. We train populations of networks of different architectures and determine on which test set examples each architecture configuration tends to make errors.
On both CIFAR-10 and ImageNet datasets, wide and deep models that have the same average accuracy still demonstrate statistically significant differences in example-level predictions. The same observation holds for class-level errors on ImageNet, with wide models exhibiting a small advantage in identifying classes corresponding to scenes, and deep networks being relatively more accurate on consumer goods.
Conclusions In studying the effects of depth and width on internal representations, we uncover a block structure phenomenon, and demonstrate its connection to model capacity. We also show that wide and deep models exhibit systematic output differences at class and example levels. Check out the paper for full details on these results and additional insights! We’re excited about the many interesting open questions these findings suggest, such as how the block structure arises during training, whether the phenomenon occurs in domains beyond image classification, and ways these insights on internal representations can inform model efficiency and generalization.
Acknowledgements This is a joint work with Maithra Raghu and Simon Kornblith. We would like to thank Tom Small for the visualizations of the representation heatmap.
The 9th International Conference on Learning Representations (ICLR 2021), a virtual conference focused on deep learning, kicked off this week, offering conference and workshop tracks that present some of the latest research in deep learning and its applications to areas such as computer vision, computational biology, speech recognition, text understanding, and more.
As a Platinum Sponsor of ICLR 2021, Google will have a strong presence with over 100 accepted publications and participation on organizing committees and in workshops. If you have registered for ICLR 2021, we hope you’ll watch our talks and learn about the work at Google that goes into solving interesting problems for billions of people. Learn more about our research being presented in the list below (Googlers in bold).
Officers and Board Members Includes: Hugo Larochelle, Tara Sainath
Organizing Committee Includes: Sanmi Koyejo, Chelsea Finn
Area Chairs Includes: Abhishek Kumar, Aditya Menon, Aleksandra Faust, Alexey Dosovitskiy, Andrew Cotter, Andrew Dai, Augustus Odena, Been Kim, Behnam Neyshabur, Ben Poole, Bo Dai, Bo Li, Branislav Kveton, Ce Liu, Claudio Gentile, Colin Raffel, Danny Tarlow, David Ha, Dengyong Zhou, Dumitru Erhan, Dustin Tran, Felix Hill, George Tucker, Hanie Sedghi, Heinrich Jiang, Hossein Mobahi, Izhak Shafran, Jascha Sohl-Dickstein, Jasper Snoek, Jean-Philippe Vert, Jeffrey Pennington, Justin Gilmer, Kevin Swersky, Marco Cuturi, Mario Lucic, Marlos C. Machado, Mathieu Blondel, Matt Johnson, Matthieu Geist, Mohammad Norouzi, Naman Agarwal, Navdeep Jaitly, Nicolas Le Roux, Niki Parmar, Olivier Bachem, Olivier Pietquin, Philip Long, Quentin Berthet, Razvan Pascanu, Rodolphe Jenatton, Samy Bengio*, Sebastian Nowozin, Silvio Lattanzi, Slav Petrov, Srinadh Bhojanapalli, Suman Ravuri, Tim Salimans, Vitaly Kuznetsov, William Cohen, Yann Dauphin, Yujia Li
Publications Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes Mike Gartrell, Insu Han, Elvis Dohmatob, Jennifer Gillenwater, Victor-Emmanuel Brunel
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (see the blog post) Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby
Share or Not? Learning to Schedule Language-Specific Capacity for Multilingual Translation Biao Zhang*, Ankur Bapna, Rico Sennrich, Orhan Firat
Evolving Reinforcement Learning Algorithms (see the blog post) John D Co-Reyes, Yingjie Miao, Daiyi Peng, Esteban Real, Quoc V Le, Sergey Levine, Honglak Lee, Aleksandra Faust
Score-Based Generative Modeling through Stochastic Differential Equations Yang Song*, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole
What Matters for On-Policy Deep Actor-Critic Methods? A Large-Scale Study Marcin Andrychowicz, Anton Raichuk, Piotr Stańczyk, Manu Orsini, Sertan Girgin, Raphaël Marinier, Leonard Hussenot, Matthieu Geist, Olivier Pietquin, Marcin Michalski, Sylvain Gelly, Olivier Bachem
When Do Curricula Work? Xiaoxia Wu, Ethan Dyer, Behnam Neyshabur
Sharpness-aware Minimization for Efficiently Improving Generalization Pierre Foret*, Ariel Kleiner, Hossein Mobahi, Behnam Neyshabur
Gradient Vaccine: Investigating and Improving Multi-task Optimization in Massively Multilingual Models Zirui Wang*, Yulia Tsvetkov, Orhan Firat, Yuan Cao
Mathematical Reasoning via Self-supervised Skip-tree Training Markus Norman Rabe, Dennis Lee, Kshitij Bansal, Christian Szegedy
Long-Tail Learning via Logit Adjustment Aditya Krishna Menon, Sadeep Jayasumana, Ankit Singh Rawat, Himanshu Jain, Andreas Veit, Sanjiv Kumar
Are Neural Rankers Still Outperformed by Gradient Boosted Decision Trees? Zhen Qin, Le Yan, Honglei Zhuang, Yi Tay, Rama Kumar Pasumarthi, Xuanhui Wang, Michael Bendersky, Marc Najork
LambdaNetworks: Modeling Long-Range Interactions without Attention Irwan Bello
Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning Rishabh Agarwal, Marlos C. Machado, Pablo Samuel Castro, Marc G Bellemare
BUSTLE: Bottom-Up Program Synthesis Through Learning-Guided Exploration Augustus Odena, Kensen Shi, David Bieber, Rishabh Singh, Charles Sutton, Hanjun Dai
Practical Real Time Recurrent Learning with a Sparse Approximation Jacob Menick, Erich Elsen, Utku Evci, Simon Osindero, Karen Simonyan, Alex Graves
LEAF: A Learnable Frontend for Audio Classification (see the blog post) Neil Zeghidour, Olivier Teboul, Félix de Chaumont Quitry, Marco Tagliasacchi
Batch Reinforcement Learning Through Continuation Method Yijie Guo, Shengyu Feng, Nicolas Le Roux, Ed Chi, Honglak Lee, Minmin Chen
Scalable Transfer Learning with Expert Models Joan Puigcerver, Carlos Riquelme Ruiz, Basil Mustafa, Cedric Renggli*, André Susano Pinto, Sylvain Gelly, Daniel Keysers, Neil Houlsby
Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning Rishabh Agarwal, Marlos C. Machado*, Pablo Samuel Castro, Marc G Bellemare
Scaling Symbolic Methods Using Gradients for Neural Model Explanation Subham Sekhar Sahoo, Subhashini Venugopalan, Li Li, Rishabh Singh, Patrick Riley
Primal Wasserstein Imitation Learning (see the blog post) Robert Dadashi, Leonard Hussenot, Matthieu Geist, Olivier Pietquin
Reset-Free Lifelong Learning with Skill-Space Planning Kevin Lu, Aditya Grover, Pieter Abbeel, Igor Mordatch
Teaching Temporal Logics to Neural Networks Christopher Hahn, Frederik Schmitt, Jens U. Kreber, Markus Norman Rabe, Bernd Finkbeiner
Shape-Texture Debiased Neural Network Training Yingwei Li, Qihang Yu, Mingxing Tan, Jieru Mei, Peng Tang, Wei Shen, Alan Yuille, Cihang Xie
Rethinking Embedding Coupling in Pre-trained Language Models Hyung Won Chung, Thibault Fevry*, Henry Tsai, Melvin Johnson, Sebastian Ruder
Overparameterisation and Worst-Case Generalisation: Friend or Foe? Aditya Krishna Menon, Ankit Singh Rawat, Sanjiv Kumar
Single-Photon Image Classification Thomas Fischbacher, Luciano Sbaiz
Into the Wild with AudioScope: Unsupervised Audio-Visual Separation of On-Screen Sounds Efthymios Tzinis*, Scott Wisdom, Aren Jansen, Shawn Hershey, Tal Remez, Daniel P. W. Ellis, John R. Hershey
Adaptive Federated Optimization Sashank J. Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečný, Sanjiv Kumar, Hugh Brendan McMahan
Off-Dynamics Reinforcement Learning: Training for Transfer with Domain Classifiers Benjamin Eysenbach, Shreyas Chaudhari, Swapnil Asawa, Sergey Levine, Ruslan Salakhutdinov
Open Question Answering over Tables and Text Wenhu Chen*, Ming-Wei Chang, Eva Schlinger, William Yang Wang, William W. Cohen
IDF++: Analyzing and Improving Integer Discrete Flows for Lossless Compression Rianne van den Berg, Alexey A. Gritsenko, Mostafa Dehghani, Casper Kaae Sønderby, Tim Salimans
A Universal Representation Transformer Layer for Few-Shot Image Classification Lu Liu, William L. Hamilton, Guodong Long, Jing Jiang, Hugo Larochelle
Tradeoffs in Data Augmentation: An Empirical Study Raphael Gontijo-Lopes, Sylvia Smullin, Ekin Dogus Cubuk, Ethan Dyer
Coping with Label Shift via Distributionally Robust Optimisation Jingzhao Zhang, Aditya Krishna Menon, Andreas Veit, Srinadh Bhojanapalli, Sanjiv Kumar, Suvrit Sra
Rethinking Attention with Performers (see the blog post) Krzysztof Marcin Choromanski, Valerii Likhosherstov, David Dohan, Xingyou Song, Andreea Gane, Tamas Sarlos, Peter Hawkins, Jared Quincy Davis, Afroz Mohiuddin, Lukasz Kaiser, David Benjamin Belanger, Lucy J Colwell, Adrian Weller
Teaching with Commentaries Aniruddh Raghu*, Maithra Raghu, Simon Kornblith, David Duvenaud, Geoffrey Hinton
Anatomy of Catastrophic Forgetting: Hidden Representations and Task Semantics Vinay Venkatesh Ramasesh, Ethan Dyer, Maithra Raghu
Model-Based Offline Planning Arthur Argenson, Gabriel Dulac-Arnold
The Geometry of Integration in Text Classification RNNs Kyle Aitken*, Vinay Venkatesh Ramasesh, Ankush Garg, Yuan Cao, David Sussillo, Niru Maheswaranathan
On the Origin of Implicit Regularization in Stochastic Gradient Descent Samuel L Smith, Benoit Dherin, David Barrett, Soham De
The Deep Bootstrap Framework: Good Online Learners are Good Offline Generalizers (see the blog post) Preetum Nakkiran*, Behnam Neyshabur, Hanie Sedghi
Learning Energy-Based Models by Diffusion Recovery Likelihood Ruiqi Gao, Yang Song, Ben Poole, Ying Nian Wu, Diederik P Kingma
Latent Skill Planning for Exploration and Transfer Kevin Xie, Homanga Bharadhwaj, Danijar Hafner, Animesh Garg, Florian Shkurti
PseudoSeg: Designing Pseudo Labels for Semantic Segmentation Yuliang Zou*, Zizhao Zhang, Han Zhang, Chun-Liang Li, Xiao Bian, Jia-Bin Huang, Tomas Pfister
WaveGrad: Estimating Gradients for Waveform Generation Nanxin Chen*, Yu Zhang, Heiga Zen, Ron J Weiss, Mohammad Norouzi, William Chan
One Network Fits All? Modular versus Monolithic Task Formulations in Neural Networks Atish Agarwala, Abhimanyu Das, Brendan Juba*, Rina Panigrahy, Vatsal Sharan*, Xin Wang, Qiuyi Zhang
Long Range Arena : A Benchmark for Efficient Transformers Yi Tay, Mostafa Dehghani, Samira Abnar, Yikang Shen, Dara Bahri, Philip Pham, Jinfeng Rao, Liu Yang, Sebastian Ruder, Donald Metzler
Explainable Deep One-Class Classification Philipp Liznerski, Lukas Ruff, Robert A. Vandermeulen, Billy Joe Franks, Marius Kloft, Klaus Robert Muller
Net-DNF: Effective Deep Modeling of Tabular Data Liran Katzir, Gal Elidan, Ran El-Yaniv
Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization Tatsuya Matsushima, Hiroki Furuta, Yutaka Matsuo, Ofir Nachum, Shixiang Gu
Auxiliary Task Update Decomposition: The Good, the Bad and the Neutral Lucio M. Dery, Yann Dauphin, David Grangier
Average-Case Acceleration for Bilinear Games and Normal Matrices Carles Domingo-Enrich, Fabian Pedregosa, Damien Scieur
OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning Anurag Ajay*, Aviral Kumar, Pulkit Agrawal, Sergey Levine, Ofir Nachum
Training Independent Subnetworks for Robust Prediction Marton Havasi*, Rodolphe Jenatton, Stanislav Fort, Jeremiah Zhe Liu, Jasper Snoek, Balaji Lakshminarayanan, Andrew Mingbo Dai, Dustin Tran
Benchmarks for Deep Off-Policy Evaluation Justin Fu, Mohammad Norouzi, Ofir Nachum, George Tucker, Ziyu Wang, Alexander Novikov, Mengjiao Yang, Michael R Zhang, Yutian Chen, Aviral Kumar, Cosmin Paduraru, Sergey Levine, Thomas Paine
TropEx: An Algorithm for Extracting Linear Terms in Deep Neural Networks Martin Trimmel, Henning Petzka, Cristian Sminchisescu
Mastering Atari with Discrete World Models (see the blog post) Danijar Hafner, Timothy P Lillicrap, Mohammad Norouzi, Jimmy Ba
Exploring the Uncertainty Properties of Neural Networks’ Implicit Priors in the Infinite-Width Limit Danijar Hafner, Timothy P Lillicrap, Mohammad Norouzi, Jimmy Ba
Graph Traversal with Tensor Functionals: A Meta-Algorithm for Scalable Learning Ben Adlam, Jaehoon Lee, Lechao Xiao, Jeffrey Pennington, Jasper Snoek
Anchor & Transform: Learning Sparse Embeddings for Large Vocabularies Paul Pu Liang*, Manzil Zaheer, Yuan Wang, Amr Ahmed
Sharpness-Aware Minimization for Efficiently Improving Generalization Pierre Foret*, Ariel Kleiner, Hossein Mobahi, Behnam Neyshabur
HyperGrid Transformers: Towards A Single Model for Multiple Tasks Yi Tay, Zhe Zhao, Dara Bahri, Donald Metzler, Da-Cheng Juan
Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms Maruan Al-Shedivat*, Jennifer Gillenwater, Eric Xing, Afshin Rostamizadeh
Do Wide and Deep Networks Learn the Same Things? Uncovering How Neural Network Representations Vary with Width and Depth Thao Nguyen, Maithra Raghu, Simon Kornblith
A Unifying View on Implicit Bias in Training Linear Neural Networks Chulhee Yun*, Shankar Krishnan, Hossein Mobahi
Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learning Aviral Kumar, Rishabh Agarwal, Dibya Ghosh, Sergey Levine
Mathematical Reasoning via Self-Supervised Skip-Tree Training Markus Norman Rabe, Dennis Lee, Kshitij Bansal, Christian Szegedy
Lipschitz Recurrent Neural Networks N. Benjamin Erichson, Omri Azencot, Alejandro Queiruga, Liam Hodgkinson, Michael W. Mahoney
Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization Michael R Zhang*, Thomas Paine, Ofir Nachum, Cosmin Paduraru, George Tucker, ziyu wang, Mohammad Norouzi
The Importance of Pessimism in Fixed-Dataset Policy Optimization Jacob Buckman, Carles Gelada, Marc G Bellemare
Monotonic Kronecker-Factored Lattice William Taylor Bakst, Nobuyuki Morioka, Erez Louidor
Adversarially Guided Actor-Critic Yannis Flet-Berliac, Johan Ferret, Olivier Pietquin, Philippe Preux, Matthieu Geist
Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes Mike Gartrell, Insu Han, Elvis Dohmatob, Jennifer Gillenwater, Victor-Emmanuel Brunel
GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding Dmitry Lepikhin, HyoukJoong Lee, Yuanzhong Xu, Dehao Chen, Orhan Firat, Yanping Huang, Maxim Krikun, Noam Shazeer, Zhifeng Chen
Revisiting Hierarchical Approach for Persistent Long-Term Video Prediction Wonkwang Lee, Whie Jung, Han Zhang, Ting Chen, Jing Yu Koh, Thomas Huang, Hyungsuk Yoon, Honglak Lee*, Seunghoon Hong
Gradient Vaccine: Investigating and Improving Multi-task Optimization in Massively Multilingual Models Zirui Wang, Yulia Tsvetkov, Orhan Firat, Yuan Cao
Dataset Meta-Learning from Kernel Ridge-Regression Timothy Nguyen, Zhourong Chen, Jaehoon Lee
Dual-Mode ASR: Unify and Improve Streaming ASR with Full-Context Modeling Jiahui Yu, Wei Han, Anmol Gulati, Chung-Cheng Chiu, Bo Li, Tara N Sainath, Yonghui Wu, Ruoming Pang
Implicit Gradient Regularization David Barrett, Benoit Dherin
Deconstructing the Regularization of BatchNorm Yann Dauphin, Ekin Dogus Cubuk
C-Learning: Learning to Achieve Goals via Recursive Classification Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine
Evolving Reinforcement Learning Algorithms John D Co-Reyes, Yingjie Miao, Daiyi Peng, Esteban Real, Quoc V Le, Sergey Levine, Honglak Lee, Aleksandra Faust
Colorization Transformer Manoj Kumar, Dirk Weissenborn, Nal Kalchbrenner
Control-Aware Representations for Model-based Reinforcement Learning Brandon Cui, Yinlam Chow, Mohammad Ghavamzadeh
Evaluations and Methods for Explanation through Robustness Analysis Cheng-Yu Hsieh, Chih-Kuan Yeh, Xuanqing Liu, Pradeep Kumar Ravikumar, Seungyeon Kim, Sanjiv Kumar, Cho-Jui Hsieh
Learning and Evaluating Representations for Deep One-Class Classification Kihyuk Sohn, Chun-Liang Li, Jinsung Yoon, Minho Jin, Tomas Pfister
No MCMC for Me: Amortized Sampling for Fast and Stable Training of Energy-Based Models Will Sussman Grathwohl, Jacob Jin Kelly, Milad Hashemi, Mohammad Norouzi, Kevin Swersky, David Duvenaud
Neural Thompson Sampling Weitong ZHANG, Dongruo Zhou, Lihong Li, Quanquan Gu
A Design Space Study for LISTA and Beyond Tianjian Meng, Xiaohan Chen, Yifan Jiang, Zhangyang Wang
i-Mix: A Domain-Agnostic Strategy for Contrastive Representation Learning Kibok Lee, Yian Zhu, Kihyuk Sohn, Chun-Liang Li, Jinwoo Shin, Honglak Lee
Factorizing Declarative and Procedural Knowledge in Structured, Dynamical Environments Anirudh Goyal, Alex Lamb, Phanideep Gampa, Philippe Beaudoin, Charles Blundell, Sergey Levine, Yoshua Bengio, Michael Curtis Mozer
Calibration of Neural Networks using Splines Kartik Gupta, Amir Rahimi, Thalaiyasingam Ajanthan, Thomas Mensink, Cristian Sminchisescu, Richard Hartley
Extreme Memorization via Scale of Initialization Harsh Mehta, Ashok Cutkosky, Behnam Neyshabur
Molecule Optimization by Explainable Evolution Binghong Chen, Tianzhe Wang, Chengtao Li, Hanjun Dai, Le Song
Combining Ensembles and Data Augmentation Can Harm Your Calibration Yeming Wen, Ghassen Jerfel, Rafael Muller, Michael W Dusenberry, Jasper Snoek, Balaji Lakshminarayanan, Dustin Tran
Workshops Science and Engineering of Deep Learning Speakers and Panelists include: Alex Hanna Moderator and Advisors include: Emily Denton Organizers include: Negar Rostemzadeh, Samy Bengio*
Synthetic Data Generation: Quality, Privacy, Bias Speakers include: Jinsung Yoon, Emily Denton Program Committee includes: Syed Ashrafulla
Enormous Language Models: Perspectives and Benchmarks Speakers and Panelists include: Noam Shazeer, Natalie Schluter Organizers include: Colin Raffel, Adam Roberts, Jascha Sohl-Dickstein, Katherine Lee, William Fedus, Aitor Lewkowycz
The Role of Mathematical Reasoning in General Artificial Intelligence Speakers and Panelists include: Markus Rabe, Christian Szegedy
Weakly Supervised Learning Invited Speakers include: Lu Jiang
Learning to Learn Organizers include: Yevgen Chebotar
Embodied Multimodal Learning (EML) Invited Speakers includes: Sergey Levine
Distributed and Private Machine Learning Program Committee includes: Peter Kairouz, Ananda Theertha Suresh
S2D-OLAD: From Shallow to Deep, Overcoming Limited and Adverse Data Invited Speakers include: Alex Hanna, Hugo Larochelle Organizers include: Vincent Dumoulin
Responsible AI (RAI) Speakers include: Been Kim
Energy-Based Models: Current Perspectives, Challenges, and Opportunities Organizers include: Adji Bousso Dieng, Igor Mordatch
A Roadmap to Never-Ending RL Invited Session Panelists include: Aleksandra Faust Program Committee includes: Coline Devin, Karol Hausman, Ben Eysenbach, Ofir Nachum, Ryan Julian, Tianhe Yu, Dumitru Erhan, Marc Pickett, Shixiang Gu
2nd Workshop on Practical ML for Developing Countries: Learning Under Limited/low Resource Scenarios Program Committee includes: Pablo Samuel Castro
Beyond Static Papers: Rethinking How We Share Scientific Understanding in ML Speakers include: David Ha, Hugo Larochelle Organizers include: Sara Hooker
* Indicates work done while at Google
