paper_id string | title string | paper_url string | authors list | type string | primary_area string | abstract large_string | keywords list | TL;DR large_string | submission_number int64 | arxiv_id string | arxiv_id_source string |
|---|---|---|---|---|---|---|---|---|---|---|---|
-QxT4mJdijq | Meta-learning Symmetries by Reparameterization | https://openreview.net/forum?id=-QxT4mJdijq | [
"Allan Zhou",
"Tom Knowles",
"Chelsea Finn"
] | Poster | null | Many successful deep learning architectures are equivariant to certain transformations in order to conserve parameters and improve generalization: most famously, convolution layers are equivariant to shifts of the input. This approach only works when practitioners know the symmetries of the task and can manually constr... | [
"meta-learning",
"equivariance",
"convolution",
"symmetry"
] | null | 1,447 | 2007.02933 | title_snapshot |
GH7QRzUDdXG | A Geometric Analysis of Deep Generative Image Models and Its Applications | https://openreview.net/forum?id=GH7QRzUDdXG | [
"Binxu Wang",
"Carlos R Ponce"
] | Poster | null | Generative adversarial networks (GANs) have emerged as a powerful unsupervised method to model the statistical patterns of real-world data sets, such as natural images. These networks are trained to map random inputs in their latent space to new samples representative of the learned data. However, the structure of the ... | [
"Deep generative model",
"Interpretability",
"GAN",
"Differential Geometry",
"Optimization",
"Model Inversion",
"Feature Visualization"
] | null | 1,446 | null | null |
tC6iW2UUbJf | What Makes Instance Discrimination Good for Transfer Learning? | https://openreview.net/forum?id=tC6iW2UUbJf | [
"Nanxuan Zhao",
"Zhirong Wu",
"Rynson W. H. Lau",
"Stephen Lin"
] | Poster | null | Contrastive visual pretraining based on the instance discrimination pretext task has made significant progress. Notably, recent work on unsupervised pretraining has shown to surpass the supervised counterpart for finetuning downstream applications such as object detection and segmentation. It comes as a surprise that... | [
"Transfer Learning",
"Unsupervised Learning",
"Self-supervised Learning"
] | null | 1,443 | 2006.06606 | title_snapshot |
EMHoBG0avc1 | Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval | https://openreview.net/forum?id=EMHoBG0avc1 | [
"Wenhan Xiong",
"Xiang Li",
"Srini Iyer",
"Jingfei Du",
"Patrick Lewis",
"William Yang Wang",
"Yashar Mehdad",
"Scott Yih",
"Sebastian Riedel",
"Douwe Kiela",
"Barlas Oguz"
] | Poster | null | We propose a simple and efficient multi-hop dense retrieval approach for answering complex open-domain questions, which achieves state-of-the-art performance on two multi-hop datasets, HotpotQA and multi-evidence FEVER. Contrary to previous work, our method does not require access to any corpus-specific information, su... | [
"multi-hop question answering",
"recursive dense retrieval",
"open domain complex question answering"
] | null | 1,441 | 2009.12756 | title_snapshot |
xgGS6PmzNq6 | On Dyadic Fairness: Exploring and Mitigating Bias in Graph Connections | https://openreview.net/forum?id=xgGS6PmzNq6 | [
"Peizhao Li",
"Yifei Wang",
"Han Zhao",
"Pengyu Hong",
"Hongfu Liu"
] | Poster | null | Disparate impact has raised serious concerns in machine learning applications and its societal impacts. In response to the need of mitigating discrimination, fairness has been regarded as a crucial property in algorithmic design. In this work, we study the problem of disparate impact on graph-structured data. Specifica... | [
"algorithmic fairness",
"graph-structured data"
] | null | 1,434 | null | null |
CF-ZIuSMXRz | Spatio-Temporal Graph Scattering Transform | https://openreview.net/forum?id=CF-ZIuSMXRz | [
"Chao Pan",
"Siheng Chen",
"Antonio Ortega"
] | Poster | null | Although spatio-temporal graph neural networks have achieved great empirical success in handling multiple correlated time series, they may be impractical in some real-world scenarios due to a lack of sufficient high-quality training data. Furthermore, spatio-temporal graph neural networks lack theoretical interpretatio... | [
"scattering transform",
"spatio-temporal graph",
"graph neural networks",
"skeleton-based action recognition"
] | null | 1,432 | 2012.03363 | title_snapshot |
GY6-6sTvGaf | Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels | https://openreview.net/forum?id=GY6-6sTvGaf | [
"Denis Yarats",
"Ilya Kostrikov",
"Rob Fergus"
] | Spotlight | null | We propose a simple data augmentation technique that can be applied to standard model-free reinforcement learning algorithms, enabling robust learning directly from pixels without the need for auxiliary losses or pre-training. The approach leverages input perturbations commonly used in computer vision tasks to transfo... | [] | null | 1,429 | 2004.13649 | title_snapshot |
XjYgR6gbCEc | MODALS: Modality-agnostic Automated Data Augmentation in the Latent Space | https://openreview.net/forum?id=XjYgR6gbCEc | [
"Tsz-Him Cheung",
"Dit-Yan Yeung"
] | Poster | null | Data augmentation is an efficient way to expand a training dataset by creating additional artificial data. While data augmentation is found to be effective in improving the generalization capabilities of models for various machine learning tasks, the underlying augmentation methods are usually manually designed and car... | [
"deep learning",
"data augmentation",
"automated data augmentation",
"latent space"
] | null | 1,427 | null | null |
0IOX0YcCdTn | ALFWorld: Aligning Text and Embodied Environments for Interactive Learning | https://openreview.net/forum?id=0IOX0YcCdTn | [
"Mohit Shridhar",
"Xingdi Yuan",
"Marc-Alexandre Cote",
"Yonatan Bisk",
"Adam Trischler",
"Matthew Hausknecht"
] | Poster | null | Given a simple request like Put a washed apple in the kitchen fridge, humans can reason in purely abstract terms by imagining action sequences and scoring their likelihood of success, prototypicality, and efficiency, all without moving a muscle. Once we see the kitchen in question, we can update our abstract plans to f... | [
"Textworld",
"Text-based Games",
"Embodied Agents",
"Language Grounding",
"Generalization",
"Imitation Learning",
"ALFRED"
] | null | 1,426 | 2010.03768 | title_snapshot |
jXe91kq3jAq | Latent Skill Planning for Exploration and Transfer | https://openreview.net/forum?id=jXe91kq3jAq | [
"Kevin Xie",
"Homanga Bharadhwaj",
"Danijar Hafner",
"Animesh Garg",
"Florian Shkurti"
] | Poster | null | To quickly solve new tasks in complex environments, intelligent agents need to build up reusable knowledge. For example, a learned world model captures knowledge about the environment that applies to new tasks. Similarly, skills capture general behaviors that can apply to new tasks. In this paper, we investigate how th... | [
"Model-Based Reinforcement Learning",
"World Models",
"Skill Discovery",
"Mutual Information",
"Planning",
"Model Predictive Control",
"Partial Amortization"
] | null | 1,425 | 2011.13897 | title_snapshot |
djwS0m4Ft_A | Evaluating the Disentanglement of Deep Generative Models through Manifold Topology | https://openreview.net/forum?id=djwS0m4Ft_A | [
"Sharon Zhou",
"Eric Zelikman",
"Fred Lu",
"Andrew Y. Ng",
"Gunnar E. Carlsson",
"Stefano Ermon"
] | Poster | null | Learning disentangled representations is regarded as a fundamental task for improving the generalization, robustness, and interpretability of generative models. However, measuring disentanglement has been challenging and inconsistent, often dependent on an ad-hoc external model or specific to a certain dataset. To addr... | [
"generative models",
"evaluation",
"disentanglement"
] | null | 1,422 | 2006.03680 | title_snapshot |
g11CZSghXyY | Combining Ensembles and Data Augmentation Can Harm Your Calibration | https://openreview.net/forum?id=g11CZSghXyY | [
"Yeming Wen",
"Ghassen Jerfel",
"Rafael Muller",
"Michael W Dusenberry",
"Jasper Snoek",
"Balaji Lakshminarayanan",
"Dustin Tran"
] | Poster | null | Ensemble methods which average over multiple neural network predictions are a simple approach to improve a model’s calibration and robustness. Similarly, data augmentation techniques, which encode prior information in the form of invariant feature transformations, are effective for improving calibration and robustness.... | [
"Ensembles",
"Uncertainty estimates",
"Calibration"
] | null | 1,420 | 2010.09875 | title_snapshot |
Vfs_2RnOD0H | Dynamic Tensor Rematerialization | https://openreview.net/forum?id=Vfs_2RnOD0H | [
"Marisa Kirisame",
"Steven Lyubomirsky",
"Altan Haan",
"Jennifer Brennan",
"Mike He",
"Jared Roesch",
"Tianqi Chen",
"Zachary Tatlock"
] | Spotlight | null | Checkpointing enables the training of deep learning models under restricted memory budgets by freeing intermediate activations from memory and recomputing them on demand. Current checkpointing techniques statically plan these recomputations offline and assume static computation graphs. We demonstrate that a simple onli... | [
"Rematerialization",
"Memory-saving",
"Runtime Systems",
"Checkpointing"
] | null | 1,417 | 2006.09616 | title_snapshot |
eom0IUrF__F | CoCo: Controllable Counterfactuals for Evaluating Dialogue State Trackers | https://openreview.net/forum?id=eom0IUrF__F | [
"SHIYANG LI",
"Semih Yavuz",
"Kazuma Hashimoto",
"Jia Li",
"Tong Niu",
"Nazneen Rajani",
"Xifeng Yan",
"Yingbo Zhou",
"Caiming Xiong"
] | Poster | null | Dialogue state trackers have made significant progress on benchmark datasets, but their generalization capability to novel and realistic scenarios beyond the held- out conversations is less understood. We propose controllable counterfactuals (COCO) to bridge this gap and evaluate dialogue state tracking (DST) models on... | [
"task-oriented dialogue",
"dialogue state tracking",
"robustness",
"dst",
"evaluation"
] | null | 1,416 | 2010.12850 | title_snapshot |
VqzVhqxkjH1 | Deep Neural Network Fingerprinting by Conferrable Adversarial Examples | https://openreview.net/forum?id=VqzVhqxkjH1 | [
"Nils Lukas",
"Yuxuan Zhang",
"Florian Kerschbaum"
] | Spotlight | null | In Machine Learning as a Service, a provider trains a deep neural network and gives many users access. The hosted (source) model is susceptible to model stealing attacks, where an adversary derives a surrogate model from API access to the source model. For post hoc detection of such attacks, the provider needs a robust... | [
"Fingerprinting",
"Adversarial Examples",
"Transferability",
"Conferrability"
] | null | 1,415 | 1912.00888 | title_snapshot |
QkRbdiiEjM | AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models | https://openreview.net/forum?id=QkRbdiiEjM | [
"Ke Sun",
"Zhanxing Zhu",
"Zhouchen Lin"
] | Poster | null | The design of deep graph models still remains to be investigated and the crucial part is how to explore and exploit the knowledge from different hops of neighbors in an efficient way. In this paper, we propose a novel RNN-like deep graph neural network architecture by incorporating AdaBoost into the computation of netw... | [
"Graph Neural Networks",
"AdaBoost"
] | null | 1,400 | 1908.05081 | title_snapshot |
kBVJ2NtiY- | Learning What To Do by Simulating the Past | https://openreview.net/forum?id=kBVJ2NtiY- | [
"David Lindner",
"Rohin Shah",
"Pieter Abbeel",
"Anca Dragan"
] | Poster | null | Since reward functions are hard to specify, recent work has focused on learning policies from human feedback. However, such approaches are impeded by the expense of acquiring such feedback. Recent work proposed that agents have access to a source of information that is effectively free: in any environment that humans h... | [
"imitation learning",
"reward learning",
"reinforcement learning"
] | null | 1,398 | 2104.03946 | title_snapshot |
PrzjugOsDeE | CcGAN: Continuous Conditional Generative Adversarial Networks for Image Generation | https://openreview.net/forum?id=PrzjugOsDeE | [
"Xin Ding",
"Yongwei Wang",
"Zuheng Xu",
"William J Welch",
"Z. Jane Wang"
] | Poster | null | This work proposes the continuous conditional generative adversarial network (CcGAN), the first generative model for image generation conditional on continuous, scalar conditions (termed regression labels). Existing conditional GANs (cGANs) are mainly designed for categorical conditions (e.g., class labels); conditioni... | [
"Conditional generative adversarial networks",
"image generation",
"continuous and scalar conditions"
] | null | 1,395 | null | null |
7I12hXRi8F | ANOCE: Analysis of Causal Effects with Multiple Mediators via Constrained Structural Learning | https://openreview.net/forum?id=7I12hXRi8F | [
"Hengrui Cai",
"Rui Song",
"Wenbin Lu"
] | Poster | null | In the era of causal revolution, identifying the causal effect of an exposure on the outcome of interest is an important problem in many areas, such as epidemics, medicine, genetics, and economics. Under a general causal graph, the exposure may have a direct effect on the outcome and also an indirect effect regulated b... | [
"Causal network",
"Constrained optimization",
"COVID-19",
"Individual mediation effects",
"Structure learning"
] | null | 1,394 | null | null |
3X64RLgzY6O | Direction Matters: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate | https://openreview.net/forum?id=3X64RLgzY6O | [
"Jingfeng Wu",
"Difan Zou",
"Vladimir Braverman",
"Quanquan Gu"
] | Poster | null | Understanding the algorithmic bias of stochastic gradient descent (SGD) is one of the key challenges in modern machine learning and deep learning theory. Most of the existing works, however, focus on very small or even infinitesimal learning rate regime, and fail to cover practical scenarios where the learning rate is ... | [
"SGD",
"regularization",
"implicit bias"
] | null | 1,393 | 2011.02538 | title_snapshot |
Eql5b1_hTE4 | Robust early-learning: Hindering the memorization of noisy labels | https://openreview.net/forum?id=Eql5b1_hTE4 | [
"Xiaobo Xia",
"Tongliang Liu",
"Bo Han",
"Chen Gong",
"Nannan Wang",
"Zongyuan Ge",
"Yi Chang"
] | Poster | null | The \textit{memorization effects} of deep networks show that they will first memorize training data with clean labels and then those with noisy labels. The \textit{early stopping} method therefore can be exploited for learning with noisy labels. However, the side effect brought by noisy labels will influence the memori... | [] | null | 1,387 | null | null |
cPZOyoDloxl | SMiRL: Surprise Minimizing Reinforcement Learning in Unstable Environments | https://openreview.net/forum?id=cPZOyoDloxl | [
"Glen Berseth",
"Daniel Geng",
"Coline Manon Devin",
"Nicholas Rhinehart",
"Chelsea Finn",
"Dinesh Jayaraman",
"Sergey Levine"
] | Oral | null | Every living organism struggles against disruptive environmental forces to carve out and maintain an orderly niche. We propose that such a struggle to achieve and preserve order might offer a principle for the emergence of useful behaviors in artificial agents. We formalize this idea into an unsupervised reinforcement ... | [
"Reinforcement learning"
] | null | 1,385 | 1912.05510 | title_snapshot |
GMgHyUPrXa | A Design Space Study for LISTA and Beyond | https://openreview.net/forum?id=GMgHyUPrXa | [
"Tianjian Meng",
"Xiaohan Chen",
"Yifan Jiang",
"Zhangyang Wang"
] | Poster | null | In recent years, great success has been witnessed in building problem-specific deep networks from unrolling iterative algorithms, for solving inverse problems and beyond. Unrolling is believed to incorporate the model-based prior with the learning capacity of deep learning. This paper revisits \textit{the role of unrol... | [] | null | 1,384 | 2104.04110 | title_snapshot |
Qk-Wq5AIjpq | PAC Confidence Predictions for Deep Neural Network Classifiers | https://openreview.net/forum?id=Qk-Wq5AIjpq | [
"Sangdon Park",
"Shuo Li",
"Insup Lee",
"Osbert Bastani"
] | Poster | null | A key challenge for deploying deep neural networks (DNNs) in safety critical settings is the need to provide rigorous ways to quantify their uncertainty. In this paper, we propose a novel algorithm for constructing predicted classification confidences for DNNs that comes with provable correctness guarantees. Our approa... | [
"classification",
"calibration",
"probably approximated correct guarantee",
"fast DNN inference",
"safe planning"
] | null | 1,383 | 2011.00716 | title_snapshot |
LiX3ECzDPHZ | X2T: Training an X-to-Text Typing Interface with Online Learning from User Feedback | https://openreview.net/forum?id=LiX3ECzDPHZ | [
"Jensen Gao",
"Siddharth Reddy",
"Glen Berseth",
"Nicholas Hardy",
"Nikhilesh Natraj",
"Karunesh Ganguly",
"Anca Dragan",
"Sergey Levine"
] | Poster | null | We aim to help users communicate their intent to machines using flexible, adaptive interfaces that translate arbitrary user input into desired actions. In this work, we focus on assistive typing applications in which a user cannot operate a keyboard, but can instead supply other inputs, such as webcam images that captu... | [
"reinforcement learning",
"human-computer interaction"
] | null | 1,373 | 2203.02072 | title_snapshot |
WEHSlH5mOk | Discrete Graph Structure Learning for Forecasting Multiple Time Series | https://openreview.net/forum?id=WEHSlH5mOk | [
"Chao Shang",
"Jie Chen",
"Jinbo Bi"
] | Poster | null | Time series forecasting is an extensively studied subject in statistics, economics, and computer science. Exploration of the correlation and causation among the variables in a multivariate time series shows promise in enhancing the performance of a time series model. When using deep neural networks as forecasting model... | [
"Time series forecasting",
"graph neural network",
"graph structure learning"
] | null | 1,372 | 2101.06861 | title_snapshot |
CGQ6ENUMX6 | Task-Agnostic Morphology Evolution | https://openreview.net/forum?id=CGQ6ENUMX6 | [
"Donald Joseph Hejna III",
"Pieter Abbeel",
"Lerrel Pinto"
] | Poster | null | Deep reinforcement learning primarily focuses on learning behavior, usually overlooking the fact that an agent's function is largely determined by form. So, how should one go about finding a morphology fit for solving tasks in a given environment? Current approaches that co-adapt morphology and behavior use a specific ... | [
"morphology",
"unsupervised",
"evolution",
"information theory",
"empowerment"
] | null | 1,368 | 2102.13100 | title_snapshot |
aDjoksTpXOP | Deep Equals Shallow for ReLU Networks in Kernel Regimes | https://openreview.net/forum?id=aDjoksTpXOP | [
"Alberto Bietti",
"Francis Bach"
] | Poster | null | Deep networks are often considered to be more expressive than shallow ones in terms of approximation. Indeed, certain functions can be approximated by deep networks provably more efficiently than by shallow ones, however, no tractable algorithms are known for learning such deep models. Separately, a recent line of work... | [
"deep learning",
"kernels",
"approximation",
"neural tangent kernels"
] | null | 1,365 | 2009.14397 | title_snapshot |
5jzlpHvvRk | Loss Function Discovery for Object Detection via Convergence-Simulation Driven Search | https://openreview.net/forum?id=5jzlpHvvRk | [
"Peidong Liu",
"Gengwei Zhang",
"Bochao Wang",
"Hang Xu",
"Xiaodan Liang",
"Yong Jiang",
"Zhenguo Li"
] | Poster | null | Designing proper loss functions for vision tasks has been a long-standing research direction to advance the capability of existing models. For object detection, the well-established classification and regression loss functions have been carefully designed by considering diverse learning challenges (e.g. class imbalance... | [
"Object detection",
"AutoML",
"Evolutionary algorithm",
"Loss function search"
] | null | 1,364 | 2102.04700 | title_snapshot |
jxdXSW9Doc | Effective Distributed Learning with Random Features: Improved Bounds and Algorithms | https://openreview.net/forum?id=jxdXSW9Doc | [
"Yong Liu",
"Jiankun Liu",
"Shuqiang Wang"
] | Poster | null | In this paper, we study the statistical properties of distributed kernel ridge regression together with random features (DKRR-RF), and obtain optimal generalization bounds under the basic setting, which can substantially relax the restriction on the number of local machines in the existing state-of-art bounds. Specific... | [
"Risk bound",
"statistical learning theory",
"kernel methods"
] | null | 1,362 | null | null |
AhElGnhU2BV | On InstaHide, Phase Retrieval, and Sparse Matrix Factorization | https://openreview.net/forum?id=AhElGnhU2BV | [
"Sitan Chen",
"Xiaoxiao Li",
"Zhao Song",
"Danyang Zhuo"
] | Poster | null | In this work, we examine the security of InstaHide, a scheme recently proposed by \cite{hsla20} for preserving the security of private datasets in the context of distributed learning. To generate a synthetic training example to be shared among the distributed learners, InstaHide takes a convex combination of private fe... | [
"Distributed learning",
"InstaHide",
"phase retrieval",
"matrix factorization"
] | null | 1,359 | 2011.11181 | title_snapshot |
Vd7lCMvtLqg | Anchor & Transform: Learning Sparse Embeddings for Large Vocabularies | https://openreview.net/forum?id=Vd7lCMvtLqg | [
"Paul Pu Liang",
"Manzil Zaheer",
"Yuan Wang",
"Amr Ahmed"
] | Poster | null | Learning continuous representations of discrete objects such as text, users, movies, and URLs lies at the heart of many applications including language and user modeling. When using discrete objects as input to neural networks, we often ignore the underlying structures (e.g., natural groupings and similarities) and emb... | [
"sparse embeddings",
"large vocabularies",
"text classification",
"language modeling",
"recommendation systems"
] | null | 1,353 | 2003.08197 | title_snapshot |
jDdzh5ul-d | Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning | https://openreview.net/forum?id=jDdzh5ul-d | [
"Haibo Yang",
"Minghong Fang",
"Jia Liu"
] | Poster | null | Federated learning (FL) is a distributed machine learning architecture that leverages a large number of workers to jointly learn a model with decentralized data. FL has received increasing attention in recent years thanks to its data privacy protection, communication efficiency and a linear speedup for convergence in ... | [
"Federated Learning",
"Linear Speedup",
"Partial Worker Participation"
] | null | 1,350 | 2101.11203 | title_snapshot |
vK9WrZ0QYQ | Deep Neural Tangent Kernel and Laplace Kernel Have the Same RKHS | https://openreview.net/forum?id=vK9WrZ0QYQ | [
"Lin Chen",
"Sheng Xu"
] | Poster | null | We prove that the reproducing kernel Hilbert spaces (RKHS) of a deep neural tangent kernel and the Laplace kernel include the same set of functions, when both kernels are restricted to the sphere $\mathbb{S}^{d-1}$. Additionally, we prove that the exponential power kernel with a smaller power (making the kernel less sm... | [
"Neural tangent kernel",
"Reproducing kernel Hilbert space",
"Laplace kernel",
"Singularity analysis"
] | null | 1,340 | 2009.10683 | title_snapshot |
w2mYg3d0eot | Fast convergence of stochastic subgradient method under interpolation | https://openreview.net/forum?id=w2mYg3d0eot | [
"Huang Fang",
"Zhenan Fan",
"Michael Friedlander"
] | Poster | null | This paper studies the behaviour of the stochastic subgradient descent (SSGD) method applied to over-parameterized nonsmooth optimization problems that satisfy an interpolation condition. By leveraging the composite structure of the empirical risk minimization problems, we prove that SSGD converges, respectively, with ... | [
"Optimization",
"stochastic subgradient method",
"interpolation",
"convergence analysis"
] | null | 1,337 | null | null |
PH5PH9ZO_4 | Generating Adversarial Computer Programs using Optimized Obfuscations | https://openreview.net/forum?id=PH5PH9ZO_4 | [
"Shashank Srikant",
"Sijia Liu",
"Tamara Mitrovska",
"Shiyu Chang",
"Quanfu Fan",
"Gaoyuan Zhang",
"Una-May O'Reilly"
] | Poster | null | Machine learning (ML) models that learn and predict properties of computer programs are increasingly being adopted and deployed.
These models have demonstrated success in applications such as auto-completing code, summarizing large programs, and detecting bugs and malware in programs.
In this work, we investigate pri... | [
"Machine Learning (ML) for Programming Languages (PL)/Software Engineering (SE)",
"Adversarial computer programs",
"Program obfuscation",
"Combinatorial optimization",
"Differentiable program generator",
"Models for code"
] | null | 1,336 | 2103.11882 | title_snapshot |
tc5qisoB-C | C-Learning: Learning to Achieve Goals via Recursive Classification | https://openreview.net/forum?id=tc5qisoB-C | [
"Benjamin Eysenbach",
"Ruslan Salakhutdinov",
"Sergey Levine"
] | Poster | null | We study the problem of predicting and controlling the future state distribution of an autonomous agent. This problem, which can be viewed as a reframing of goal-conditioned reinforcement learning (RL), is centered around learning a conditional probability density function over future states. Instead of directly estima... | [
"reinforcement learning",
"goal reaching",
"density estimation",
"Q-learning",
"hindsight relabeling"
] | null | 1,334 | 2011.08909 | title_snapshot |
eqBwg3AcIAK | Off-Dynamics Reinforcement Learning: Training for Transfer with Domain Classifiers | https://openreview.net/forum?id=eqBwg3AcIAK | [
"Benjamin Eysenbach",
"Shreyas Chaudhari",
"Swapnil Asawa",
"Sergey Levine",
"Ruslan Salakhutdinov"
] | Poster | null | We propose a simple, practical, and intuitive approach for domain adaptation in reinforcement learning. Our approach stems from the idea that the agent's experience in the source domain should look similar to its experience in the target domain. Building off of a probabilistic view of RL, we achieve this goal by compen... | [
"reinforcement learning",
"transfer learning",
"domain adaptation"
] | null | 1,333 | 2006.13916 | title_snapshot |
rALA0Xo6yNJ | Learning to Reach Goals via Iterated Supervised Learning | https://openreview.net/forum?id=rALA0Xo6yNJ | [
"Dibya Ghosh",
"Abhishek Gupta",
"Ashwin Reddy",
"Justin Fu",
"Coline Manon Devin",
"Benjamin Eysenbach",
"Sergey Levine"
] | Oral | null | Current reinforcement learning (RL) algorithms can be brittle and difficult to use, especially when learning goal-reaching behaviors from sparse rewards. Although supervised imitation learning provides a simple and stable alternative, it requires access to demonstrations from a human supervisor. In this paper, we study... | [
"goal reaching",
"reinforcement learning",
"behavior cloning",
"goal-conditioned RL"
] | null | 1,331 | 1912.06088 | title_snapshot |
UcoXdfrORC | Model-Based Visual Planning with Self-Supervised Functional Distances | https://openreview.net/forum?id=UcoXdfrORC | [
"Stephen Tian",
"Suraj Nair",
"Frederik Ebert",
"Sudeep Dasari",
"Benjamin Eysenbach",
"Chelsea Finn",
"Sergey Levine"
] | Spotlight | null | A generalist robot must be able to complete a variety of tasks in its environment. One appealing way to specify each task is in terms of a goal observation. However, learning goal-reaching policies with reinforcement learning remains a challenging problem, particularly when hand-engineered reward functions are not avai... | [
"planning",
"model learning",
"distance learning",
"reinforcement learning",
"robotics"
] | null | 1,330 | 2012.15373 | title_snapshot |
YmqAnY0CMEy | Mathematical Reasoning via Self-supervised Skip-tree Training | https://openreview.net/forum?id=YmqAnY0CMEy | [
"Markus Norman Rabe",
"Dennis Lee",
"Kshitij Bansal",
"Christian Szegedy"
] | Spotlight | null | We demonstrate that self-supervised language modeling applied to mathematical formulas enables logical reasoning. To measure the logical reasoning abilities of language models, we formulate several evaluation (downstream) tasks, such as inferring types, suggesting missing assumptions and completing equalities. For trai... | [
"self-supervised learning",
"mathematics",
"reasoning",
"theorem proving",
"language modeling"
] | null | 1,327 | 2006.04757 | title_snapshot |
eMP1j9efXtX | DeepAveragers: Offline Reinforcement Learning By Solving Derived Non-Parametric MDPs | https://openreview.net/forum?id=eMP1j9efXtX | [
"Aayam Kumar Shrestha",
"Stefan Lee",
"Prasad Tadepalli",
"Alan Fern"
] | Spotlight | null | We study an approach to offline reinforcement learning (RL) based on optimally solving finitely-represented MDPs derived from a static dataset of experience. This approach can be applied on top of any learned representation and has the potential to easily support multiple solution objectives as well as zero-sh... | [
"Offline Reinforcement Learning",
"Planning"
] | null | 1,325 | 2010.08891 | title_snapshot |
KtH8W3S_RE | Multi-resolution modeling of a discrete stochastic process identifies causes of cancer | https://openreview.net/forum?id=KtH8W3S_RE | [
"Adam Uri Yaari",
"Maxwell Sherman",
"Oliver Clarke Priebe",
"Po-Ru Loh",
"Boris Katz",
"Andrei Barbu",
"Bonnie Berger"
] | Poster | null | Detection of cancer-causing mutations within the vast and mostly unexplored human genome is a major challenge. Doing so requires modeling the background mutation rate, a highly non-stationary stochastic process, across regions of interest varying in size from one to millions of positions. Here, we present the split-Poi... | [
"Computational Biology",
"non-stationary stochastic processes",
"cancer research",
"deep learning",
"probabelistic models",
"graphical models"
] | null | 1,324 | null | null |
p-NZIuwqhI4 | On the Theory of Implicit Deep Learning: Global Convergence with Implicit Layers | https://openreview.net/forum?id=p-NZIuwqhI4 | [
"Kenji Kawaguchi"
] | Spotlight | null | A deep equilibrium model uses implicit layers, which are implicitly defined through an equilibrium point of an infinite sequence of computation. It avoids any explicit computation of the infinite sequence by finding an equilibrium point directly via root-finding and by computing gradients via implicit differentiation. ... | [
"Implicit Deep Learning",
"Deep Equilibrium Models",
"Gradient Descent",
"Learning Theory",
"Non-Convex Optimization"
] | null | 1,317 | 2102.07346 | title_snapshot |
8Ln-Bq0mZcy | On the Critical Role of Conventions in Adaptive Human-AI Collaboration | https://openreview.net/forum?id=8Ln-Bq0mZcy | [
"Andy Shih",
"Arjun Sawhney",
"Jovana Kondic",
"Stefano Ermon",
"Dorsa Sadigh"
] | Poster | null | Humans can quickly adapt to new partners in collaborative tasks (e.g. playing basketball), because they understand which fundamental skills of the task (e.g. how to dribble, how to shoot) carry over across new partners. Humans can also quickly adapt to similar tasks with the same partners by carrying over conventions t... | [
"Multi-agent games",
"emergent behavior",
"transfer learning",
"human-AI collaboration"
] | null | 1,315 | 2104.02871 | title_snapshot |
NsMLjcFaO8O | WaveGrad: Estimating Gradients for Waveform Generation | https://openreview.net/forum?id=NsMLjcFaO8O | [
"Nanxin Chen",
"Yu Zhang",
"Heiga Zen",
"Ron J Weiss",
"Mohammad Norouzi",
"William Chan"
] | Poster | null | This paper introduces WaveGrad, a conditional model for waveform generation which estimates gradients of the data density. The model is built on prior work on score matching and diffusion probabilistic models. It starts from a Gaussian white noise signal and iteratively refines the signal via a gradient-based sampler c... | [
"vocoder",
"diffusion",
"score matching",
"text-to-speech",
"gradient estimation",
"waveform generation"
] | null | 1,311 | 2009.00713 | title_snapshot |
yHeg4PbFHh | BUSTLE: Bottom-Up Program Synthesis Through Learning-Guided Exploration | https://openreview.net/forum?id=yHeg4PbFHh | [
"Augustus Odena",
"Kensen Shi",
"David Bieber",
"Rishabh Singh",
"Charles Sutton",
"Hanjun Dai"
] | Spotlight | null | Program synthesis is challenging largely because of the difficulty of search in a large space of programs. Human programmers routinely tackle the task of writing complex programs by writing sub-programs and then analyzing their intermediate results to compose them in appropriate ways. Motivated by this intuition, we pr... | [
"Program Synthesis"
] | null | 1,310 | 2007.14381 | title_snapshot |
FOyuZ26emy | A Critique of Self-Expressive Deep Subspace Clustering | https://openreview.net/forum?id=FOyuZ26emy | [
"Benjamin David Haeffele",
"Chong You",
"Rene Vidal"
] | Poster | null | Subspace clustering is an unsupervised clustering technique designed to cluster data that is supported on a union of linear subspaces, with each subspace defining a cluster with dimension lower than the ambient space. Many existing formulations for this problem are based on exploiting the self-expressive property of li... | [
"Subspace clustering",
"Manifold clustering",
"Theory of deep learning",
"Autoencoders"
] | null | 1,308 | 2010.03697 | title_snapshot |
unI5ucw_Jk | Explaining by Imitating: Understanding Decisions by Interpretable Policy Learning | https://openreview.net/forum?id=unI5ucw_Jk | [
"Alihan Hüyük",
"Daniel Jarrett",
"Cem Tekin",
"Mihaela van der Schaar"
] | Poster | null | Understanding human behavior from observed data is critical for transparency and accountability in decision-making. Consider real-world settings such as healthcare, in which modeling a decision-maker’s policy is challenging—with no access to underlying states, no knowledge of environment dynamics, and no allowance for ... | [
"interpretable policy learning",
"understanding decision-making"
] | null | 1,306 | 2310.19831 | title_snapshot |
Srmggo3b3X6 | For self-supervised learning, Rationality implies generalization, provably | https://openreview.net/forum?id=Srmggo3b3X6 | [
"Yamini Bansal",
"Gal Kaplun",
"Boaz Barak"
] | Poster | null | We prove a new upper bound on the generalization gap of classifiers that are obtained by first using self-supervision to learn a representation $r$ of the training~data, and then fitting a simple (e.g., linear) classifier $g$ to the labels. Specifically, we show that (under the assumptions described below) the generali... | [
"Deep Learning Theory",
"Generalization Bounds",
"Self-supervised learning",
"Representation learning"
] | null | 1,303 | 2010.08508 | title_snapshot |
JbuYF437WB6 | Directed Acyclic Graph Neural Networks | https://openreview.net/forum?id=JbuYF437WB6 | [
"Veronika Thost",
"Jie Chen"
] | Poster | null | Graph-structured data ubiquitously appears in science and engineering. Graph neural networks (GNNs) are designed to exploit the relational inductive bias exhibited in graphs; they have been shown to outperform other forms of neural networks in scenarios where structure information supplements node features. The most co... | [
"Graph Neural Networks",
"Graph Representation Learning",
"Directed Acyclic Graphs",
"DAG",
"Inductive Bias"
] | null | 1,297 | 2101.07965 | title_snapshot |
qYda4oLEc1 | The Traveling Observer Model: Multi-task Learning Through Spatial Variable Embeddings | https://openreview.net/forum?id=qYda4oLEc1 | [
"Elliot Meyerson",
"Risto Miikkulainen"
] | Spotlight | null | This paper frames a general prediction system as an observer traveling around a continuous space, measuring values at some locations, and predicting them at others. The observer is completely agnostic about any particular task being solved; it cares only about measurement locations and their values. This perspective le... | [
"Multi-task",
"Many-task",
"Multi-domain",
"Cross-domain",
"Variable Embeddings",
"Task Embeddings",
"Tabular",
"Analogies"
] | null | 1,296 | 2010.02354 | title_snapshot |
N3zUDGN5lO | My Body is a Cage: the Role of Morphology in Graph-Based Incompatible Control | https://openreview.net/forum?id=N3zUDGN5lO | [
"Vitaly Kurin",
"Maximilian Igl",
"Tim Rocktäschel",
"Wendelin Boehmer",
"Shimon Whiteson"
] | Poster | null | Multitask Reinforcement Learning is a promising way to obtain models with better performance, generalisation, data efficiency, and robustness. Most existing work is limited to compatible settings, where the state and action space dimensions are the same across tasks. Graph Neural Networks (GNN) are one way to address i... | [
"Deep Reinforcement Learning",
"Multitask Reinforcement Learning",
"Graph Neural Networks",
"Continuous Control",
"Incompatible Environments"
] | null | 1,292 | 2010.01856 | title_snapshot |
3SV-ZePhnZM | Incremental few-shot learning via vector quantization in deep embedded space | https://openreview.net/forum?id=3SV-ZePhnZM | [
"Kuilin Chen",
"Chi-Guhn Lee"
] | Poster | null | The capability of incrementally learning new tasks without forgetting old ones is a challenging problem due to catastrophic forgetting. This challenge becomes greater when novel tasks contain very few labelled training samples. Currently, most methods are dedicated to class-incremental learning and rely on sufficient t... | [
"incremental learning",
"few-shot",
"vector quantization"
] | null | 1,287 | null | null |
cO1IH43yUF | Revisiting Few-sample BERT Fine-tuning | https://openreview.net/forum?id=cO1IH43yUF | [
"Tianyi Zhang",
"Felix Wu",
"Arzoo Katiyar",
"Kilian Q Weinberger",
"Yoav Artzi"
] | Poster | null | This paper is a study of fine-tuning of BERT contextual representations, with focus on commonly observed instabilities in few-sample scenarios. We identify several factors that cause this instability: the common use of a non-standard optimization method with biased gradient estimation; the limited applicability of sign... | [
"Fine-tuning",
"Optimization",
"BERT"
] | null | 1,284 | 2006.05987 | title_snapshot |
84gjULz1t5 | Linear Convergent Decentralized Optimization with Compression | https://openreview.net/forum?id=84gjULz1t5 | [
"Xiaorui Liu",
"Yao Li",
"Rongrong Wang",
"Jiliang Tang",
"Ming Yan"
] | Poster | null | Communication compression has become a key strategy to speed up distributed optimization. However, existing decentralized algorithms with compression mainly focus on compressing DGD-type algorithms. They are unsatisfactory in terms of convergence rate, stability, and the capability to handle heterogeneous data. Motivat... | [
"Decentralized Optimization",
"Communication Compression",
"Linear Convergence",
"Heterogeneous data"
] | null | 1,280 | 2007.00232 | title_snapshot |
Ldau9eHU-qO | Learning from Demonstration with Weakly Supervised Disentanglement | https://openreview.net/forum?id=Ldau9eHU-qO | [
"Yordan Hristov",
"Subramanian Ramamoorthy"
] | Poster | null | Robotic manipulation tasks, such as wiping with a soft sponge, require control from multiple rich sensory modalities. Human-robot interaction, aimed at teach- ing robots, is difficult in this setting as there is potential for mismatch between human and machine comprehension of the rich data streams. We treat the task o... | [
"representation learning for robotics",
"physical symbol grounding",
"semi-supervised learning"
] | null | 1,279 | 2006.09107 | title_snapshot |
wta_8Hx2KD | Incorporating Symmetry into Deep Dynamics Models for Improved Generalization | https://openreview.net/forum?id=wta_8Hx2KD | [
"Rui Wang",
"Robin Walters",
"Rose Yu"
] | Poster | null | Recent work has shown deep learning can accelerate the prediction of physical dynamics relative to numerical solvers. However, limited physical accuracy and an inability to generalize under distributional shift limit its applicability to the real world. We propose to improve accuracy and generalization by incorporating... | [
"deep sequence model",
"equivariant neural network",
"physics-guided deep learning",
"AI for earth science"
] | null | 1,278 | 2002.03061 | title_snapshot |
BbNIbVPJ-42 | The Risks of Invariant Risk Minimization | https://openreview.net/forum?id=BbNIbVPJ-42 | [
"Elan Rosenfeld",
"Pradeep Kumar Ravikumar",
"Andrej Risteski"
] | Poster | null | Invariant Causal Prediction (Peters et al., 2016) is a technique for out-of-distribution generalization which assumes that some aspects of the data distribution vary across the training set but that the underlying causal mechanisms remain constant. Recently, Arjovsky et al. (2019) proposed Invariant Risk Minimization (... | [
"out-of-distribution generalization",
"causality",
"representation learning",
"deep learning"
] | null | 1,273 | 2010.05761 | title_snapshot |
V5j-jdoDDP | Scaling Symbolic Methods using Gradients for Neural Model Explanation | https://openreview.net/forum?id=V5j-jdoDDP | [
"Subham Sekhar Sahoo",
"Subhashini Venugopalan",
"Li Li",
"Rishabh Singh",
"Patrick Riley"
] | Poster | null | Symbolic techniques based on Satisfiability Modulo Theory (SMT) solvers have been proposed for analyzing and verifying neural network properties, but their usage has been fairly limited owing to their poor scalability with larger networks. In this work, we propose a technique for combining gradient-based methods with s... | [
"Neural Model Explanation",
"SMT Solvers",
"Symbolic Methods"
] | null | 1,272 | 2006.16322 | title_snapshot |
ct8_a9h1M | Contextual Dropout: An Efficient Sample-Dependent Dropout Module | https://openreview.net/forum?id=ct8_a9h1M | [
"XINJIE FAN",
"Shujian Zhang",
"Korawat Tanwisuth",
"Xiaoning Qian",
"Mingyuan Zhou"
] | Poster | null | Dropout has been demonstrated as a simple and effective module to not only regularize the training process of deep neural networks, but also provide the uncertainty estimation for prediction. However, the quality of uncertainty estimation is highly dependent on the dropout probabilities. Most current models use the sam... | [
"Efficient Inference Methods",
"Probabilistic Methods",
"Supervised Deep Networks"
] | null | 1,270 | 2103.04181 | title_snapshot |
CR1XOQ0UTh- | Contrastive Learning with Hard Negative Samples | https://openreview.net/forum?id=CR1XOQ0UTh- | [
"Joshua David Robinson",
"Ching-Yao Chuang",
"Suvrit Sra",
"Stefanie Jegelka"
] | Poster | null | We consider the question: how can you sample good negative examples for contrastive learning? We argue that, as with metric learning, learning contrastive representations benefits from hard negative samples (i.e., points that are difficult to distinguish from an anchor point). The key challenge toward using hard negati... | [
"contrastive learning",
"unsupervised representation learning",
"hard negative sampling"
] | null | 1,264 | 2010.04592 | title_snapshot |
6puUoArESGp | Debiasing Concept-based Explanations with Causal Analysis | https://openreview.net/forum?id=6puUoArESGp | [
"Mohammad Taha Bahadori",
"David Heckerman"
] | Poster | null | Concept-based explanation approach is a popular model interpertability tool because it expresses the reasons for a model's predictions in terms of concepts that are meaningful for the domain experts. In this work, we study the problem of the concepts being correlated with confounding information in the features. We pro... | [
"Interpretability",
"Concept-based Explanation"
] | null | 1,263 | 2007.11500 | title_snapshot |
O3Y56aqpChA | Self-training For Few-shot Transfer Across Extreme Task Differences | https://openreview.net/forum?id=O3Y56aqpChA | [
"Cheng Perng Phoo",
"Bharath Hariharan"
] | Oral | null | Most few-shot learning techniques are pre-trained on a large, labeled “base dataset”. In problem domains where such large labeled datasets are not available for pre-training (e.g., X-ray, satellite images), one must resort to pre-training in a different “source” problem domain (e.g., ImageNet), which can be very differ... | [
"few-shot learning",
"self-training",
"cross-domain few-shot learning"
] | null | 1,260 | 2010.07734 | title_snapshot |
TBIzh9b5eaz | Risk-Averse Offline Reinforcement Learning | https://openreview.net/forum?id=TBIzh9b5eaz | [
"Núria Armengol Urpí",
"Sebastian Curi",
"Andreas Krause"
] | Poster | null | Training Reinforcement Learning (RL) agents in high-stakes applications might be too prohibitive due to the risk associated to exploration. Thus, the agent can only use data previously collected by safe policies. While previous work considers optimizing the average performance using offline data, we focus on optimizing... | [
"offline",
"reinforcement learning",
"risk-averse",
"risk sensitive",
"robust",
"safety",
"safe"
] | null | 1,257 | 2102.05371 | title_snapshot |
6UdQLhqJyFD | Parameter Efficient Multimodal Transformers for Video Representation Learning | https://openreview.net/forum?id=6UdQLhqJyFD | [
"Sangho Lee",
"Youngjae Yu",
"Gunhee Kim",
"Thomas Breuel",
"Jan Kautz",
"Yale Song"
] | Poster | null | The recent success of Transformers in the language domain has motivated adapting it to a multimodal setting, where a new visual model is trained in tandem with an already pretrained language model. However, due to the excessive memory requirements from Transformers, existing work typically fixes the language model and ... | [
"Self-supervised learning",
"audio-visual representation learning",
"video representation learning"
] | null | 1,253 | 2012.04124 | title_snapshot |
ZcKPWuhG6wy | Tradeoffs in Data Augmentation: An Empirical Study | https://openreview.net/forum?id=ZcKPWuhG6wy | [
"Raphael Gontijo-Lopes",
"Sylvia Smullin",
"Ekin Dogus Cubuk",
"Ethan Dyer"
] | Poster | null | Though data augmentation has become a standard component of deep neural network training, the underlying mechanism behind the effectiveness of these techniques remains poorly understood. In practice, augmentation policies are often chosen using heuristics of distribution shift or augmentation diversity. Inspired by the... | [
"Generalization",
"Interpretability",
"Understanding Data Augmentation"
] | null | 1,249 | null | null |
eJIJF3-LoZO | Concept Learners for Few-Shot Learning | https://openreview.net/forum?id=eJIJF3-LoZO | [
"Kaidi Cao",
"Maria Brbic",
"Jure Leskovec"
] | Poster | null | Developing algorithms that are able to generalize to a novel task given only a few labeled examples represents a fundamental challenge in closing the gap between machine- and human-level performance. The core of human cognition lies in the structured, reusable concepts that help us to rapidly adapt to new tasks and pro... | [
"few-shot learning",
"meta learning"
] | null | 1,248 | 2007.07375 | title_snapshot |
q8qLAbQBupm | Neural Mechanics: Symmetry and Broken Conservation Laws in Deep Learning Dynamics | https://openreview.net/forum?id=q8qLAbQBupm | [
"Daniel Kunin",
"Javier Sagastuy-Brena",
"Surya Ganguli",
"Daniel LK Yamins",
"Hidenori Tanaka"
] | Poster | null | Understanding the dynamics of neural network parameters during training is one of the key challenges in building a theoretical foundation for deep learning. A central obstacle is that the motion of a network in high-dimensional parameter space undergoes discrete finite steps along complex stochastic gradients derived f... | [
"learning dynamics",
"symmetry",
"loss landscape",
"stochastic differential equation",
"modified equation analysis",
"conservation law",
"hessian",
"geometry",
"physics",
"gradient flow"
] | null | 1,243 | 2012.04728 | title_snapshot |
B7v4QMR6Z9w | Federated Learning Based on Dynamic Regularization | https://openreview.net/forum?id=B7v4QMR6Z9w | [
"Durmus Alp Emre Acar",
"Yue Zhao",
"Ramon Matas",
"Matthew Mattina",
"Paul Whatmough",
"Venkatesh Saligrama"
] | Oral | null | We propose a novel federated learning method for distributively training neural network models, where the server orchestrates cooperation between a subset of randomly chosen devices in each round. We view Federated Learning problem primarily from a communication perspective and allow more device level computations to s... | [
"Federated Learning",
"Deep Neural Networks",
"Distributed Optimization"
] | null | 1,241 | 2111.04263 | title_snapshot |
DNl5s5BXeBn | Fair Mixup: Fairness via Interpolation | https://openreview.net/forum?id=DNl5s5BXeBn | [
"Ching-Yao Chuang",
"Youssef Mroueh"
] | Poster | null | Training classifiers under fairness constraints such as group fairness, regularizes the disparities of predictions between the groups. Nevertheless, even though the constraints are satisfied during training, they might not generalize at evaluation time. To improve the generalizability of fair classifiers, we propose fa... | [
"fairness",
"data augmentation"
] | null | 1,240 | 2103.06503 | title_snapshot |
NECTfffOvn1 | Fidelity-based Deep Adiabatic Scheduling | https://openreview.net/forum?id=NECTfffOvn1 | [
"Eli Ovits",
"Lior Wolf"
] | Spotlight | null | Adiabatic quantum computation is a form of computation that acts by slowly interpolating a quantum system between an easy to prepare initial state and a final state that represents a solution to a given computational problem. The choice of the interpolation schedule is critical to the performance: if at a certain time ... | [] | null | 1,239 | null | null |
UwGY2qjqoLD | Heating up decision boundaries: isocapacitory saturation, adversarial scenarios and generalization bounds | https://openreview.net/forum?id=UwGY2qjqoLD | [
"Bogdan Georgiev",
"Lukas Franken",
"Mayukh Mukherjee"
] | Poster | null | In the present work we study classifiers' decision boundaries via Brownian motion processes in ambient data space and associated probabilistic techniques. Intuitively, our ideas correspond to placing a heat source at the decision boundary and observing how effectively the sample points warm up. We are largely motivated... | [
"Brownian motion",
"deep learning theory",
"decision boundary geometry",
"curvature estimates",
"generalization bounds",
"adversarial attacks/defenses"
] | null | 1,237 | 2101.06061 | title_snapshot |
Cri3xz59ga | Deciphering and Optimizing Multi-Task Learning: a Random Matrix Approach | https://openreview.net/forum?id=Cri3xz59ga | [
"Malik Tiomoko",
"Hafiz Tiomoko Ali",
"Romain Couillet"
] | Spotlight | null | This article provides theoretical insights into the inner workings of multi-task and transfer learning methods, by studying the tractable least-square support vector machine multi-task learning (LS-SVM MTL) method, in the limit of large ($p$) and numerous ($n$) data. By a random matrix analysis applied to a Gaussian mi... | [
"Transfer Learning",
"Multi Task Learning",
"Random Matrix Theory"
] | null | 1,234 | null | null |
xHKVVHGDOEk | Influence Functions in Deep Learning Are Fragile | https://openreview.net/forum?id=xHKVVHGDOEk | [
"Samyadeep Basu",
"Phil Pope",
"Soheil Feizi"
] | Poster | null | Influence functions approximate the effect of training samples in test-time predictions and have a wide variety of applications in machine learning interpretability and uncertainty estimation. A commonly-used (first-order) influence function can be implemented efficiently as a post-hoc method requiring access only to t... | [
"Influence Functions",
"Interpretability"
] | null | 1,227 | 2006.14651 | title_snapshot |
pW2Q2xLwIMD | Few-Shot Learning via Learning the Representation, Provably | https://openreview.net/forum?id=pW2Q2xLwIMD | [
"Simon Shaolei Du",
"Wei Hu",
"Sham M. Kakade",
"Jason D. Lee",
"Qi Lei"
] | Poster | null | This paper studies few-shot learning via representation learning, where one uses $T$ source tasks with $n_1$ data per task to learn a representation in order to reduce the sample complexity of a target task for which there is only $n_2 (\ll n_1)$ data. Specifically, we focus on the setting where there exists a good com... | [
"representation learning",
"statistical learning theory"
] | null | 1,223 | 2002.09434 | title_snapshot |
jMPcEkJpdD | Self-Supervised Learning of Compressed Video Representations | https://openreview.net/forum?id=jMPcEkJpdD | [
"Youngjae Yu",
"Sangho Lee",
"Gunhee Kim",
"Yale Song"
] | Poster | null | Self-supervised learning of video representations has received great attention. Existing methods typically require frames to be decoded before being processed, which increases compute and storage requirements and ultimately hinders large-scale training. In this work, we propose an efficient self-supervised approach to ... | [
"Compressed videos",
"self-supervised learning"
] | null | 1,222 | null | null |
Ig53hpHxS4 | Flowtron: an Autoregressive Flow-based Generative Network for Text-to-Speech Synthesis | https://openreview.net/forum?id=Ig53hpHxS4 | [
"Rafael Valle",
"Kevin J. Shih",
"Ryan Prenger",
"Bryan Catanzaro"
] | Poster | null | In this paper we propose Flowtron: an autoregressive flow-based generative network for text-to-speech synthesis with style transfer and speech variation. Flowtron borrows insights from Autoregressive Flows and revamps Tacotron 2 in order to provide high-quality and expressive mel-spectrogram synthesis. Flowtron is opti... | [
"Text to speech synthesis",
"normalizing flows",
"deep learning"
] | null | 1,218 | 2005.05957 | title_snapshot |
RSU17UoKfJF | R-GAP: Recursive Gradient Attack on Privacy | https://openreview.net/forum?id=RSU17UoKfJF | [
"Junyi Zhu",
"Matthew B. Blaschko"
] | Poster | null | Federated learning frameworks have been regarded as a promising approach to break the dilemma between demands on privacy and the promise of learning from large collections of distributed data. Many such frameworks only ask collaborators to share their local update of a common model, i.e. gradients with respect to local... | [
"privacy leakage from gradients",
"federated learning",
"collaborative learning"
] | null | 1,211 | 2010.07733 | title_snapshot |
iQQK02mxVIT | Why resampling outperforms reweighting for correcting sampling bias with stochastic gradients | https://openreview.net/forum?id=iQQK02mxVIT | [
"Jing An",
"Lexing Ying",
"Yuhua Zhu"
] | Poster | null | A data set sampled from a certain population is biased if the subgroups of the population are sampled at proportions that are significantly different from their underlying proportions. Training machine learning models on biased data sets requires correction techniques to compensate for the bias. We consider two commonl... | [
"biased sampling",
"reweighting",
"resampling",
"stability",
"stochastic asymptotics"
] | null | 1,205 | 2009.13447 | title_snapshot |
2VXyy9mIyU3 | Learning with Instance-Dependent Label Noise: A Sample Sieve Approach | https://openreview.net/forum?id=2VXyy9mIyU3 | [
"Hao Cheng",
"Zhaowei Zhu",
"Xingyu Li",
"Yifei Gong",
"Xing Sun",
"Yang Liu"
] | Poster | null | Human-annotated labels are often prone to noise, and the presence of such noise will degrade the performance of the resulting deep neural network (DNN) models. Much of the literature (with several recent exceptions) of learning with noisy labels focuses on the case when the label noise is independent of features. Pract... | [
"Learning with noisy labels",
"instance-based label noise",
"deep neural networks."
] | null | 1,201 | 2010.02347 | title_snapshot |
43VKWxg_Sqr | Unsupervised Audiovisual Synthesis via Exemplar Autoencoders | https://openreview.net/forum?id=43VKWxg_Sqr | [
"Kangle Deng",
"Aayush Bansal",
"Deva Ramanan"
] | Poster | null | We present an unsupervised approach that converts the input speech of any individual into audiovisual streams of potentially-infinitely many output speakers. Our approach builds on simple autoencoders that project out-of-sample data onto the distribution of the training set. We use exemplar autoencoders to learn the vo... | [
"unsupervised learning",
"autoencoders",
"speech-impaired",
"assistive technology",
"audiovisual synthesis",
"voice conversion"
] | null | 1,197 | 2001.04463 | title_snapshot |
pqZV_srUVmK | Single-Timescale Actor-Critic Provably Finds Globally Optimal Policy | https://openreview.net/forum?id=pqZV_srUVmK | [
"Zuyue Fu",
"Zhuoran Yang",
"Zhaoran Wang"
] | Poster | null | We study the global convergence and global optimality of actor-critic, one of the most popular families of reinforcement learning algorithms. While most existing works on actor-critic employ bi-level or two-timescale updates, we focus on the more practical single-timescale setting, where the actor and critic are update... | [] | null | 1,194 | 2008.00483 | title_snapshot |
oZIvHV04XgC | Wandering within a world: Online contextualized few-shot learning | https://openreview.net/forum?id=oZIvHV04XgC | [
"Mengye Ren",
"Michael Louis Iuzzolino",
"Michael Curtis Mozer",
"Richard Zemel"
] | Poster | null | We aim to bridge the gap between typical human and machine-learning environments by extending the standard framework of few-shot learning to an online, continual setting. In this setting, episodes do not have separate training and testing phases, and instead models are evaluated online while learning novel classes. As ... | [
"Few-shot learning",
"continual learning",
"lifelong learning"
] | null | 1,192 | 2007.04546 | title_snapshot |
tilovEHA3YS | Learning-based Support Estimation in Sublinear Time | https://openreview.net/forum?id=tilovEHA3YS | [
"Talya Eden",
"Piotr Indyk",
"Shyam Narayanan",
"Ronitt Rubinfeld",
"Sandeep Silwal",
"Tal Wagner"
] | Spotlight | null | We consider the problem of estimating the number of distinct elements in a large data set (or, equivalently, the support size of the distribution induced by the data set) from a random sample of its elements. The problem occurs in many applications, including biology, genomics, computer systems and linguistics. A line... | [
"support estimation",
"sublinear",
"learning-based",
"distinct elements",
"chebyshev polynomial"
] | null | 1,189 | 2106.08396 | title_snapshot |
Q1jmmQz72M2 | Neural Delay Differential Equations | https://openreview.net/forum?id=Q1jmmQz72M2 | [
"Qunxi Zhu",
"Yao Guo",
"Wei Lin"
] | Poster | null | Neural Ordinary Differential Equations (NODEs), a framework of continuous-depth neural networks, have been widely applied, showing exceptional efficacy in coping with some representative datasets. Recently, an augmented framework has been successfully developed for conquering some limitations emergent in applicati... | [
"Delay differential equations",
"neural networks"
] | null | 1,188 | 2102.10801 | title_snapshot |
0OlrLvrsHwQ | Learning Parametrised Graph Shift Operators | https://openreview.net/forum?id=0OlrLvrsHwQ | [
"George Dasoulas",
"Johannes F. Lutzeyer",
"Michalis Vazirgiannis"
] | Poster | null | In many domains data is currently represented as graphs and therefore, the graph representation of this data becomes increasingly important in machine learning. Network data is, implicitly or explicitly, always represented using a graph shift operator (GSO) with the most common choices being the adjacency, Laplacian ma... | [
"graph neural networks",
"graph shift operators",
"graph classification",
"node classification",
"graph representation learning"
] | null | 1,178 | 2101.10050 | title_snapshot |
iAmZUo0DxC0 | Unlearnable Examples: Making Personal Data Unexploitable | https://openreview.net/forum?id=iAmZUo0DxC0 | [
"Hanxun Huang",
"Xingjun Ma",
"Sarah Monazam Erfani",
"James Bailey",
"Yisen Wang"
] | Spotlight | null | The volume of "free" data on the internet has been key to the current success of deep learning. However, it also raises privacy concerns about the unauthorized exploitation of personal data for training commercial models. It is thus crucial to develop methods to prevent unauthorized data exploitation. This paper raises... | [
"Unlearnable Examples",
"Data Protection",
"Adversarial Machine Learning"
] | null | 1,169 | 2101.04898 | title_snapshot |
Mk6PZtgAgfq | Rao-Blackwellizing the Straight-Through Gumbel-Softmax Gradient Estimator | https://openreview.net/forum?id=Mk6PZtgAgfq | [
"Max B Paulus",
"Chris J. Maddison",
"Andreas Krause"
] | Oral | null | Gradient estimation in models with discrete latent variables is a challenging problem, because the simplest unbiased estimators tend to have high variance. To counteract this, modern estimators either introduce bias, rely on multiple function evaluations, or use learned, input-dependent baselines. Thus, there is a need... | [
"gumbel",
"softmax",
"gumbel-softmax",
"straight-through",
"straightthrough",
"rao",
"rao-blackwell"
] | null | 1,166 | 2010.04838 | title_snapshot |
6FqKiVAdI3Y | DOP: Off-Policy Multi-Agent Decomposed Policy Gradients | https://openreview.net/forum?id=6FqKiVAdI3Y | [
"Yihan Wang",
"Beining Han",
"Tonghan Wang",
"Heng Dong",
"Chongjie Zhang"
] | Poster | null | Multi-agent policy gradient (MAPG) methods recently witness vigorous progress. However, there is a significant performance discrepancy between MAPG methods and state-of-the-art multi-agent value-based approaches. In this paper, we investigate causes that hinder the performance of MAPG algorithms and present a multi-age... | [
"Multi-Agent Reinforcement Learning",
"Multi-Agent Policy Gradients"
] | null | 1,158 | 2007.12322 | title_judge |
lf7st0bJIA5 | Unsupervised Discovery of 3D Physical Objects from Video | https://openreview.net/forum?id=lf7st0bJIA5 | [
"Yilun Du",
"Kevin A. Smith",
"Tomer Ullman",
"Joshua B. Tenenbaum",
"Jiajun Wu"
] | Poster | null | We study the problem of unsupervised physical object discovery. While existing frameworks aim to decompose scenes into 2D segments based off each object's appearance, we explore how physics, especially object interactions, facilitates disentangling of 3D geometry and position of objects from video, in an unsupervised m... | [
"unsupervised object discovery",
"surprisal",
"scene decomposition",
"physical scene understanding"
] | null | 1,156 | 2007.12348 | title_snapshot |
OqtLIabPTit | Exploring Balanced Feature Spaces for Representation Learning | https://openreview.net/forum?id=OqtLIabPTit | [
"Bingyi Kang",
"Yu Li",
"Sa Xie",
"Zehuan Yuan",
"Jiashi Feng"
] | Poster | null | Existing self-supervised learning (SSL) methods are mostly applied for training representation models from artificially balanced datasets (e.g., ImageNet). It is unclear how well they will perform in the practical scenarios where datasets are often imbalanced w.r.t. the classes. Motivated by this question, we conduct a... | [
"Representation Learning",
"Contrastive Learning",
"Long-Tailed Recognition"
] | null | 1,154 | null | null |
n7wIfYPdVet | Auxiliary Learning by Implicit Differentiation | https://openreview.net/forum?id=n7wIfYPdVet | [
"Aviv Navon",
"Idan Achituve",
"Haggai Maron",
"Gal Chechik",
"Ethan Fetaya"
] | Poster | null | Training neural networks with auxiliary tasks is a common practice for improving the performance on a main task of interest.
Two main challenges arise in this multi-task learning setting: (i) designing useful auxiliary tasks; and (ii) combining auxiliary tasks into a single coherent loss. Here, we propose a novel frame... | [
"Auxiliary Learning",
"Multi-task Learning"
] | null | 1,149 | 2007.02693 | title_snapshot |
i80OPhOCVH2 | On the Bottleneck of Graph Neural Networks and its Practical Implications | https://openreview.net/forum?id=i80OPhOCVH2 | [
"Uri Alon",
"Eran Yahav"
] | Poster | null | Since the proposal of the graph neural network (GNN) by Gori et al. (2005) and Scarselli et al. (2008), one of the major problems in training GNNs was their struggle to propagate information between distant nodes in the graph.
We propose a new explanation for this problem: GNNs are susceptible to a bottleneck when aggr... | [
"graphs",
"GNNs",
"limitations",
"understanding",
"bottleneck",
"over-squashing"
] | null | 1,144 | 2006.05205 | title_snapshot |
L7WD8ZdscQ5 | The Role of Momentum Parameters in the Optimal Convergence of Adaptive Polyak's Heavy-ball Methods | https://openreview.net/forum?id=L7WD8ZdscQ5 | [
"Wei Tao",
"Sheng Long",
"Gaowei Wu",
"Qing Tao"
] | Poster | null | The adaptive stochastic gradient descent (SGD) with momentum has been widely adopted in deep learning as well as convex optimization. In practice, the last iterate is commonly used as the final solution. However, the available regret analysis and the setting of constant momentum parameters only guarantee the optimal co... | [
"Deep learning",
"convex optimization",
"momentum methods",
"adaptive heavy-ball methods",
"optimal convergence"
] | null | 1,139 | 2102.07314 | title_snapshot |
g-wu9TMPODo | How Benign is Benign Overfitting ? | https://openreview.net/forum?id=g-wu9TMPODo | [
"Amartya Sanyal",
"Puneet K. Dokania",
"Varun Kanade",
"Philip Torr"
] | Spotlight | null | We investigate two causes for adversarial vulnerability in deep neural networks: bad data and (poorly) trained models. When trained with SGD, deep neural networks essentially achieve zero training error, even in the presence of label noise, while also exhibiting good generalization on natural test data, something refer... | [
"benign overfitting",
"adversarial robustness",
"memorization",
"generalization"
] | null | 1,136 | 2007.04028 | title_snapshot |
xjXg0bnoDmS | Entropic gradient descent algorithms and wide flat minima | https://openreview.net/forum?id=xjXg0bnoDmS | [
"Fabrizio Pittorino",
"Carlo Lucibello",
"Christoph Feinauer",
"Gabriele Perugini",
"Carlo Baldassi",
"Elizaveta Demyanenko",
"Riccardo Zecchina"
] | Poster | null | The properties of flat minima in the empirical risk landscape of neural networks have been debated for some time. Increasing evidence suggests they possess better generalization capabilities with respect to sharp ones. In this work we first discuss the relationship between alternative measures of flatness: The local en... | [
"flat minima",
"entropic algorithms",
"statistical physics",
"belief-propagation"
] | null | 1,135 | 2006.07897 | title_snapshot |
XLfdzwNKzch | SEDONA: Search for Decoupled Neural Networks toward Greedy Block-wise Learning | https://openreview.net/forum?id=XLfdzwNKzch | [
"Myeongjang Pyeon",
"Jihwan Moon",
"Taeyoung Hahn",
"Gunhee Kim"
] | Poster | null | Backward locking and update locking are well-known sources of inefficiency in backpropagation that prevent from concurrently updating layers. Several works have recently suggested using local error signals to train network blocks asynchronously to overcome these limitations. However, they often require numerous iterati... | [
"AutoML",
"Neural Architecture Search",
"Greedy Learning",
"Deep Learning"
] | null | 1,127 | null | null |
F3s69XzWOia | Coupled Oscillatory Recurrent Neural Network (coRNN): An accurate and (gradient) stable architecture for learning long time dependencies | https://openreview.net/forum?id=F3s69XzWOia | [
"T. Konstantin Rusch",
"Siddhartha Mishra"
] | Oral | null | Circuits of biological neurons, such as in the functional parts of the brain can be modeled as networks of coupled oscillators. Inspired by the ability of these systems to express a rich set of outputs while keeping (gradients of) state variables bounded, we propose a novel architecture for recurrent neural networks. O... | [
"RNNs",
"Oscillators",
"Gradient stability",
"Long-term dependencies"
] | null | 1,126 | 2010.00951 | title_snapshot |
tu29GQT0JFy | not-MIWAE: Deep Generative Modelling with Missing not at Random Data | https://openreview.net/forum?id=tu29GQT0JFy | [
"Niels Bruun Ipsen",
"Pierre-Alexandre Mattei",
"Jes Frellsen"
] | Poster | null | When a missing process depends on the missing values themselves, it needs to be explicitly modelled and taken into account while doing likelihood-based inference. We present an approach for building and fitting deep latent variable models (DLVMs) in cases where the missing process is dependent on the missing data. Spec... | [] | null | 1,124 | 2006.12871 | title_snapshot |
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