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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
9G5MIc-goqB | Reweighting Augmented Samples by Minimizing the Maximal Expected Loss | https://openreview.net/forum?id=9G5MIc-goqB | [
"Mingyang Yi",
"Lu Hou",
"Lifeng Shang",
"Xin Jiang",
"Qun Liu",
"Zhi-Ming Ma"
] | Poster | null | Data augmentation is an effective technique to improve the generalization of deep neural networks. However, previous data augmentation methods usually treat the augmented samples equally without considering their individual impacts on the model. To address this, for the augmented samples from the same training example,... | [
"data augmentation",
"sample reweighting"
] | null | 1,123 | 2103.08933 | title_snapshot |
Xv_s64FiXTv | Learning to Represent Action Values as a Hypergraph on the Action Vertices | https://openreview.net/forum?id=Xv_s64FiXTv | [
"Arash Tavakoli",
"Mehdi Fatemi",
"Petar Kormushev"
] | Poster | null | Action-value estimation is a critical component of many reinforcement learning (RL) methods whereby sample complexity relies heavily on how fast a good estimator for action value can be learned. By viewing this problem through the lens of representation learning, good representations of both state and action can facili... | [
"reinforcement learning",
"structural credit assignment",
"structural inductive bias",
"multi-dimensional discrete action spaces",
"learning action representations"
] | null | 1,122 | 2010.14680 | title_snapshot |
5k8F6UU39V | Autoregressive Entity Retrieval | https://openreview.net/forum?id=5k8F6UU39V | [
"Nicola De Cao",
"Gautier Izacard",
"Sebastian Riedel",
"Fabio Petroni"
] | Spotlight | null | Entities are at the center of how we represent and aggregate knowledge. For instance, Encyclopedias such as Wikipedia are structured by entities (e.g., one per Wikipedia article). The ability to retrieve such entities given a query is fundamental for knowledge-intensive tasks such as entity linking and open-domain ques... | [
"entity retrieval",
"document retrieval",
"autoregressive language model",
"entity linking",
"end-to-end entity linking",
"entity disambiguation",
"constrained beam search"
] | null | 1,115 | 2010.00904 | title_snapshot |
vsU0efpivw | Shapley Explanation Networks | https://openreview.net/forum?id=vsU0efpivw | [
"Rui Wang",
"Xiaoqian Wang",
"David I. Inouye"
] | Poster | null | Shapley values have become one of the most popular feature attribution explanation methods. However, most prior work has focused on post-hoc Shapley explanations, which can be computationally demanding due to its exponential time complexity and preclude model regularization based on Shapley explanations during training... | [
"Shapley values",
"Feature Attribution",
"Interpretable Machine Learning"
] | null | 1,113 | 2104.02297 | title_snapshot |
SRDuJssQud | Neural Approximate Sufficient Statistics for Implicit Models | https://openreview.net/forum?id=SRDuJssQud | [
"Yanzhi Chen",
"Dinghuai Zhang",
"Michael U. Gutmann",
"Aaron Courville",
"Zhanxing Zhu"
] | Spotlight | null | We consider the fundamental problem of how to automatically construct summary statistics for implicit generative models where the evaluation of the likelihood function is intractable but sampling data from the model is possible. The idea is to frame the task of constructing sufficient statistics as learning mutual info... | [
"likelihood-free inference",
"bayesian inference",
"mutual information",
"representation learning",
"summary statistics"
] | null | 1,107 | 2010.10079 | title_snapshot |
mCLVeEpplNE | NBDT: Neural-Backed Decision Tree | https://openreview.net/forum?id=mCLVeEpplNE | [
"Alvin Wan",
"Lisa Dunlap",
"Daniel Ho",
"Jihan Yin",
"Scott Lee",
"Suzanne Petryk",
"Sarah Adel Bargal",
"Joseph E. Gonzalez"
] | Poster | null | Machine learning applications such as finance and medicine demand accurate and justifiable predictions, barring most deep learning methods from use. In response, previous work combines decision trees with deep learning, yielding models that (1) sacrifice interpretability for accuracy or (2) sacrifice accuracy for inter... | [
"explainability",
"computer vision",
"interpretability"
] | null | 1,088 | null | null |
a-xFK8Ymz5J | DiffWave: A Versatile Diffusion Model for Audio Synthesis | https://openreview.net/forum?id=a-xFK8Ymz5J | [
"Zhifeng Kong",
"Wei Ping",
"Jiaji Huang",
"Kexin Zhao",
"Bryan Catanzaro"
] | Oral | null | In this work, we propose DiffWave, a versatile diffusion probabilistic model for conditional and unconditional waveform generation. The model is non-autoregressive, and converts the white noise signal into structured waveform through a Markov chain with a constant number of steps at synthesis. It is efficiently trained... | [
"diffusion probabilistic models",
"audio synthesis",
"speech synthesis",
"generative models"
] | null | 1,087 | 2009.09761 | title_snapshot |
St1giarCHLP | Denoising Diffusion Implicit Models | https://openreview.net/forum?id=St1giarCHLP | [
"Jiaming Song",
"Chenlin Meng",
"Stefano Ermon"
] | Poster | null | Denoising diffusion probabilistic models (DDPMs) have achieved high quality image generation without adversarial training, yet they require simulating a Markov chain for many steps in order to produce a sample. To accelerate sampling, we present denoising diffusion implicit models (DDIMs), a more efficient class of ite... | [
"generative models",
"variational autoencoders",
"denoising score matching",
"variational inference"
] | null | 1,080 | 2010.02502 | title_snapshot |
sSjqmfsk95O | Large Scale Image Completion via Co-Modulated Generative Adversarial Networks | https://openreview.net/forum?id=sSjqmfsk95O | [
"Shengyu Zhao",
"Jonathan Cui",
"Yilun Sheng",
"Yue Dong",
"Xiao Liang",
"Eric I-Chao Chang",
"Yan Xu"
] | Spotlight | null | Numerous task-specific variants of conditional generative adversarial networks have been developed for image completion. Yet, a serious limitation remains that all existing algorithms tend to fail when handling large-scale missing regions. To overcome this challenge, we propose a generic new approach that bridges the g... | [
"image completion",
"generative adversarial networks",
"co-modulation"
] | null | 1,061 | 2103.10428 | title_snapshot |
qrwe7XHTmYb | GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding | https://openreview.net/forum?id=qrwe7XHTmYb | [
"Dmitry Lepikhin",
"HyoukJoong Lee",
"Yuanzhong Xu",
"Dehao Chen",
"Orhan Firat",
"Yanping Huang",
"Maxim Krikun",
"Noam Shazeer",
"Zhifeng Chen"
] | Poster | null | Neural network scaling has been critical for improving the model quality in many real-world machine learning applications with vast amounts of training data and compute. Although this trend of scaling is affirmed to be a sure-fire approach for better model quality, there are challenges on the path such as the computati... | [] | null | 1,059 | 2006.16668 | title_snapshot |
CZ8Y3NzuVzO | What Should Not Be Contrastive in Contrastive Learning | https://openreview.net/forum?id=CZ8Y3NzuVzO | [
"Tete Xiao",
"Xiaolong Wang",
"Alexei A Efros",
"Trevor Darrell"
] | Poster | null | Recent self-supervised contrastive methods have been able to produce impressive transferable visual representations by learning to be invariant to different data augmentations. However, these methods implicitly assume a particular set of representational invariances (e.g., invariance to color), and can perform poorly w... | [
"Self-supervised learning",
"Contrastive learning",
"Representation learning"
] | null | 1,057 | 2008.05659 | title_snapshot |
QIRlze3I6hX | Learning Cross-Domain Correspondence for Control with Dynamics Cycle-Consistency | https://openreview.net/forum?id=QIRlze3I6hX | [
"Qiang Zhang",
"Tete Xiao",
"Alexei A Efros",
"Lerrel Pinto",
"Xiaolong Wang"
] | Oral | null | At the heart of many robotics problems is the challenge of learning correspondences across domains. For instance, imitation learning requires obtaining correspondence between humans and robots; sim-to-real requires correspondence between physics simulators and real hardware; transfer learning requires correspondences b... | [
"self-supervised learning",
"robotics"
] | null | 1,056 | 2012.09811 | title_snapshot |
7FNqrcPtieT | On Data-Augmentation and Consistency-Based Semi-Supervised Learning | https://openreview.net/forum?id=7FNqrcPtieT | [
"Atin Ghosh",
"Alexandre H. Thiery"
] | Poster | null | Recently proposed consistency-based Semi-Supervised Learning (SSL) methods such as the Pi-model, temporal ensembling, the mean teacher, or the virtual adversarial training, achieve the state of the art results in several SSL tasks. These methods can typically reach performances that are comparable to their fully superv... | [
"Semi-Supervised Learning",
"Regularization",
"Data augmentation"
] | null | 1,052 | 2101.06967 | title_snapshot |
6s7ME_X5_Un | DDPNOpt: Differential Dynamic Programming Neural Optimizer | https://openreview.net/forum?id=6s7ME_X5_Un | [
"Guan-Horng Liu",
"Tianrong Chen",
"Evangelos Theodorou"
] | Spotlight | null | Interpretation of Deep Neural Networks (DNNs) training as an optimal control problem with nonlinear dynamical systems has received considerable attention recently, yet the algorithmic development remains relatively limited. In this work, we make an attempt along this line by reformulating the training procedure from th... | [
"deep learning training",
"optimal control",
"trajectory optimization",
"differential dynamica programming"
] | null | 1,049 | 2002.08809 | title_snapshot |
Qm7R_SdqTpT | Diverse Video Generation using a Gaussian Process Trigger | https://openreview.net/forum?id=Qm7R_SdqTpT | [
"Gaurav Shrivastava",
"Abhinav Shrivastava"
] | Poster | null | Generating future frames given a few context (or past) frames is a challenging task. It requires modeling the temporal coherence of videos as well as multi-modality in terms of diversity in the potential future states. Current variational approaches for video generation tend to marginalize over multi-modal future outco... | [
"video synthesis",
"future frame generation",
"video generation",
"gaussian process priors",
"diverse video generation"
] | null | 1,046 | 2107.04619 | title_snapshot |
0pxiMpCyBtr | Monotonic Kronecker-Factored Lattice | https://openreview.net/forum?id=0pxiMpCyBtr | [
"William Taylor Bakst",
"Nobuyuki Morioka",
"Erez Louidor"
] | Poster | null | It is computationally challenging to learn flexible monotonic functions that guarantee model behavior and provide interpretability beyond a few input features, and in a time where minimizing resource use is increasingly important, we must be able to learn such models that are still efficient. In this paper we show how ... | [
"Theory",
"Regularization",
"Algorithms",
"Classification",
"Regression",
"Matrix and Tensor Factorization",
"Fairness",
"Evaluation",
"Efficiency",
"Machine Learning"
] | null | 1,044 | null | null |
MJAqnaC2vO1 | Auto Seg-Loss: Searching Metric Surrogates for Semantic Segmentation | https://openreview.net/forum?id=MJAqnaC2vO1 | [
"Hao Li",
"Chenxin Tao",
"Xizhou Zhu",
"Xiaogang Wang",
"Gao Huang",
"Jifeng Dai"
] | Poster | null | Designing proper loss functions is essential in training deep networks. Especially in the field of semantic segmentation, various evaluation metrics have been proposed for diverse scenarios. Despite the success of the widely adopted cross-entropy loss and its variants, the mis-alignment between the loss functions and e... | [
"Loss Function Search",
"Metric Surrogate",
"Semantic Segmentation"
] | null | 1,042 | 2010.07930 | title_snapshot |
gZ9hCDWe6ke | Deformable DETR: Deformable Transformers for End-to-End Object Detection | https://openreview.net/forum?id=gZ9hCDWe6ke | [
"Xizhou Zhu",
"Weijie Su",
"Lewei Lu",
"Bin Li",
"Xiaogang Wang",
"Jifeng Dai"
] | Oral | null | DETR has been recently proposed to eliminate the need for many hand-designed components in object detection while demonstrating good performance. However, it suffers from slow convergence and limited feature spatial resolution, due to the limitation of Transformer attention modules in processing image feature maps. To ... | [
"Efficient Attention Mechanism",
"Deformation Modeling",
"Multi-scale Representation",
"End-to-End Object Detection"
] | null | 1,041 | 2010.04159 | title_snapshot |
l0V53bErniB | Combining Physics and Machine Learning for Network Flow Estimation | https://openreview.net/forum?id=l0V53bErniB | [
"Arlei Lopes da Silva",
"Furkan Kocayusufoglu",
"Saber Jafarpour",
"Francesco Bullo",
"Ananthram Swami",
"Ambuj Singh"
] | Poster | null | The flow estimation problem consists of predicting missing edge flows in a network (e.g., traffic, power, and water) based on partial observations. These missing flows depend both on the underlying \textit{physics} (edge features and a flow conservation law) as well as the observed edge flows. This paper introduces an ... | [
"graphs",
"networks",
"bilevel optimization",
"metalearning",
"flow graphs"
] | null | 1,031 | null | null |
pmj131uIL9H | NeMo: Neural Mesh Models of Contrastive Features for Robust 3D Pose Estimation | https://openreview.net/forum?id=pmj131uIL9H | [
"Angtian Wang",
"Adam Kortylewski",
"Alan Yuille"
] | Poster | null | 3D pose estimation is a challenging but important task in computer vision. In this work, we show that standard deep learning approaches to 3D pose estimation are not robust to partial occlusion. Inspired by the robustness of generative vision models to partial occlusion, we propose to integrate deep neural networks wit... | [
"Pose Estimation",
"Robust Deep Learning",
"Contrastive Learning",
"Render-and-Compare"
] | null | 1,030 | 2101.12378 | title_snapshot |
Wi5KUNlqWty | How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision | https://openreview.net/forum?id=Wi5KUNlqWty | [
"Dongkwan Kim",
"Alice Oh"
] | Poster | null | Attention mechanism in graph neural networks is designed to assign larger weights to important neighbor nodes for better representation. However, what graph attention learns is not understood well, particularly when graphs are noisy. In this paper, we propose a self-supervised graph attention network (SuperGAT), an imp... | [
"Graph Neural Network",
"Attention Mechanism",
"Self-supervised Learning"
] | null | 1,028 | 2204.04879 | title_snapshot |
gV3wdEOGy_V | MiCE: Mixture of Contrastive Experts for Unsupervised Image Clustering | https://openreview.net/forum?id=gV3wdEOGy_V | [
"Tsung Wei Tsai",
"Chongxuan Li",
"Jun Zhu"
] | Poster | null | We present Mixture of Contrastive Experts (MiCE), a unified probabilistic clustering framework that simultaneously exploits the discriminative representations learned by contrastive learning and the semantic structures captured by a latent mixture model. Motivated by the mixture of experts, MiCE employs a gating functi... | [
"unsupervised learning",
"clustering",
"self supervised learning",
"mixture of experts"
] | null | 1,026 | 2105.01899 | title_snapshot |
TSRTzJnuEBS | Anytime Sampling for Autoregressive Models via Ordered Autoencoding | https://openreview.net/forum?id=TSRTzJnuEBS | [
"Yilun Xu",
"Yang Song",
"Sahaj Garg",
"Linyuan Gong",
"Rui Shu",
"Aditya Grover",
"Stefano Ermon"
] | Poster | null | Autoregressive models are widely used for tasks such as image and audio generation. The sampling process of these models, however, does not allow interruptions and cannot adapt to real-time computational resources. This challenge impedes the deployment of powerful autoregressive models, which involve a slow sampling pr... | [] | null | 1,024 | 2102.11495 | title_snapshot |
KTlJT1nof6d | Initialization and Regularization of Factorized Neural Layers | https://openreview.net/forum?id=KTlJT1nof6d | [
"Mikhail Khodak",
"Neil A. Tenenholtz",
"Lester Mackey",
"Nicolo Fusi"
] | Poster | null | Factorized layers—operations parameterized by products of two or more matrices—occur in a variety of deep learning contexts, including compressed model training, certain types of knowledge distillation, and multi-head self-attention architectures. We study how to initialize and regularize deep nets containing such laye... | [
"model compression",
"knowledge distillation",
"multi-head attention",
"matrix factorization"
] | null | 1,021 | 2105.01029 | title_snapshot |
U4XLJhqwNF1 | CO2: Consistent Contrast for Unsupervised Visual Representation Learning | https://openreview.net/forum?id=U4XLJhqwNF1 | [
"Chen Wei",
"Huiyu Wang",
"Wei Shen",
"Alan Yuille"
] | Poster | null | Contrastive learning has recently been a core for unsupervised visual representation learning. Without human annotation, the common practice is to perform an instance discrimination task: Given a query image crop, label crops from the same image as positives, and crops from other randomly sampled images as negatives. A... | [
"unsupervised representation learning",
"contrastive learning",
"consistency regularization"
] | null | 1,020 | 2010.02217 | title_snapshot |
MuSYkd1hxRP | Geometry-Aware Gradient Algorithms for Neural Architecture Search | https://openreview.net/forum?id=MuSYkd1hxRP | [
"Liam Li",
"Mikhail Khodak",
"Nina Balcan",
"Ameet Talwalkar"
] | Spotlight | null | Recent state-of-the-art methods for neural architecture search (NAS) exploit gradient-based optimization by relaxing the problem into continuous optimization over architectures and shared-weights, a noisy process that remains poorly understood. We argue for the study of single-level empirical risk minimization to under... | [
"neural architecture search",
"automated machine learning",
"weight-sharing",
"optimization"
] | null | 1,019 | 2004.07802 | title_snapshot |
rcQdycl0zyk | Beyond Fully-Connected Layers with Quaternions: Parameterization of Hypercomplex Multiplications with $1/n$ Parameters | https://openreview.net/forum?id=rcQdycl0zyk | [
"Aston Zhang",
"Yi Tay",
"SHUAI Zhang",
"Alvin Chan",
"Anh Tuan Luu",
"Siu Hui",
"Jie Fu"
] | Spotlight | null | Recent works have demonstrated reasonable success of representation learning in hypercomplex space. Specifically, “fully-connected layers with quaternions” (quaternions are 4D hypercomplex numbers), which replace real-valued matrix multiplications in fully-connected layers with Hamilton products of quaternions, both en... | [
"hypercomplex representation learning"
] | null | 1,018 | 2102.08597 | title_snapshot |
uXl3bZLkr3c | Tent: Fully Test-Time Adaptation by Entropy Minimization | https://openreview.net/forum?id=uXl3bZLkr3c | [
"Dequan Wang",
"Evan Shelhamer",
"Shaoteng Liu",
"Bruno Olshausen",
"Trevor Darrell"
] | Spotlight | null | A model must adapt itself to generalize to new and different data during testing. In this setting of fully test-time adaptation the model has only the test data and its own parameters. We propose to adapt by test entropy minimization (tent): we optimize the model for confidence as measured by the entropy of its predict... | [
"deep learning",
"unsupervised learning",
"domain adaptation",
"self-supervision",
"robustness"
] | null | 1,015 | 2006.10726 | title_snapshot |
V1ZHVxJ6dSS | DC3: A learning method for optimization with hard constraints | https://openreview.net/forum?id=V1ZHVxJ6dSS | [
"Priya L. Donti",
"David Rolnick",
"J Zico Kolter"
] | Poster | null | Large optimization problems with hard constraints arise in many settings, yet classical solvers are often prohibitively slow, motivating the use of deep networks as cheap "approximate solvers." Unfortunately, naive deep learning approaches typically cannot enforce the hard constraints of such problems, leading to infea... | [
"approximate constrained optimization",
"implicit differentiation",
"optimal power flow",
"surrogate models"
] | null | 1,011 | 2104.12225 | title_snapshot |
5lhWG3Hj2By | Enforcing robust control guarantees within neural network policies | https://openreview.net/forum?id=5lhWG3Hj2By | [
"Priya L. Donti",
"Melrose Roderick",
"Mahyar Fazlyab",
"J Zico Kolter"
] | Poster | null | When designing controllers for safety-critical systems, practitioners often face a challenging tradeoff between robustness and performance. While robust control methods provide rigorous guarantees on system stability under certain worst-case disturbances, they often yield simple controllers that perform poorly in the a... | [
"robust control",
"reinforcement learning",
"differentiable optimization"
] | null | 1,007 | 2011.08105 | title_snapshot |
Oos98K9Lv-k | Neural Topic Model via Optimal Transport | https://openreview.net/forum?id=Oos98K9Lv-k | [
"He Zhao",
"Dinh Phung",
"Viet Huynh",
"Trung Le",
"Wray Buntine"
] | Spotlight | null | Recently, Neural Topic Models (NTMs) inspired by variational autoencoders have obtained increasingly research interest due to their promising results on text analysis. However, it is usually hard for existing NTMs to achieve good document representation and coherent/diverse topics at the same time. Moreover, they often... | [
"topic modelling",
"optimal transport",
"document analysis"
] | null | 1,005 | 2008.13537 | title_snapshot |
BVSM0x3EDK6 | Robust and Generalizable Visual Representation Learning via Random Convolutions | https://openreview.net/forum?id=BVSM0x3EDK6 | [
"Zhenlin Xu",
"Deyi Liu",
"Junlin Yang",
"Colin Raffel",
"Marc Niethammer"
] | Poster | null | While successful for various computer vision tasks, deep neural networks have shown to be vulnerable to texture style shifts and small perturbations to which humans are robust. In this work, we show that the robustness of neural networks can be greatly improved through the use of random convolutions as data augmentatio... | [
"domain generalization",
"robustness",
"representation learning",
"data augmentation"
] | null | 1,004 | 2007.13003 | title_snapshot |
3SqrRe8FWQ- | WrapNet: Neural Net Inference with Ultra-Low-Precision Arithmetic | https://openreview.net/forum?id=3SqrRe8FWQ- | [
"Renkun Ni",
"Hong-min Chu",
"Oscar Castaneda",
"Ping-yeh Chiang",
"Christoph Studer",
"Tom Goldstein"
] | Poster | null | Low-precision neural networks represent both weights and activations with few bits, drastically reducing the cost of multiplications. Meanwhile, these products are accumulated using high-precision (typically 32-bit) additions. Additions dominate the arithmetic complexity of inference in quantized (e.g., binary) nets, ... | [
"quantization",
"efficient inference"
] | null | 1,002 | 2007.13242 | title_judge |
MtEE0CktZht | Rank the Episodes: A Simple Approach for Exploration in Procedurally-Generated Environments | https://openreview.net/forum?id=MtEE0CktZht | [
"Daochen Zha",
"Wenye Ma",
"Lei Yuan",
"Xia Hu",
"Ji Liu"
] | Poster | null | Exploration under sparse reward is a long-standing challenge of model-free reinforcement learning. The state-of-the-art methods address this challenge by introducing intrinsic rewards to encourage exploration in novel states or uncertain environment dynamics. Unfortunately, methods based on intrinsic rewards often fall... | [
"Reinforcement Learning",
"Exploration",
"Generalization of Reinforcement Learning",
"Self-Imitation"
] | null | 990 | 2101.08152 | title_snapshot |
gwFTuzxJW0 | Stochastic Security: Adversarial Defense Using Long-Run Dynamics of Energy-Based Models | https://openreview.net/forum?id=gwFTuzxJW0 | [
"Mitch Hill",
"Jonathan Craig Mitchell",
"Song-Chun Zhu"
] | Poster | null | The vulnerability of deep networks to adversarial attacks is a central problem for deep learning from the perspective of both cognition and security. The current most successful defense method is to train a classifier using adversarial images created during learning. Another defense approach involves transformation or ... | [
"adversarial defense",
"adversarial robustness",
"energy-based model",
"Markov chain Monte Carlo",
"Langevin sampling",
"adversarial attack"
] | null | 976 | 2005.13525 | title_snapshot |
7wCBOfJ8hJM | Nearest Neighbor Machine Translation | https://openreview.net/forum?id=7wCBOfJ8hJM | [
"Urvashi Khandelwal",
"Angela Fan",
"Dan Jurafsky",
"Luke Zettlemoyer",
"Mike Lewis"
] | Poster | null | We introduce $k$-nearest-neighbor machine translation ($k$NN-MT), which predicts tokens with a nearest-neighbor classifier over a large datastore of cached examples, using representations from a neural translation model for similarity search. This approach requires no additional training and scales to give the decoder ... | [
"nearest neighbors",
"machine translation"
] | null | 971 | 2010.00710 | title_snapshot |
yqPnIRhHtZv | Learning Hyperbolic Representations of Topological Features | https://openreview.net/forum?id=yqPnIRhHtZv | [
"Panagiotis Kyriakis",
"Iordanis Fostiropoulos",
"Paul Bogdan"
] | Poster | null | Learning task-specific representations of persistence diagrams is an important problem in topological data analysis and machine learning. However, current state of the art methods are restricted in terms of their expressivity as they are focused on Euclidean representations. Persistence diagrams often contain features ... | [
"representation learning",
"hyperbolic deep learning",
"persistent homology",
"persistence diagrams"
] | null | 967 | 2103.09273 | title_snapshot |
9xC2tWEwBD | A Panda? No, It's a Sloth: Slowdown Attacks on Adaptive Multi-Exit Neural Network Inference | https://openreview.net/forum?id=9xC2tWEwBD | [
"Sanghyun Hong",
"Yigitcan Kaya",
"Ionuț-Vlad Modoranu",
"Tudor Dumitras"
] | Spotlight | null | Recent increases in the computational demands of deep neural networks (DNNs), combined with the observation that most input samples require only simple models, have sparked interest in input-adaptive multi-exit architectures, such as MSDNets or Shallow-Deep Networks. These architectures enable faster inferences and cou... | [
"Slowdown attacks",
"efficient inference",
"input-adaptive multi-exit neural networks",
"adversarial examples"
] | null | 962 | 2010.02432 | title_snapshot |
8nl0k08uMi | Selectivity considered harmful: evaluating the causal impact of class selectivity in DNNs | https://openreview.net/forum?id=8nl0k08uMi | [
"Matthew L Leavitt",
"Ari S. Morcos"
] | Poster | null | The properties of individual neurons are often analyzed in order to understand the biological and artificial neural networks in which they're embedded. Class selectivity—typically defined as how different a neuron's responses are across different classes of stimuli or data samples—is commonly used for this purpose. How... | [
"interpretability",
"explainability",
"empirical analysis",
"deep learning",
"selectivity"
] | null | 954 | 2003.01262 | title_snapshot |
IMPA6MndSXU | Integrating Categorical Semantics into Unsupervised Domain Translation | https://openreview.net/forum?id=IMPA6MndSXU | [
"Samuel Lavoie-Marchildon",
"Faruk Ahmed",
"Aaron Courville"
] | Poster | null | While unsupervised domain translation (UDT) has seen a lot of success recently, we argue that mediating its translation via categorical semantic features could broaden its applicability. In particular, we demonstrate that categorical semantics improves the translation between perceptually different domains sharing mult... | [
"Unsupervised Domain Translation",
"Unsupervised Learning",
"Image-to-Image Translation",
"Deep Learning",
"Representation Learning"
] | null | 953 | 2010.01262 | title_snapshot |
Ut1vF_q_vC | Are Neural Rankers still Outperformed by Gradient Boosted Decision Trees? | https://openreview.net/forum?id=Ut1vF_q_vC | [
"Zhen Qin",
"Le Yan",
"Honglei Zhuang",
"Yi Tay",
"Rama Kumar Pasumarthi",
"Xuanhui Wang",
"Michael Bendersky",
"Marc Najork"
] | Spotlight | null | Despite the success of neural models on many major machine learning problems, their effectiveness on traditional Learning-to-Rank (LTR) problems is still not widely acknowledged. We first validate this concern by showing that most recent neural LTR models are, by a large margin, inferior to the best publicly available ... | [
"Learning to Rank",
"benchmark",
"neural network",
"gradient boosted decision trees"
] | null | 952 | null | null |
8wqCDnBmnrT | Zero-shot Synthesis with Group-Supervised Learning | https://openreview.net/forum?id=8wqCDnBmnrT | [
"Yunhao Ge",
"Sami Abu-El-Haija",
"Gan Xin",
"Laurent Itti"
] | Poster | null | Visual cognition of primates is superior to that of artificial neural networks in its ability to “envision” a visual object, even a newly-introduced one, in different attributes including pose, position, color, texture, etc. To aid neural networks to envision objects with different attributes, we propose a family of ... | [
"Disentangled representation learning",
"Group-supervised learning",
"Zero-shot synthesis",
"Knowledge factorization"
] | null | 951 | 2009.06586 | title_snapshot |
UuchYL8wSZo | Learning Generalizable Visual Representations via Interactive Gameplay | https://openreview.net/forum?id=UuchYL8wSZo | [
"Luca Weihs",
"Aniruddha Kembhavi",
"Kiana Ehsani",
"Sarah M Pratt",
"Winson Han",
"Alvaro Herrasti",
"Eric Kolve",
"Dustin Schwenk",
"Roozbeh Mottaghi",
"Ali Farhadi"
] | Oral | null | A growing body of research suggests that embodied gameplay, prevalent not just in human cultures but across a variety of animal species including turtles and ravens, is critical in developing the neural flexibility for creative problem solving, decision making, and socialization. Comparatively little is known regarding... | [
"representation learning",
"deep reinforcement learning",
"computer vision"
] | null | 949 | 1912.08195 | title_snapshot |
g21u6nlbPzn | VA-RED$^2$: Video Adaptive Redundancy Reduction | https://openreview.net/forum?id=g21u6nlbPzn | [
"Bowen Pan",
"Rameswar Panda",
"Camilo Luciano Fosco",
"Chung-Ching Lin",
"Alex J Andonian",
"Yue Meng",
"Kate Saenko",
"Aude Oliva",
"Rogerio Feris"
] | Poster | null | Performing inference on deep learning models for videos remains a challenge due to the large amount of computational resources required to achieve robust recognition. An inherent property of real-world videos is the high correlation of information across frames which can translate into redundancy in either temporal or ... | [] | null | 948 | 2102.07887 | title_snapshot |
YWtLZvLmud7 | BERTology Meets Biology: Interpreting Attention in Protein Language Models | https://openreview.net/forum?id=YWtLZvLmud7 | [
"Jesse Vig",
"Ali Madani",
"Lav R. Varshney",
"Caiming Xiong",
"richard socher",
"Nazneen Rajani"
] | Poster | null | Transformer architectures have proven to learn useful representations for protein classification and generation tasks. However, these representations present challenges in interpretability. In this work, we demonstrate a set of methods for analyzing protein Transformer models through the lens of attention. We show that... | [
"interpretability",
"black box",
"computational biology",
"representation learning",
"attention",
"transformers",
"visualization",
"natural language processing"
] | null | 947 | 2006.15222 | title_snapshot |
J8_GttYLFgr | Trajectory Prediction using Equivariant Continuous Convolution | https://openreview.net/forum?id=J8_GttYLFgr | [
"Robin Walters",
"Jinxi Li",
"Rose Yu"
] | Poster | null | Trajectory prediction is a critical part of many AI applications, for example, the safe operation of autonomous vehicles. However, current methods are prone to making inconsistent and physically unrealistic predictions. We leverage insights from fluid dynamics to overcome this limitation by considering internal symmet... | [
"equivariant",
"symmetry",
"trajectory prediction",
"continuous convolution",
"argoverse"
] | null | 942 | 2010.11344 | title_snapshot |
XOjv2HxIF6i | Unsupervised Meta-Learning through Latent-Space Interpolation in Generative Models | https://openreview.net/forum?id=XOjv2HxIF6i | [
"Siavash Khodadadeh",
"Sharare Zehtabian",
"Saeed Vahidian",
"Weijia Wang",
"Bill Lin",
"Ladislau Boloni"
] | Poster | null | Several recently proposed unsupervised meta-learning approaches rely on synthetic meta-tasks created using techniques such as random selection, clustering and/or augmentation. In this work, we describe a novel approach that generates meta-tasks using generative models. The proposed family of algorithms generate pairs o... | [
"Meta-learning",
"Unsupervised learning",
"GANs"
] | null | 930 | 2006.10236 | title_snapshot |
mEdwVCRJuX4 | Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization | https://openreview.net/forum?id=mEdwVCRJuX4 | [
"Kaidi Cao",
"Yining Chen",
"Junwei Lu",
"Nikos Arechiga",
"Adrien Gaidon",
"Tengyu Ma"
] | Poster | null | Real-world large-scale datasets are heteroskedastic and imbalanced --- labels have varying levels of uncertainty and label distributions are long-tailed. Heteroskedasticity and imbalance challenge deep learning algorithms due to the difficulty of distinguishing among mislabeled, ambiguous, and rare examples. Addressing... | [
"deep learning",
"noise robust learning",
"imbalanced learning"
] | null | 926 | 2006.15766 | title_snapshot |
O9bnihsFfXU | Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learning | https://openreview.net/forum?id=O9bnihsFfXU | [
"Aviral Kumar",
"Rishabh Agarwal",
"Dibya Ghosh",
"Sergey Levine"
] | Poster | null | We identify an implicit under-parameterization phenomenon in value-based deep RL methods that use bootstrapping: when value functions, approximated using deep neural networks, are trained with gradient descent using iterated regression onto target values generated by previous instances of the value network, more gradie... | [
"deep Q-learning",
"data-efficient RL",
"rank-collapse",
"offline RL"
] | null | 913 | 2010.14498 | title_snapshot |
ESG-DMKQKsD | Bowtie Networks: Generative Modeling for Joint Few-Shot Recognition and Novel-View Synthesis | https://openreview.net/forum?id=ESG-DMKQKsD | [
"Zhipeng Bao",
"Yu-Xiong Wang",
"Martial Hebert"
] | Poster | null | We propose a novel task of joint few-shot recognition and novel-view synthesis: given only one or few images of a novel object from arbitrary views with only category annotation, we aim to simultaneously learn an object classifier and generate images of that type of object from new viewpoints. While existing work copes... | [
"computer vision",
"object recognition",
"few-shot learning",
"generative models",
"adversarial training"
] | null | 906 | 2008.06981 | title_snapshot |
c9-WeM-ceB | Saliency is a Possible Red Herring When Diagnosing Poor Generalization | https://openreview.net/forum?id=c9-WeM-ceB | [
"Joseph D Viviano",
"Becks Simpson",
"Francis Dutil",
"Yoshua Bengio",
"Joseph Paul Cohen"
] | Poster | null | Poor generalization is one symptom of models that learn to predict target variables using spuriously-correlated image features present only in the training distribution instead of the true image features that denote a class. It is often thought that this can be diagnosed visually using attribution (aka saliency) maps. ... | [
"Feature Attribution",
"Generalization",
"Saliency"
] | null | 899 | 1910.00199 | title_snapshot |
Qm8UNVCFdh | What Can You Learn From Your Muscles? Learning Visual Representation from Human Interactions | https://openreview.net/forum?id=Qm8UNVCFdh | [
"Kiana Ehsani",
"Daniel Gordon",
"Thomas Hai Dang Nguyen",
"Roozbeh Mottaghi",
"Ali Farhadi"
] | Poster | null | Learning effective representations of visual data that generalize to a variety of downstream tasks has been a long quest for computer vision. Most representation learning approaches rely solely on visual data such as images or videos. In this paper, we explore a novel approach, where we use human interaction and attent... | [
"representation learning",
"computer vision"
] | null | 894 | 2010.08539 | title_snapshot |
qda7-sVg84 | Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning | https://openreview.net/forum?id=qda7-sVg84 | [
"Rishabh Agarwal",
"Marlos C. Machado",
"Pablo Samuel Castro",
"Marc G Bellemare"
] | Spotlight | null | Reinforcement learning methods trained on few environments rarely learn policies that generalize to unseen environments. To improve generalization, we incorporate the inherent sequential structure in reinforcement learning into the representation learning process. This approach is orthogonal to recent approaches, which... | [
"Reinforcement",
"Generalization",
"Contrastive learning",
"Bisimulation",
"Representation Learning"
] | null | 887 | 2101.05265 | title_snapshot |
RLRXCV6DbEJ | Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images | https://openreview.net/forum?id=RLRXCV6DbEJ | [
"Rewon Child"
] | Spotlight | null | We present a hierarchical VAE that, for the first time, generates samples quickly $\textit{and}$ outperforms the PixelCNN in log-likelihood on all natural image benchmarks. We begin by observing that, in theory, VAEs can actually represent autoregressive models, as well as faster, better models if they exist, when made... | [
"VAE",
"generative modeling",
"deep learning",
"likelihood-based models"
] | null | 884 | 2011.10650 | title_snapshot |
iaO86DUuKi | Conservative Safety Critics for Exploration | https://openreview.net/forum?id=iaO86DUuKi | [
"Homanga Bharadhwaj",
"Aviral Kumar",
"Nicholas Rhinehart",
"Sergey Levine",
"Florian Shkurti",
"Animesh Garg"
] | Poster | null | Safe exploration presents a major challenge in reinforcement learning (RL): when active data collection requires deploying partially trained policies, we must ensure that these policies avoid catastrophically unsafe regions, while still enabling trial and error learning. In this paper, we target the problem of safe exp... | [
"Safe exploration",
"Reinforcement Learning"
] | null | 883 | 2010.14497 | title_snapshot |
4c0J6lwQ4_ | Multi-Time Attention Networks for Irregularly Sampled Time Series | https://openreview.net/forum?id=4c0J6lwQ4_ | [
"Satya Narayan Shukla",
"Benjamin Marlin"
] | Poster | null | Irregular sampling occurs in many time series modeling applications where it presents a significant challenge to standard deep learning models. This work is motivated by the analysis of physiological time series data in electronic health records, which are sparse, irregularly sampled, and multivariate. In this paper, w... | [
"irregular sampling",
"multivariate time series",
"attention",
"missing data"
] | null | 881 | 2101.10318 | title_snapshot |
6DOZ8XNNfGN | Graph Traversal with Tensor Functionals: A Meta-Algorithm for Scalable Learning | https://openreview.net/forum?id=6DOZ8XNNfGN | [
"Elan Sopher Markowitz",
"Keshav Balasubramanian",
"Mehrnoosh Mirtaheri",
"Sami Abu-El-Haija",
"Bryan Perozzi",
"Greg Ver Steeg",
"Aram Galstyan"
] | Poster | null | Graph Representation Learning (GRL) methods have impacted fields from chemistry to social science. However, their algorithmic implementations are specialized to specific use-cases e.g. "message passing" methods are run differently from "node embedding" ones. Despite their apparent differences, all these methods utilize... | [
"Graph",
"Learning",
"Algorithm",
"Scale",
"Message Passing",
"Node Embeddings"
] | null | 880 | 2102.04350 | title_snapshot |
YmA86Zo-P_t | What they do when in doubt: a study of inductive biases in seq2seq learners | https://openreview.net/forum?id=YmA86Zo-P_t | [
"Eugene Kharitonov",
"Rahma Chaabouni"
] | Poster | null | Sequence-to-sequence (seq2seq) learners are widely used, but we still have only limited knowledge about what inductive biases shape the way they generalize. We address that by investigating how popular seq2seq learners generalize in tasks that have high ambiguity in the training data. We use four new tasks to study le... | [
"inductive biases",
"description length",
"sequence-to-sequence models"
] | null | 878 | 2006.14953 | title_snapshot |
9EsrXMzlFQY | Async-RED: A Provably Convergent Asynchronous Block Parallel Stochastic Method using Deep Denoising Priors | https://openreview.net/forum?id=9EsrXMzlFQY | [
"Yu Sun",
"Jiaming Liu",
"Yiran Sun",
"Brendt Wohlberg",
"Ulugbek Kamilov"
] | Spotlight | null | Regularization by denoising (RED) is a recently developed framework for solving inverse problems by integrating advanced denoisers as image priors. Recent work has shown its state-of-the-art performance when combined with pre-trained deep denoisers. However, current RED algorithms are inadequate for parallel processing... | [
"Regularization by denoising",
"Computational imaging",
"asynchronous parallel algorithm",
"Deep denoising priors"
] | null | 874 | 2010.01446 | title_snapshot |
YtMG5ex0ou | Tomographic Auto-Encoder: Unsupervised Bayesian Recovery of Corrupted Data | https://openreview.net/forum?id=YtMG5ex0ou | [
"Francesco Tonolini",
"Pablo Garcia Moreno",
"Andreas Damianou",
"Roderick Murray-Smith"
] | Poster | null | We propose a new probabilistic method for unsupervised recovery of corrupted data. Given a large ensemble of degraded samples, our method recovers accurate posteriors of clean values, allowing the exploration of the manifold of possible reconstructed data and hence characterising the underlying uncertainty. In this set... | [
"Missing value imputation",
"variational inference",
"variational auto-encoders"
] | null | 871 | 2006.16938 | title_snapshot |
V69LGwJ0lIN | OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning | https://openreview.net/forum?id=V69LGwJ0lIN | [
"Anurag Ajay",
"Aviral Kumar",
"Pulkit Agrawal",
"Sergey Levine",
"Ofir Nachum"
] | Poster | null | Reinforcement learning (RL) has achieved impressive performance in a variety of online settings in which an agent’s ability to query the environment for transitions and rewards is effectively unlimited. However, in many practical applications, the situation is reversed: an agent may have access to large amounts of und... | [
"Offline Reinforcement Learning",
"Primitive Discovery",
"Unsupervised Learning"
] | null | 870 | 2010.13611 | title_snapshot |
ZzwDy_wiWv | Knowledge distillation via softmax regression representation learning | https://openreview.net/forum?id=ZzwDy_wiWv | [
"Jing Yang",
"Brais Martinez",
"Adrian Bulat",
"Georgios Tzimiropoulos"
] | Poster | null | This paper addresses the problem of model compression via knowledge distillation. We advocate for a method that optimizes the output feature of the penultimate layer of the student network and hence is directly related to representation learning. Previous distillation methods which typically impose direct feature match... | [] | null | 869 | null | null |
X4y_10OX-hX | Large Associative Memory Problem in Neurobiology and Machine Learning | https://openreview.net/forum?id=X4y_10OX-hX | [
"Dmitry Krotov",
"John J. Hopfield"
] | Poster | null | Dense Associative Memories or modern Hopfield networks permit storage and reliable retrieval of an exponentially large (in the dimension of feature space) number of memories. At the same time, their naive implementation is non-biological, since it seemingly requires the existence of many-body synaptic junctions betwee... | [
"associative memory",
"Hopfield networks",
"modern Hopfield networks",
"neuroscience"
] | null | 868 | 2008.06996 | title_snapshot |
tHgJoMfy6nI | Remembering for the Right Reasons: Explanations Reduce Catastrophic Forgetting | https://openreview.net/forum?id=tHgJoMfy6nI | [
"Sayna Ebrahimi",
"Suzanne Petryk",
"Akash Gokul",
"William Gan",
"Joseph E. Gonzalez",
"Marcus Rohrbach",
"trevor darrell"
] | Poster | null | The goal of continual learning (CL) is to learn a sequence of tasks without suffering from the phenomenon of catastrophic forgetting. Previous work has shown that leveraging memory in the form of a replay buffer can reduce performance degradation on prior tasks. We hypothesize that forgetting can be further reduced whe... | [
"Continual Learning",
"Lifelong Learning",
"Catastrophic Forgetting",
"XAI",
"Explainability"
] | null | 864 | 2010.01528 | title_snapshot |
O-6Pm_d_Q- | Deep Networks and the Multiple Manifold Problem | https://openreview.net/forum?id=O-6Pm_d_Q- | [
"Sam Buchanan",
"Dar Gilboa",
"John Wright"
] | Poster | null | We study the multiple manifold problem, a binary classification task modeled on applications in machine vision, in which a deep fully-connected neural network is trained to separate two low-dimensional submanifolds of the unit sphere. We provide an analysis of the one-dimensional case, proving for a simple manifold con... | [
"deep learning",
"overparameterized neural networks",
"low-dimensional structure"
] | null | 863 | 2008.11245 | title_snapshot |
gLWj29369lW | Interpreting Knowledge Graph Relation Representation from Word Embeddings | https://openreview.net/forum?id=gLWj29369lW | [
"Carl Allen",
"Ivana Balazevic",
"Timothy Hospedales"
] | Poster | null | Many models learn representations of knowledge graph data by exploiting its low-rank latent structure, encoding known relations between entities and enabling unknown facts to be inferred. To predict whether a relation holds between entities, embeddings are typically compared in the latent space following a relation-spe... | [
"knowledge graphs",
"word embedding",
"representation learning"
] | null | 860 | 1909.11611 | title_snapshot |
MIDckA56aD | Learning perturbation sets for robust machine learning | https://openreview.net/forum?id=MIDckA56aD | [
"Eric Wong",
"J Zico Kolter"
] | Poster | null | Although much progress has been made towards robust deep learning, a significant gap in robustness remains between real-world perturbations and more narrowly defined sets typically studied in adversarial defenses. In this paper, we aim to bridge this gap by learning perturbation sets from data, in order to characterize... | [
"adversarial examples",
"perturbation sets",
"robust machine learning",
"conditional variational autoencoder"
] | null | 859 | 2007.08450 | title_snapshot |
3AOj0RCNC2 | Gradient Projection Memory for Continual Learning | https://openreview.net/forum?id=3AOj0RCNC2 | [
"Gobinda Saha",
"Isha Garg",
"Kaushik Roy"
] | Oral | null | The ability to learn continually without forgetting the past tasks is a desired attribute for artificial learning systems. Existing approaches to enable such learning in artificial neural networks usually rely on network growth, importance based weight update or replay of old data from the memory. In contrast, we propo... | [
"Continual Learning",
"Representation Learning",
"Computer Vision",
"Deep learning"
] | null | 857 | 2103.09762 | title_snapshot |
ZK6vTvb84s | A Trainable Optimal Transport Embedding for Feature Aggregation and its Relationship to Attention | https://openreview.net/forum?id=ZK6vTvb84s | [
"Grégoire Mialon",
"Dexiong Chen",
"Alexandre d'Aspremont",
"Julien Mairal"
] | Poster | null | We address the problem of learning on sets of features, motivated by the need of performing pooling operations in long biological sequences of varying sizes, with long-range dependencies, and possibly few labeled data. To address this challenging task, we introduce a parametrized representation of fixed size, which em... | [
"bioinformatics",
"optimal transport",
"kernel methods",
"attention",
"transformers"
] | null | 855 | 2006.12065 | title_snapshot |
TTUVg6vkNjK | RODE: Learning Roles to Decompose Multi-Agent Tasks | https://openreview.net/forum?id=TTUVg6vkNjK | [
"Tonghan Wang",
"Tarun Gupta",
"Anuj Mahajan",
"Bei Peng",
"Shimon Whiteson",
"Chongjie Zhang"
] | Poster | null | Role-based learning holds the promise of achieving scalable multi-agent learning by decomposing complex tasks using roles. However, it is largely unclear how to efficiently discover such a set of roles. To solve this problem, we propose to first decompose joint action spaces into restricted role action spaces by cluste... | [
"Multi-Agent Reinforcement Learning",
"Role-Based Learning",
"Hierarchical Multi-Agent Learning",
"Multi-Agent Transfer Learning"
] | null | 850 | 2010.01523 | title_snapshot |
4qR3coiNaIv | Scalable Bayesian Inverse Reinforcement Learning | https://openreview.net/forum?id=4qR3coiNaIv | [
"Alex James Chan",
"Mihaela van der Schaar"
] | Poster | null | Bayesian inference over the reward presents an ideal solution to the ill-posed nature of the inverse reinforcement learning problem. Unfortunately current methods generally do not scale well beyond the small tabular setting due to the need for an inner-loop MDP solver, and even non-Bayesian methods that do themselves s... | [
"Bayesian",
"Inverse reinforcement learning",
"Imitation Learning"
] | null | 846 | 2102.06483 | title_snapshot |
h0de3QWtGG | Learning "What-if" Explanations for Sequential Decision-Making | https://openreview.net/forum?id=h0de3QWtGG | [
"Ioana Bica",
"Daniel Jarrett",
"Alihan Hüyük",
"Mihaela van der Schaar"
] | Poster | null | Building interpretable parameterizations of real-world decision-making on the basis of demonstrated behavior--i.e. trajectories of observations and actions made by an expert maximizing some unknown reward function--is essential for introspecting and auditing policies in different institutions. In this paper, we propose... | [
"counterfactuals",
"explaining decision-making",
"preference learning"
] | null | 843 | 2007.13531 | title_snapshot |
-gfhS00XfKj | Learning advanced mathematical computations from examples | https://openreview.net/forum?id=-gfhS00XfKj | [
"Francois Charton",
"Amaury Hayat",
"Guillaume Lample"
] | Poster | null | Using transformers over large generated datasets, we train models to learn mathematical properties of differential systems, such as local stability, behavior at infinity and controllability. We achieve near perfect prediction of qualitative characteristics, and good approximations of numerical features of the system. T... | [
"differential equations",
"computation",
"transformers",
"deep learning"
] | null | 841 | 2006.06462 | title_snapshot |
qkLMTphG5-h | Repurposing Pretrained Models for Robust Out-of-domain Few-Shot Learning | https://openreview.net/forum?id=qkLMTphG5-h | [
"Namyeong Kwon",
"Hwidong Na",
"Gabriel Huang",
"Simon Lacoste-Julien"
] | Poster | null | Model-agnostic meta-learning (MAML) is a popular method for few-shot learning but assumes that we have access to the meta-training set. In practice, training on the meta-training set may not always be an option due to data privacy concerns, intellectual property issues, or merely lack of computing resources. In this pa... | [
"Meta-learning",
"Few-shot learning",
"Out-of-domain",
"Uncertainty",
"Ensemble",
"Adversarial training",
"Stepsize optimization"
] | null | 831 | 2103.09027 | title_snapshot |
5jRVa89sZk | Empirical Analysis of Unlabeled Entity Problem in Named Entity Recognition | https://openreview.net/forum?id=5jRVa89sZk | [
"Yangming Li",
"lemao liu",
"Shuming Shi"
] | Poster | null | In many scenarios, named entity recognition (NER) models severely suffer from unlabeled entity problem, where the entities of a sentence may not be fully annotated. Through empirical studies performed on synthetic datasets, we find two causes of performance degradation. One is the reduction of annotated entities and th... | [
"Named Entity Recognition",
"Unlabeled Entity Problem",
"Negative Sampling"
] | null | 823 | 2012.05426 | title_snapshot |
eQe8DEWNN2W | Calibration of Neural Networks using Splines | https://openreview.net/forum?id=eQe8DEWNN2W | [
"Kartik Gupta",
"Amir Rahimi",
"Thalaiyasingam Ajanthan",
"Thomas Mensink",
"Cristian Sminchisescu",
"Richard Hartley"
] | Poster | null | Calibrating neural networks is of utmost importance when employing them in safety-critical applications where the downstream decision making depends on the predicted probabilities. Measuring calibration error amounts to comparing two empirical distributions. In this work, we introduce a binning-free calibration measure... | [
"neural network calibration",
"uncertainty",
"calibration measure"
] | null | 812 | 2006.12800 | title_snapshot |
17VnwXYZyhH | Probing BERT in Hyperbolic Spaces | https://openreview.net/forum?id=17VnwXYZyhH | [
"Boli Chen",
"Yao Fu",
"Guangwei Xu",
"Pengjun Xie",
"Chuanqi Tan",
"Mosha Chen",
"Liping Jing"
] | Poster | null | Recently, a variety of probing tasks are proposed to discover linguistic properties learned in contextualized word embeddings. Many of these works implicitly assume these embeddings lay in certain metric spaces, typically the Euclidean space. This work considers a family of geometrically special spaces, the hyperbolic ... | [
"Hyperbolic",
"BERT",
"Probe",
"Syntax",
"Sentiment"
] | null | 807 | 2104.03869 | title_snapshot |
Zbc-ue9p_rE | Refining Deep Generative Models via Discriminator Gradient Flow | https://openreview.net/forum?id=Zbc-ue9p_rE | [
"Abdul Fatir Ansari",
"Ming Liang Ang",
"Harold Soh"
] | Poster | null | Deep generative modeling has seen impressive advances in recent years, to the point where it is now commonplace to see simulated samples (e.g., images) that closely resemble real-world data. However, generation quality is generally inconsistent for any given model and can vary dramatically between samples. We introduce... | [
"gradient flows",
"generative models",
"GAN",
"VAE",
"Normalizing Flow"
] | null | 805 | 2012.00780 | title_snapshot |
BtZhsSGNRNi | Coping with Label Shift via Distributionally Robust Optimisation | https://openreview.net/forum?id=BtZhsSGNRNi | [
"Jingzhao Zhang",
"Aditya Krishna Menon",
"Andreas Veit",
"Srinadh Bhojanapalli",
"Sanjiv Kumar",
"Suvrit Sra"
] | Poster | null | The label shift problem refers to the supervised learning setting where the train and test label distributions do not match. Existing work addressing label shift usually assumes access to an unlabelled test sample. This sample may be used to estimate the test label distribution, and to then train a suitably re-weighted... | [
"Label shift",
"distributional robust optimization"
] | null | 801 | 2010.12230 | title_snapshot |
XAS3uKeFWj | Variational State-Space Models for Localisation and Dense 3D Mapping in 6 DoF | https://openreview.net/forum?id=XAS3uKeFWj | [
"Atanas Mirchev",
"Baris Kayalibay",
"Patrick van der Smagt",
"Justin Bayer"
] | Poster | null | We solve the problem of 6-DoF localisation and 3D dense reconstruction in spatial environments as approximate Bayesian inference in a deep state-space model. Our approach leverages both learning and domain knowledge from multiple-view geometry and rigid-body dynamics. This results in an expressive predictive model of t... | [
"Generative models",
"Bayesian inference",
"Variational inference",
"SLAM",
"Deep learning"
] | null | 792 | 2006.10178 | title_snapshot |
bJxgv5C3sYc | Few-Shot Bayesian Optimization with Deep Kernel Surrogates | https://openreview.net/forum?id=bJxgv5C3sYc | [
"Martin Wistuba",
"Josif Grabocka"
] | Poster | null | Hyperparameter optimization (HPO) is a central pillar in the automation of machine learning solutions and is mainly performed via Bayesian optimization, where a parametric surrogate is learned to approximate the black box response function (e.g. validation error). Unfortunately, evaluating the response function is comp... | [
"bayesian optimization",
"metalearning",
"few-shot learning",
"automl"
] | null | 788 | 2101.07667 | title_snapshot |
T6AxtOaWydQ | $i$-Mix: A Domain-Agnostic Strategy for Contrastive Representation Learning | https://openreview.net/forum?id=T6AxtOaWydQ | [
"Kibok Lee",
"Yian Zhu",
"Kihyuk Sohn",
"Chun-Liang Li",
"Jinwoo Shin",
"Honglak Lee"
] | Poster | null | Contrastive representation learning has shown to be effective to learn representations from unlabeled data. However, much progress has been made in vision domains relying on data augmentations carefully designed using domain knowledge. In this work, we propose i-Mix, a simple yet effective domain-agnostic regularizatio... | [
"self-supervised learning",
"unsupervised representation learning",
"contrastive representation learning",
"data augmentation",
"MixUp"
] | null | 782 | 2010.08887 | title_snapshot |
bM4Iqfg8M2k | Graph Information Bottleneck for Subgraph Recognition | https://openreview.net/forum?id=bM4Iqfg8M2k | [
"Junchi Yu",
"Tingyang Xu",
"Yu Rong",
"Yatao Bian",
"Junzhou Huang",
"Ran He"
] | Poster | null | Given the input graph and its label/property, several key problems of graph learning, such as finding interpretable subgraphs, graph denoising and graph compression, can be attributed to the fundamental problem of recognizing a subgraph of the original one. This subgraph shall be as informative as possible, yet con... | [] | null | 779 | 2010.05563 | title_snapshot |
09-528y2Fgf | Rethinking Positional Encoding in Language Pre-training | https://openreview.net/forum?id=09-528y2Fgf | [
"Guolin Ke",
"Di He",
"Tie-Yan Liu"
] | Poster | null | In this work, we investigate the positional encoding methods used in language pre-training (e.g., BERT) and identify several problems in the existing formulations. First, we show that in the absolute positional encoding, the addition operation applied on positional embeddings and word embeddings brings mixed correlatio... | [
"Natural Language Processing",
"Pre-training"
] | null | 772 | 2006.15595 | title_snapshot |
6k7VdojAIK | Practical Massively Parallel Monte-Carlo Tree Search Applied to Molecular Design | https://openreview.net/forum?id=6k7VdojAIK | [
"Xiufeng Yang",
"Tanuj Aasawat",
"Kazuki Yoshizoe"
] | Poster | null | It is common practice to use large computational resources to train neural networks, known from many examples, such as reinforcement learning applications. However, while massively parallel computing is often used for training models, it is rarely used to search solutions for combinatorial optimization problems. This p... | [
"parallel Monte Carlo Tree Search (MCTS)",
"Upper Confidence bound applied to Trees (UCT)",
"molecular design"
] | null | 767 | 2006.10504 | title_snapshot |
kmG8vRXTFv | Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting | https://openreview.net/forum?id=kmG8vRXTFv | [
"Yuan Yin",
"Vincent LE GUEN",
"Jérémie DONA",
"Emmanuel de Bezenac",
"Ibrahim Ayed",
"Nicolas THOME",
"patrick gallinari"
] | Oral | null | Forecasting complex dynamical phenomena in settings where only partial knowledge of their dynamics is available is a prevalent problem across various scientific fields. While purely data-driven approaches are arguably insufficient in this context, standard physical modeling based approaches tend to be over-simplistic, ... | [
"spatio-temporal forecasting",
"deep learning",
"physics",
"differential equations",
"hybrid systems"
] | null | 761 | 2010.04456 | title_snapshot |
S724o4_WB3 | When does preconditioning help or hurt generalization? | https://openreview.net/forum?id=S724o4_WB3 | [
"Shun-ichi Amari",
"Jimmy Ba",
"Roger Baker Grosse",
"Xuechen Li",
"Atsushi Nitanda",
"Taiji Suzuki",
"Denny Wu",
"Ji Xu"
] | Poster | null | While second order optimizers such as natural gradient descent (NGD) often speed up optimization, their effect on generalization has been called into question. This work presents a more nuanced view on how the \textit{implicit bias} of optimizers affects the comparison of generalization properties.
We provide an exact... | [
"generalization",
"second-order optimization",
"natural gradient descent",
"high-dimensional asymptotics"
] | null | 755 | 2006.10732 | title_snapshot |
6puCSjH3hwA | A Good Image Generator Is What You Need for High-Resolution Video Synthesis | https://openreview.net/forum?id=6puCSjH3hwA | [
"Yu Tian",
"Jian Ren",
"Menglei Chai",
"Kyle Olszewski",
"Xi Peng",
"Dimitris N. Metaxas",
"Sergey Tulyakov"
] | Spotlight | null | Image and video synthesis are closely related areas aiming at generating content from noise. While rapid progress has been demonstrated in improving image-based models to handle large resolutions, high-quality renderings, and wide variations in image content, achieving comparable video generation results remains proble... | [
"high-resolution video generation",
"contrastive learning",
"cross-domain video generation"
] | null | 751 | 2104.15069 | title_snapshot |
JoCR4h9O3Ew | ARMOURED: Adversarially Robust MOdels using Unlabeled data by REgularizing Diversity | https://openreview.net/forum?id=JoCR4h9O3Ew | [
"Kangkang Lu",
"Cuong Manh Nguyen",
"Xun Xu",
"Kiran Krishnamachari",
"Yu Jing Goh",
"Chuan-Sheng Foo"
] | Poster | null | Adversarial attacks pose a major challenge for modern deep neural networks. Recent advancements show that adversarially robust generalization requires a large amount of labeled data for training. If annotation becomes a burden, can unlabeled data help bridge the gap? In this paper, we propose ARMOURED, an adversarially... | [
"Adversarial Robustness",
"Semi-supervised Learning",
"Multi-view Learning",
"Diversity Regularization",
"Entropy Maximization"
] | null | 745 | null | null |
aD1_5zowqV | Learning Energy-Based Generative Models via Coarse-to-Fine Expanding and Sampling | https://openreview.net/forum?id=aD1_5zowqV | [
"Yang Zhao",
"Jianwen Xie",
"Ping Li"
] | Poster | null | Energy-based models (EBMs) parameterized by neural networks can be trained by the Markov chain Monte Carlo (MCMC) sampling-based maximum likelihood estimation. Despite the recent significant success of EBMs in image generation, the current approaches to train EBMs are unstable and have difficulty synthesizing diverse a... | [
"Energy-based model",
"generative model",
"image translation",
"Langevin dynamics"
] | null | 742 | null | null |
0zvfm-nZqQs | Undistillable: Making A Nasty Teacher That CANNOT teach students | https://openreview.net/forum?id=0zvfm-nZqQs | [
"Haoyu Ma",
"Tianlong Chen",
"Ting-Kuei Hu",
"Chenyu You",
"Xiaohui Xie",
"Zhangyang Wang"
] | Spotlight | null | Knowledge Distillation (KD) is a widely used technique to transfer knowledge from pre-trained teacher models to (usually more lightweight) student models. However, in certain situations, this technique is more of a curse than a blessing. For instance, KD poses a potential risk of exposing intellectual properties (IPs)... | [
"knowledge distillation",
"avoid knowledge leaking"
] | null | 741 | 2105.07381 | title_snapshot |
U_mat0b9iv | Multi-Prize Lottery Ticket Hypothesis: Finding Accurate Binary Neural Networks by Pruning A Randomly Weighted Network | https://openreview.net/forum?id=U_mat0b9iv | [
"James Diffenderfer",
"Bhavya Kailkhura"
] | Poster | null | Recently, Frankle & Carbin (2019) demonstrated that randomly-initialized dense networks contain subnetworks that once found can be trained to reach test accuracy comparable to the trained dense network. However, finding these high performing trainable subnetworks is expensive, requiring iterative process of training an... | [
"Binary Neural Networks",
"Pruning",
"Lottery Ticket Hypothesis"
] | null | 738 | 2103.09377 | title_snapshot |
cTbIjyrUVwJ | Learning Accurate Entropy Model with Global Reference for Image Compression | https://openreview.net/forum?id=cTbIjyrUVwJ | [
"Yichen Qian",
"Zhiyu Tan",
"Xiuyu Sun",
"Ming Lin",
"Dongyang Li",
"Zhenhong Sun",
"Li Hao",
"Rong Jin"
] | Poster | null | In recent deep image compression neural networks, the entropy model plays a critical role in estimating the prior distribution of deep image encodings. Existing methods combine hyperprior with local context in the entropy estimation function. This greatly limits their performance due to the absence of a global vision. ... | [
"Image compression",
"Entropy Model",
"Global Reference"
] | null | 733 | 2010.08321 | title_snapshot |
te7PVH1sPxJ | Convex Potential Flows: Universal Probability Distributions with Optimal Transport and Convex Optimization | https://openreview.net/forum?id=te7PVH1sPxJ | [
"Chin-Wei Huang",
"Ricky T. Q. Chen",
"Christos Tsirigotis",
"Aaron Courville"
] | Poster | null | Flow-based models are powerful tools for designing probabilistic models with tractable density. This paper introduces Convex Potential Flows (CP-Flow), a natural and efficient parameterization of invertible models inspired by the optimal transport (OT) theory. CP-Flows are the gradient map of a strongly convex neural p... | [
"Normalizing flows",
"generative models",
"variational inference",
"invertible neural networks",
"universal approximation",
"optimal transport",
"convex optimization"
] | null | 727 | 2012.05942 | title_snapshot |
6t_dLShIUyZ | Greedy-GQ with Variance Reduction: Finite-time Analysis and Improved Complexity | https://openreview.net/forum?id=6t_dLShIUyZ | [
"Shaocong Ma",
"Ziyi Chen",
"Yi Zhou",
"Shaofeng Zou"
] | Poster | null | Greedy-GQ is a value-based reinforcement learning (RL) algorithm for optimal control. Recently, the finite-time analysis of Greedy-GQ has been developed under linear function approximation and Markovian sampling, and the algorithm is shown to achieve an $\epsilon$-stationary point with a sample complexity in the order ... | [
"Optimization",
"Reinforcement Learning",
"Machine Learning"
] | null | 725 | 2103.16377 | title_snapshot |
cP5IcoAkfKa | Large Batch Simulation for Deep Reinforcement Learning | https://openreview.net/forum?id=cP5IcoAkfKa | [
"Brennan Shacklett",
"Erik Wijmans",
"Aleksei Petrenko",
"Manolis Savva",
"Dhruv Batra",
"Vladlen Koltun",
"Kayvon Fatahalian"
] | Poster | null | We accelerate deep reinforcement learning-based training in visually complex 3D environments by two orders of magnitude over prior work, realizing end-to-end training speeds of over 19,000 frames of experience per second on a single GPU and up to 72,000 frames per second on a single eight-GPU machine. The key idea of o... | [
"reinforcement learning",
"simulation"
] | null | 721 | 2103.07013 | title_snapshot |
MaZFq7bJif7 | Hopper: Multi-hop Transformer for Spatiotemporal Reasoning | https://openreview.net/forum?id=MaZFq7bJif7 | [
"Honglu Zhou",
"Asim Kadav",
"Farley Lai",
"Alexandru Niculescu-Mizil",
"Martin Renqiang Min",
"Mubbasir Kapadia",
"Hans Peter Graf"
] | Poster | null | This paper considers the problem of spatiotemporal object-centric reasoning in videos. Central to our approach is the notion of object permanence, i.e., the ability to reason about the location of objects as they move through the video while being occluded, contained or carried by other objects. Existing deep learning ... | [
"Multi-hop Reasoning",
"Object Permanence",
"Spatiotemporal Understanding",
"Video Recognition",
"Transformer"
] | null | 719 | 2103.10574 | title_snapshot |
fmtSg8591Q | Efficient Reinforcement Learning in Factored MDPs with Application to Constrained RL | https://openreview.net/forum?id=fmtSg8591Q | [
"Xiaoyu Chen",
"Jiachen Hu",
"Lihong Li",
"Liwei Wang"
] | Poster | null | Reinforcement learning (RL) in episodic, factored Markov decision processes (FMDPs) is studied. We propose an algorithm called FMDP-BF, which leverages the factorization structure of FMDP. The regret of FMDP-BF is shown to be exponentially smaller than that of optimal algorithms designed for non-factored MDPs, and imp... | [
"reinforcement learning",
"factored MDP",
"constrained RL",
"learning theory"
] | null | 718 | 2008.13319 | title_snapshot |
MJIve1zgR_ | Unbiased Teacher for Semi-Supervised Object Detection | https://openreview.net/forum?id=MJIve1zgR_ | [
"Yen-Cheng Liu",
"Chih-Yao Ma",
"Zijian He",
"Chia-Wen Kuo",
"Kan Chen",
"Peizhao Zhang",
"Bichen Wu",
"Zsolt Kira",
"Peter Vajda"
] | Poster | null | Semi-supervised learning, i.e., training networks with both labeled and unlabeled data, has made significant progress recently. However, existing works have primarily focused on image classification tasks and neglected object detection which requires more annotation effort. In this work, we revisit the Semi-Supervised ... | [
"Object Detection"
] | null | 712 | 2102.09480 | title_snapshot |
0-uUGPbIjD | Human-Level Performance in No-Press Diplomacy via Equilibrium Search | https://openreview.net/forum?id=0-uUGPbIjD | [
"Jonathan Gray",
"Adam Lerer",
"Anton Bakhtin",
"Noam Brown"
] | Oral | null | Prior AI breakthroughs in complex games have focused on either the purely adversarial or purely cooperative settings. In contrast, Diplomacy is a game of shifting alliances that involves both cooperation and competition. For this reason, Diplomacy has proven to be a formidable research challenge. In this paper we descr... | [
"multi-agent systems",
"regret minimization",
"no-regret learning",
"game theory",
"reinforcement learning"
] | null | 702 | 2010.02923 | title_snapshot |
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