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Net-DNF: Effective Deep Modeling of Tabular Data | https://openreview.net/forum?id=73WTGs96kho | [
"Liran Katzir",
"Gal Elidan",
"Ran El-Yaniv"
] | Poster | null | A challenging open question in deep learning is how to handle tabular data. Unlike domains such as image and natural language processing, where deep architectures prevail, there is still no widely accepted neural architecture that dominates tabular data. As a step toward bridging this gap, we present Net-DNF a novel ge... | [
"Neural Networks",
"Architectures",
"Tabular Data",
"Predictive Modeling"
] | null | 1,574 | null | null | [
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Predicting Inductive Biases of Pre-Trained Models | https://openreview.net/forum?id=mNtmhaDkAr | [
"Charles Lovering",
"Rohan Jha",
"Tal Linzen",
"Ellie Pavlick"
] | Poster | null | Most current NLP systems are based on a pre-train-then-fine-tune paradigm, in which a large neural network is first trained in a self-supervised way designed to encourage the network to extract broadly-useful linguistic features, and then fine-tuned for a specific task of interest. Recent work attempts to understand wh... | [
"information-theoretical probing",
"probing",
"challenge sets",
"natural language processing"
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Optimism in Reinforcement Learning with Generalized Linear Function Approximation | https://openreview.net/forum?id=CBmJwzneppz | [
"Yining Wang",
"Ruosong Wang",
"Simon Shaolei Du",
"Akshay Krishnamurthy"
] | Poster | null | We design a new provably efficient algorithm for episodic reinforcement learning with generalized linear function approximation. We analyze the algorithm under a new expressivity assumption that we call ``optimistic closure,'' which is strictly weaker than assumptions from prior analyses for the linear setting. With op... | [
"reinforcement learning",
"optimism",
"exploration",
"function approximation",
"theory",
"regret analysis",
"provable sample efficiency"
] | null | 3,820 | 1912.04136 | title_snapshot | [
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SCoRe: Pre-Training for Context Representation in Conversational Semantic Parsing | https://openreview.net/forum?id=oyZxhRI2RiE | [
"Tao Yu",
"Rui Zhang",
"Alex Polozov",
"Christopher Meek",
"Ahmed Hassan Awadallah"
] | Poster | null | Conversational Semantic Parsing (CSP) is the task of converting a sequence of natural language queries to formal language (e.g., SQL, SPARQL) that can be executed against a structured ontology (e.g. databases, knowledge bases). To accomplish this task, a CSP system needs to model the relation between the ... | [] | null | 3,773 | null | null | [
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A teacher-student framework to distill future trajectories | https://openreview.net/forum?id=ECuvULjFQia | [
"Alexander Neitz",
"Giambattista Parascandolo",
"Bernhard Schölkopf"
] | Poster | null | By learning to predict trajectories of dynamical systems, model-based methods can make extensive use of all observations from past experience. However, due to partial observability, stochasticity, compounding errors, and irrelevant dynamics, training to predict observations explicitly often results in poor models. Mode... | [
"meta-learning",
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Certify or Predict: Boosting Certified Robustness with Compositional Architectures | https://openreview.net/forum?id=USCNapootw | [
"Mark Niklas Mueller",
"Mislav Balunovic",
"Martin Vechev"
] | Poster | null | A core challenge with existing certified defense mechanisms is that while they improve certified robustness, they also tend to drastically decrease natural accuracy, making it difficult to use these methods in practice. In this work, we propose a new architecture which addresses this challenge and enables one to boost ... | [
"Provable Robustness",
"Network Architecture",
"Robustness",
"Adversarial Accuracy",
"Certified Robustness"
] | null | 3,751 | null | null | [
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On the Transfer of Disentangled Representations in Realistic Settings | https://openreview.net/forum?id=8VXvj1QNRl1 | [
"Andrea Dittadi",
"Frederik Träuble",
"Francesco Locatello",
"Manuel Wuthrich",
"Vaibhav Agrawal",
"Ole Winther",
"Stefan Bauer",
"Bernhard Schölkopf"
] | Poster | null | Learning meaningful representations that disentangle the underlying structure of the data generating process is considered to be of key importance in machine learning. While disentangled representations were found to be useful for diverse tasks such as abstract reasoning and fair classification, their scalability and r... | [
"representation learning",
"disentanglement",
"real-world"
] | null | 3,746 | 2010.14407 | title_snapshot | [
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Robust Reinforcement Learning on State Observations with Learned Optimal Adversary | https://openreview.net/forum?id=sCZbhBvqQaU | [
"Huan Zhang",
"Hongge Chen",
"Duane S Boning",
"Cho-Jui Hsieh"
] | Poster | null | We study the robustness of reinforcement learning (RL) with adversarially perturbed state observations, which aligns with the setting of many adversarial attacks to deep reinforcement learning (DRL) and is also important for rolling out real-world RL agent under unpredictable sensing noise. With a fixed agent policy, w... | [
"reinforcement learning",
"robustness",
"adversarial attacks",
"adversarial defense"
] | null | 3,741 | 2101.08452 | title_snapshot | [
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Practical Real Time Recurrent Learning with a Sparse Approximation | https://openreview.net/forum?id=q3KSThy2GwB | [
"Jacob Menick",
"Erich Elsen",
"Utku Evci",
"Simon Osindero",
"Karen Simonyan",
"Alex Graves"
] | Spotlight | null | Recurrent neural networks are usually trained with backpropagation through time, which requires storing a complete history of network states, and prohibits updating the weights "online" (after every timestep). Real Time Recurrent Learning (RTRL) eliminates the need for history storage and allows for online weight updat... | [
"recurrent neural networks",
"backpropagation",
"biologically plausible",
"forward mode",
"real time recurrent learning",
"rtrl",
"bptt"
] | null | 3,731 | 2006.07232 | title_judge | [
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Retrieval-Augmented Generation for Code Summarization via Hybrid GNN | https://openreview.net/forum?id=zv-typ1gPxA | [
"Shangqing Liu",
"Yu Chen",
"Xiaofei Xie",
"Jing Kai Siow",
"Yang Liu"
] | Spotlight | null | Source code summarization aims to generate natural language summaries from structured code snippets for better understanding code functionalities. However, automatic code summarization is challenging due to the complexity of the source code and the language gap between the source code and natural language summaries. Mo... | [
"Code Summarization",
"Graph Neural Network",
"Retrieval",
"Generation"
] | null | 3,719 | 2006.05405 | title_snapshot | [
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Learning from others' mistakes: Avoiding dataset biases without modeling them | https://openreview.net/forum?id=Hf3qXoiNkR | [
"Victor Sanh",
"Thomas Wolf",
"Yonatan Belinkov",
"Alexander M Rush"
] | Poster | null | State-of-the-art natural language processing (NLP) models often learn to model dataset biases and surface form correlations instead of features that target the intended underlying task. Previous work has demonstrated effective methods to circumvent these issues when knowledge of the bias is available. We consider cases... | [
"dataset bias",
"product of experts",
"natural language processing"
] | null | 3,718 | 2012.01300 | title_snapshot | [
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Fast and Complete: Enabling Complete Neural Network Verification with Rapid and Massively Parallel Incomplete Verifiers | https://openreview.net/forum?id=nVZtXBI6LNn | [
"Kaidi Xu",
"Huan Zhang",
"Shiqi Wang",
"Yihan Wang",
"Suman Jana",
"Xue Lin",
"Cho-Jui Hsieh"
] | Poster | null | Formal verification of neural networks (NNs) is a challenging and important problem. Existing efficient complete solvers typically require the branch-and-bound (BaB) process, which splits the problem domain into sub-domains and solves each sub-domain using faster but weaker incomplete verifiers, such as Linear Programm... | [
"neural network verification",
"branch and bound"
] | null | 3,717 | 2011.13824 | title_snapshot | [
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Self-supervised Adversarial Robustness for the Low-label, High-data Regime | https://openreview.net/forum?id=bgQek2O63w | [
"Sven Gowal",
"Po-Sen Huang",
"Aaron van den Oord",
"Timothy Mann",
"Pushmeet Kohli"
] | Poster | null | Recent work discovered that training models to be invariant to adversarial perturbations requires substantially larger datasets than those required for standard classification. Perhaps more surprisingly, these larger datasets can be "mostly" unlabeled. Pseudo-labeling, a technique simultaneously pioneered by four separ... | [
"self-supervised",
"adversarial training",
"robustness"
] | null | 3,713 | null | null | [
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Modeling the Second Player in Distributionally Robust Optimization | https://openreview.net/forum?id=ZDnzZrTqU9N | [
"Paul Michel",
"Tatsunori Hashimoto",
"Graham Neubig"
] | Poster | null | Distributionally robust optimization (DRO) provides a framework for training machine learning models that are able to perform well on a collection of related data distributions (the "uncertainty set"). This is done by solving a min-max game: the model is trained to minimize its maximum expected loss among all distribut... | [
"distributionally robust optimization",
"deep learning",
"robustness",
"adversarial learning"
] | null | 3,696 | 2103.10282 | title_snapshot | [
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Neural Jump Ordinary Differential Equations: Consistent Continuous-Time Prediction and Filtering | https://openreview.net/forum?id=JFKR3WqwyXR | [
"Calypso Herrera",
"Florian Krach",
"Josef Teichmann"
] | Poster | null | Combinations of neural ODEs with recurrent neural networks (RNN), like GRU-ODE-Bayes or ODE-RNN are well suited to model irregularly observed time series. While those models outperform existing discrete-time approaches, no theoretical guarantees for their predictive capabilities are available. Assuming that the irregul... | [
"Neural ODE",
"conditional expectation",
"irregular-observed data modelling"
] | null | 3,695 | 2006.04727 | title_snapshot | [
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Gradient Origin Networks | https://openreview.net/forum?id=0O_cQfw6uEh | [
"Sam Bond-Taylor",
"Chris G. Willcocks"
] | Poster | null | This paper proposes a new type of generative model that is able to quickly learn a latent representation without an encoder. This is achieved using empirical Bayes to calculate the expectation of the posterior, which is implemented by initialising a latent vector with zeros, then using the gradient of the log-likelihoo... | [
"Deep Learning",
"Generative Models",
"Implicit Representation"
] | null | 3,694 | 2007.02798 | title_snapshot | [
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On the mapping between Hopfield networks and Restricted Boltzmann Machines | https://openreview.net/forum?id=RGJbergVIoO | [
"Matthew Smart",
"Anton Zilman"
] | Oral | null | Hopfield networks (HNs) and Restricted Boltzmann Machines (RBMs) are two important models at the interface of statistical physics, machine learning, and neuroscience. Recently, there has been interest in the relationship between HNs and RBMs, due to their similarity under the statistical mechanics formalism. An exact m... | [
"Hopfield Networks",
"Restricted Boltzmann Machines",
"Statistical Physics"
] | null | 3,693 | 2101.11744 | title_snapshot | [
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Efficient Generalized Spherical CNNs | https://openreview.net/forum?id=rWZz3sJfCkm | [
"Oliver Cobb",
"Christopher G. R. Wallis",
"Augustine N. Mavor-Parker",
"Augustin Marignier",
"Matthew A. Price",
"Mayeul d'Avezac",
"Jason McEwen"
] | Poster | null | Many problems across computer vision and the natural sciences require the analysis of spherical data, for which representations may be learned efficiently by encoding equivariance to rotational symmetries. We present a generalized spherical CNN framework that encompasses various existing approaches and allows them to ... | [] | null | 3,692 | 2010.11661 | title_snapshot | [
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DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION | https://openreview.net/forum?id=XPZIaotutsD | [
"Pengcheng He",
"Xiaodong Liu",
"Jianfeng Gao",
"Weizhu Chen"
] | Poster | null | Recent progress in pre-trained neural language models has significantly improved the performance of many natural language processing (NLP) tasks. In this paper we propose a new model architecture DeBERTa (Decoding-enhanced BERT with disentangled attention) that improves the BERT and RoBERTa models using two novel techn... | [
"Transformer",
"Attention",
"Natural Language Processing",
"Language Model Pre-training",
"Position Encoding"
] | null | 3,690 | 2006.03654 | title_snapshot | [
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Optimizing Memory Placement using Evolutionary Graph Reinforcement Learning | https://openreview.net/forum?id=-6vS_4Kfz0 | [
"Shauharda Khadka",
"Estelle Aflalo",
"Mattias Marder",
"Avrech Ben-David",
"Santiago Miret",
"Shie Mannor",
"Tamir Hazan",
"Hanlin Tang",
"Somdeb Majumdar"
] | Poster | null | For deep neural network accelerators, memory movement is both energetically expensive and can bound computation. Therefore, optimal mapping of tensors to memory hierarchies is critical to performance. The growing complexity of neural networks calls for automated memory mapping instead of manual heuristic approaches; ye... | [
"Reinforcement Learning",
"Memory Mapping",
"Device Placement",
"Evolutionary Algorithms"
] | null | 3,688 | 2007.07298 | title_snapshot | [
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On the geometry of generalization and memorization in deep neural networks | https://openreview.net/forum?id=V8jrrnwGbuc | [
"Cory Stephenson",
"suchismita padhy",
"Abhinav Ganesh",
"Yue Hui",
"Hanlin Tang",
"SueYeon Chung"
] | Poster | null | Understanding how large neural networks avoid memorizing training data is key to explaining their high generalization performance. To examine the structure of when and where memorization occurs in a deep network, we use a recently developed replica-based mean field theoretic geometric analysis method. We find that all ... | [
"deep learning theory",
"representation learning",
"statistical physics methods",
"double descent"
] | null | 3,668 | 2105.14602 | title_snapshot | [
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Continual learning in recurrent neural networks | https://openreview.net/forum?id=8xeBUgD8u9 | [
"Benjamin Ehret",
"Christian Henning",
"Maria Cervera",
"Alexander Meulemans",
"Johannes von Oswald",
"Benjamin F Grewe"
] | Poster | null | While a diverse collection of continual learning (CL) methods has been proposed to prevent catastrophic forgetting, a thorough investigation of their effectiveness for processing sequential data with recurrent neural networks (RNNs) is lacking. Here, we provide the first comprehensive evaluation of established CL metho... | [
"Recurrent Neural Networks",
"Continual Learning"
] | null | 3,663 | 2006.12109 | title_snapshot | [
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Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching | https://openreview.net/forum?id=01olnfLIbD | [
"Jonas Geiping",
"Liam H Fowl",
"W. Ronny Huang",
"Wojciech Czaja",
"Gavin Taylor",
"Michael Moeller",
"Tom Goldstein"
] | Poster | null | Data Poisoning attacks modify training data to maliciously control a model trained on such data.
In this work, we focus on targeted poisoning attacks which cause a reclassification of an unmodified test image and as such breach model integrity. We consider a
particularly malicious poisoning attack that is both ``from s... | [
"Data Poisoning",
"ImageNet",
"Large-scale",
"Gradient Alignment",
"Security",
"Backdoor Attacks",
"from-scratch",
"clean-label"
] | null | 3,659 | 2009.02276 | title_snapshot | [
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Overfitting for Fun and Profit: Instance-Adaptive Data Compression | https://openreview.net/forum?id=oFp8Mx_V5FL | [
"Ties van Rozendaal",
"Iris AM Huijben",
"Taco Cohen"
] | Poster | null | Neural data compression has been shown to outperform classical methods in terms of $RD$ performance, with results still improving rapidly.
At a high level, neural compression is based on an autoencoder that tries to reconstruct the input instance from a (quantized) latent representation, coupled with a prior that is us... | [
"Neural data compression",
"Learned compression",
"Generative modeling",
"Overfitting",
"Finetuning",
"Instance learning",
"Instance adaptation",
"Variational autoencoders",
"Rate-distortion optimization",
"Model compression",
"Weight quantization"
] | null | 3,658 | 2101.08687 | title_snapshot | [
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A Block Minifloat Representation for Training Deep Neural Networks | https://openreview.net/forum?id=6zaTwpNSsQ2 | [
"Sean Fox",
"Seyedramin Rasoulinezhad",
"Julian Faraone",
"david boland",
"Philip Leong"
] | Poster | null | Training Deep Neural Networks (DNN) with high efficiency can be difficult to achieve with native floating-point representations and commercially available hardware. Specialized arithmetic with custom acceleration offers perhaps the most promising alternative. Ongoing research is trending towards narrow floating-point r... | [] | null | 3,654 | null | null | [
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Representation Learning via Invariant Causal Mechanisms | https://openreview.net/forum?id=9p2ekP904Rs | [
"Jovana Mitrovic",
"Brian McWilliams",
"Jacob C Walker",
"Lars Holger Buesing",
"Charles Blundell"
] | Poster | null | Self-supervised learning has emerged as a strategy to reduce the reliance on costly supervised signal by pretraining representations only using unlabeled data. These methods combine heuristic proxy classification tasks with data augmentations and have achieved significant success, but our theoretical understanding of t... | [
"Representation Learning",
"Self-supervised Learning",
"Contrastive Methods",
"Causality"
] | null | 3,652 | 2010.07922 | title_snapshot | [
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Sparse encoding for more-interpretable feature-selecting representations in probabilistic matrix factorization | https://openreview.net/forum?id=D_KeYoqCYC | [
"Joshua C Chang",
"Patrick Fletcher",
"Jungmin Han",
"Ted L Chang",
"Shashaank Vattikuti",
"Bart Desmet",
"Ayah Zirikly",
"Carson C Chow"
] | Poster | null | Dimensionality reduction methods for count data are critical to a wide range of applications in medical informatics and other fields where model interpretability is paramount. For such data, hierarchical Poisson matrix factorization (HPF) and other sparse probabilistic non-negative matrix factorization (NMF) methods ar... | [
"poisson matrix factorization",
"generalized additive model",
"probabilistic matrix factorization",
"bayesian",
"sparse coding",
"interpretability",
"factor analysis"
] | null | 3,647 | 2012.04171 | title_snapshot | [
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Mapping the Timescale Organization of Neural Language Models | https://openreview.net/forum?id=J3OUycKwz- | [
"Hsiang-Yun Sherry Chien",
"Jinhan Zhang",
"Christopher Honey"
] | Poster | null | In the human brain, sequences of language input are processed within a distributed and hierarchical architecture, in which higher stages of processing encode contextual information over longer timescales. In contrast, in recurrent neural networks which perform natural language processing, we know little about how the m... | [
"natural language processing",
"LSTM",
"timescale",
"hierarchy",
"temporal context"
] | null | 3,640 | 2012.06717 | title_snapshot | [
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Neural networks with late-phase weights | https://openreview.net/forum?id=C0qJUx5dxFb | [
"Johannes von Oswald",
"Seijin Kobayashi",
"Joao Sacramento",
"Alexander Meulemans",
"Christian Henning",
"Benjamin F Grewe"
] | Poster | null | The largely successful method of training neural networks is to learn their weights using some variant of stochastic gradient descent (SGD). Here, we show that the solutions found by SGD can be further improved by ensembling a subset of the weights in late stages of learning. At the end of learning, we obtain back a si... | [] | null | 3,621 | 2007.12927 | title_snapshot | [
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Uncertainty-aware Active Learning for Optimal Bayesian Classifier | https://openreview.net/forum?id=Mu2ZxFctAI | [
"Guang Zhao",
"Edward Dougherty",
"Byung-Jun Yoon",
"Francis Alexander",
"Xiaoning Qian"
] | Poster | null | For pool-based active learning, in each iteration a candidate training sample is chosen for labeling by optimizing an acquisition function. In Bayesian classification, expected Loss Reduction~(ELR) methods maximize the expected reduction in the classification error given a new labeled candidate based on a one-step-look... | [
"Active learning",
"Bayesian classification"
] | null | 3,616 | null | null | [
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ResNet After All: Neural ODEs and Their Numerical Solution | https://openreview.net/forum?id=HxzSxSxLOJZ | [
"Katharina Ott",
"Prateek Katiyar",
"Philipp Hennig",
"Michael Tiemann"
] | Poster | null | A key appeal of the recently proposed Neural Ordinary Differential Equation (ODE) framework is that it seems to provide a continuous-time extension of discrete residual neural networks.
As we show herein, though, trained Neural ODE models actually depend on the specific numerical method used during training.
If the tr... | [] | null | 3,615 | 2007.15386 | title_snapshot | [
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Benefit of deep learning with non-convex noisy gradient descent: Provable excess risk bound and superiority to kernel methods | https://openreview.net/forum?id=2m0g1wEafh | [
"Taiji Suzuki",
"Shunta Akiyama"
] | Spotlight | null | Establishing a theoretical analysis that explains why deep learning can outperform shallow learning such as kernel methods is one of the biggest issues in the deep learning literature. Towards answering this question, we evaluate excess risk of a deep learning estimator trained by a noisy gradient descent with ridge re... | [
"Excess risk",
"minimax optimal rate",
"local Rademacher complexity",
"fast learning rate",
"kernel method",
"linear estimator"
] | null | 3,614 | 2012.03224 | title_snapshot | [
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Generalized Variational Continual Learning | https://openreview.net/forum?id=_IM-AfFhna9 | [
"Noel Loo",
"Siddharth Swaroop",
"Richard E Turner"
] | Poster | null | Continual learning deals with training models on new tasks and datasets in an online fashion. One strand of research has used probabilistic regularization for continual learning, with two of the main approaches in this vein being Online Elastic Weight Consolidation (Online EWC) and Variational Continual Learning (VCL).... | [] | null | 3,595 | 2011.12328 | title_snapshot | [
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Mind the Gap when Conditioning Amortised Inference in Sequential Latent-Variable Models | https://openreview.net/forum?id=a2gqxKDvYys | [
"Justin Bayer",
"Maximilian Soelch",
"Atanas Mirchev",
"Baris Kayalibay",
"Patrick van der Smagt"
] | Poster | null | Amortised inference enables scalable learning of sequential latent-variable models (LVMs) with the evidence lower bound (ELBO). In this setting, variational posteriors are often only partially conditioned. While the true posteriors depend, e.g., on the entire sequence of observations, approximate posteriors are only in... | [
"variational inference",
"state-space models",
"amortized inference",
"recurrent networks"
] | null | 3,587 | 2101.07046 | title_snapshot | [
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CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning | https://openreview.net/forum?id=SK7A5pdrgov | [
"Ossama Ahmed",
"Frederik Träuble",
"Anirudh Goyal",
"Alexander Neitz",
"Manuel Wuthrich",
"Yoshua Bengio",
"Bernhard Schölkopf",
"Stefan Bauer"
] | Poster | null | Despite recent successes of reinforcement learning (RL), it remains a challenge for agents to transfer learned skills to related environments. To facilitate research addressing this problem, we proposeCausalWorld, a benchmark for causal structure and transfer learning in a robotic manipulation environment. The environm... | [
"reinforcement learning",
"transfer learning",
"sim2real transfer",
"domain adaptation",
"causality",
"generalization",
"robotics"
] | null | 3,586 | 2010.04296 | title_snapshot | [
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Transformer protein language models are unsupervised structure learners | https://openreview.net/forum?id=fylclEqgvgd | [
"Roshan Rao",
"Joshua Meier",
"Tom Sercu",
"Sergey Ovchinnikov",
"Alexander Rives"
] | Poster | null | Unsupervised contact prediction is central to uncovering physical, structural, and functional constraints for protein structure determination and design. For decades, the predominant approach has been to infer evolutionary constraints from a set of related sequences. In the past year, protein language models have emerg... | [
"proteins",
"language modeling",
"structure prediction",
"unsupervised learning",
"explainable"
] | null | 3,581 | null | null | [
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Neural ODE Processes | https://openreview.net/forum?id=27acGyyI1BY | [
"Alexander Norcliffe",
"Cristian Bodnar",
"Ben Day",
"Jacob Moss",
"Pietro Liò"
] | Poster | null | Neural Ordinary Differential Equations (NODEs) use a neural network to model the instantaneous rate of change in the state of a system. However, despite their apparent suitability for dynamics-governed time-series, NODEs present a few disadvantages. First, they are unable to adapt to incoming data-points, a fundamental... | [
"differential equations",
"neural processes",
"dynamics",
"deep learning",
"neural ode"
] | null | 3,579 | 2103.12413 | title_snapshot | [
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The role of Disentanglement in Generalisation | https://openreview.net/forum?id=qbH974jKUVy | [
"Milton Llera Montero",
"Casimir JH Ludwig",
"Rui Ponte Costa",
"Gaurav Malhotra",
"Jeffrey Bowers"
] | Poster | null | Combinatorial generalisation — the ability to understand and produce novel combinations of familiar elements — is a core capacity of human intelligence that current AI systems struggle with. Recently, it has been suggested that learning disentangled representations may help address this problem. It is claimed that such... | [
"disentanglement",
"compositionality",
"compositional generalization",
"generalisation",
"generative models",
"variational autoencoders"
] | null | 3,567 | null | null | [
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Learning to live with Dale's principle: ANNs with separate excitatory and inhibitory units | https://openreview.net/forum?id=eU776ZYxEpz | [
"Jonathan Cornford",
"Damjan Kalajdzievski",
"Marco Leite",
"Amélie Lamarquette",
"Dimitri Michael Kullmann",
"Blake Aaron Richards"
] | Poster | null | The units in artificial neural networks (ANNs) can be thought of as abstractions of biological neurons, and ANNs are increasingly used in neuroscience research. However, there are many important differences between ANN units and real neurons. One of the most notable is the absence of Dale's principle, which ensures th... | [] | null | 3,565 | null | null | [
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SALD: Sign Agnostic Learning with Derivatives | https://openreview.net/forum?id=7EDgLu9reQD | [
"Matan Atzmon",
"Yaron Lipman"
] | Poster | null | Learning 3D geometry directly from raw data, such as point clouds, triangle soups, or unoriented meshes is still a challenging task that feeds many downstream computer vision and graphics applications.
In this paper, we introduce SALD: a method for learning implicit neural representations of shapes directly from raw ... | [
"implicit neural representations",
"3D shapes learning",
"sign agnostic learning"
] | null | 3,563 | 2006.05400 | title_snapshot | [
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Ringing ReLUs: Harmonic Distortion Analysis of Nonlinear Feedforward Networks | https://openreview.net/forum?id=TaYhv-q1Xit | [
"Christian H.X. Ali Mehmeti-Göpel",
"David Hartmann",
"Michael Wand"
] | Poster | null | In this paper, we apply harmonic distortion analysis to understand the effect of nonlinearities in the spectral domain. Each nonlinear layer creates higher-frequency harmonics, which we call "blueshift", whose magnitude increases with network depth, thereby increasing the “roughness” of the output landscape. Unlike dif... | [
"deep learning theory",
"loss landscape",
"harmonic distortion analysis",
"network trainability"
] | null | 3,561 | null | null | [
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CoCon: A Self-Supervised Approach for Controlled Text Generation | https://openreview.net/forum?id=VD_ozqvBy4W | [
"Alvin Chan",
"Yew-Soon Ong",
"Bill Pung",
"Aston Zhang",
"Jie Fu"
] | Poster | null | Pretrained Transformer-based language models (LMs) display remarkable natural language generation capabilities. With their immense potential, controlling text generation of such LMs is getting attention. While there are studies that seek to control high-level attributes (such as sentiment and topic) of generated text, ... | [
"Language modeling",
"text generation",
"controlled generation",
"self-supervised learning"
] | null | 3,544 | 2006.03535 | title_snapshot | [
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Bidirectional Variational Inference for Non-Autoregressive Text-to-Speech | https://openreview.net/forum?id=o3iritJHLfO | [
"Yoonhyung Lee",
"Joongbo Shin",
"Kyomin Jung"
] | Poster | null | Although early text-to-speech (TTS) models such as Tacotron 2 have succeeded in generating human-like speech, their autoregressive architectures have several limitations: (1) They require a lot of time to generate a mel-spectrogram consisting of hundreds of steps. (2) The autoregressive speech generation shows a lack o... | [
"text-to-speech",
"speech synthesis",
"non-autoregressive",
"VAE"
] | null | 3,537 | null | null | [
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Learning continuous-time PDEs from sparse data with graph neural networks | https://openreview.net/forum?id=aUX5Plaq7Oy | [
"Valerii Iakovlev",
"Markus Heinonen",
"Harri Lähdesmäki"
] | Poster | null | The behavior of many dynamical systems follow complex, yet still unknown partial differential equations (PDEs). While several machine learning methods have been proposed to learn PDEs directly from data, previous methods are limited to discrete-time approximations or make the limiting assumption of the observations arr... | [
"dynamical systems",
"partial differential equations",
"PDEs",
"graph neural networks",
"continuous time"
] | null | 3,532 | 2006.08956 | title_snapshot | [
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NAS-Bench-ASR: Reproducible Neural Architecture Search for Speech Recognition | https://openreview.net/forum?id=CU0APx9LMaL | [
"Abhinav Mehrotra",
"Alberto Gil C. P. Ramos",
"Sourav Bhattacharya",
"Łukasz Dudziak",
"Ravichander Vipperla",
"Thomas Chau",
"Mohamed S Abdelfattah",
"Samin Ishtiaq",
"Nicholas Donald Lane"
] | Poster | null | Powered by innovations in novel architecture design, noise tolerance techniques and increasing model capacity, Automatic Speech Recognition (ASR) has made giant strides in reducing word-error-rate over the past decade. ASR models are often trained with tens of thousand hours of high quality speech data to produce state... | [
"NAS",
"ASR",
"Benchmark"
] | null | 3,528 | null | null | [
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Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks | https://openreview.net/forum?id=ULQdiUTHe3y | [
"Jan Schuchardt",
"Aleksandar Bojchevski",
"Johannes Gasteiger",
"Stephan Günnemann"
] | Poster | null | In tasks like node classification, image segmentation, and named-entity recognition we have a classifier that simultaneously outputs multiple predictions (a vector of labels) based on a single input, i.e. a single graph, image, or document respectively. Existing adversarial robustness certificates consider each predict... | [
"Robustness certificates",
"Adversarial robustness",
"Graph neural networks"
] | null | 3,522 | 2302.02829 | title_snapshot | [
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Adversarially Guided Actor-Critic | https://openreview.net/forum?id=_mQp5cr_iNy | [
"Yannis Flet-Berliac",
"Johan Ferret",
"Olivier Pietquin",
"Philippe Preux",
"Matthieu Geist"
] | Poster | null | Despite definite success in deep reinforcement learning problems, actor-critic algorithms are still confronted with sample inefficiency in complex environments, particularly in tasks where efficient exploration is a bottleneck. These methods consider a policy (the actor) and a value function (the critic) whose respecti... | [] | null | 3,504 | 2102.04376 | title_snapshot | [
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Training independent subnetworks for robust prediction | https://openreview.net/forum?id=OGg9XnKxFAH | [
"Marton Havasi",
"Rodolphe Jenatton",
"Stanislav Fort",
"Jeremiah Zhe Liu",
"Jasper Snoek",
"Balaji Lakshminarayanan",
"Andrew Mingbo Dai",
"Dustin Tran"
] | Poster | null | Recent approaches to efficiently ensemble neural networks have shown that strong robustness and uncertainty performance can be achieved with a negligible gain in parameters over the original network. However, these methods still require multiple forward passes for prediction, leading to a significant runtime cost. In ... | [
"Efficient ensembles",
"robustness"
] | null | 3,498 | 2010.06610 | title_snapshot | [
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Complex Query Answering with Neural Link Predictors | https://openreview.net/forum?id=Mos9F9kDwkz | [
"Erik Arakelyan",
"Daniel Daza",
"Pasquale Minervini",
"Michael Cochez"
] | Oral | null | Neural link predictors are immensely useful for identifying missing edges in large scale Knowledge Graphs. However, it is still not clear how to use these models for answering more complex queries that arise in a number of domains, such as queries using logical conjunctions ($\land$), disjunctions ($\lor$) and existent... | [
"neural link prediction",
"complex query answering"
] | null | 3,496 | 2011.03459 | title_snapshot | [
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Grounding Language to Autonomously-Acquired Skills via Goal Generation | https://openreview.net/forum?id=chPj_I5KMHG | [
"Ahmed Akakzia",
"Cédric Colas",
"Pierre-Yves Oudeyer",
"Mohamed CHETOUANI",
"Olivier Sigaud"
] | Poster | null | We are interested in the autonomous acquisition of repertoires of skills. Language-conditioned reinforcement learning (LC-RL) approaches are great tools in this quest, as they allow to express abstract goals as sets of constraints on the states. However, most LC-RL agents are not autonomous and cannot learn without ext... | [
"Deep reinforcement learning",
"intrinsic motivations",
"symbolic representations",
"autonomous learning"
] | null | 3,493 | 2006.07185 | title_snapshot | [
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Hopfield Networks is All You Need | https://openreview.net/forum?id=tL89RnzIiCd | [
"Hubert Ramsauer",
"Bernhard Schäfl",
"Johannes Lehner",
"Philipp Seidl",
"Michael Widrich",
"Lukas Gruber",
"Markus Holzleitner",
"Thomas Adler",
"David Kreil",
"Michael K Kopp",
"Günter Klambauer",
"Johannes Brandstetter",
"Sepp Hochreiter"
] | Poster | null | We introduce a modern Hopfield network with continuous states and a corresponding update rule. The new Hopfield network can store exponentially (with the dimension of the associative space) many patterns, retrieves the pattern with one update, and has exponentially small retrieval errors. It has three types of energy m... | [
"Modern Hopfield Network",
"Energy",
"Attention",
"Convergence",
"Storage Capacity",
"Hopfield layer",
"Associative Memory"
] | null | 3,489 | 2008.02217 | title_snapshot | [
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Differentiable Trust Region Layers for Deep Reinforcement Learning | https://openreview.net/forum?id=qYZD-AO1Vn | [
"Fabian Otto",
"Philipp Becker",
"Vien Anh Ngo",
"Hanna Carolin Maria Ziesche",
"Gerhard Neumann"
] | Poster | null | Trust region methods are a popular tool in reinforcement learning as they yield robust policy updates in continuous and discrete action spaces. However, enforcing such trust regions in deep reinforcement learning is difficult. Hence, many approaches, such as Trust Region Policy Optimization (TRPO) and Proximal Policy O... | [
"reinforcement learning",
"trust region",
"policy gradient",
"projection",
"Wasserstein distance",
"Kullback-Leibler divergence",
"Frobenius norm"
] | null | 3,480 | 2101.09207 | title_snapshot | [
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Self-supervised Visual Reinforcement Learning with Object-centric Representations | https://openreview.net/forum?id=xppLmXCbOw1 | [
"Andrii Zadaianchuk",
"Maximilian Seitzer",
"Georg Martius"
] | Spotlight | null | Autonomous agents need large repertoires of skills to act reasonably on new tasks that they have not seen before. However, acquiring these skills using only a stream of high-dimensional, unstructured, and unlabeled observations is a tricky challenge for any autonomous agent. Previous methods have used variational autoe... | [
"self-supervision",
"autonomous learning",
"object-centric representations",
"visual reinforcement learning"
] | null | 3,479 | 2011.14381 | title_snapshot | [
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Temporally-Extended ε-Greedy Exploration | https://openreview.net/forum?id=ONBPHFZ7zG4 | [
"Will Dabney",
"Georg Ostrovski",
"Andre Barreto"
] | Poster | null | Recent work on exploration in reinforcement learning (RL) has led to a series of increasingly complex solutions to the problem. This increase in complexity often comes at the expense of generality. Recent empirical studies suggest that, when applied to a broader set of domains, some sophisticated exploration methods ar... | [
"reinforcement learning",
"exploration"
] | null | 3,475 | 2006.01782 | title_snapshot | [
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Learning Associative Inference Using Fast Weight Memory | https://openreview.net/forum?id=TuK6agbdt27 | [
"Imanol Schlag",
"Tsendsuren Munkhdalai",
"Jürgen Schmidhuber"
] | Poster | null | Humans can quickly associate stimuli to solve problems in novel contexts. Our novel neural network model learns state representations of facts that can be composed to perform such associative inference. To this end, we augment the LSTM model with an associative memory, dubbed \textit{Fast Weight Memory} (FWM). Through ... | [
"memory-augmented neural networks",
"tensor product",
"fast weights"
] | null | 3,467 | 2011.07831 | title_snapshot | [
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Multiscale Score Matching for Out-of-Distribution Detection | https://openreview.net/forum?id=xoHdgbQJohv | [
"Ahsan Mahmood",
"Junier Oliva",
"Martin Andreas Styner"
] | Poster | null | We present a new methodology for detecting out-of-distribution (OOD) images by utilizing norms of the score estimates at multiple noise scales. A score is defined to be the gradient of the log density with respect to the input data. Our methodology is completely unsupervised and follows a straight forward training sche... | [
"out-of-distribution detection",
"score matching",
"deep learning",
"outlier detection"
] | null | 3,465 | 2010.13132 | title_snapshot | [
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Learning to Sample with Local and Global Contexts in Experience Replay Buffer | https://openreview.net/forum?id=gJYlaqL8i8 | [
"Youngmin Oh",
"Kimin Lee",
"Jinwoo Shin",
"Eunho Yang",
"Sung Ju Hwang"
] | Poster | null | Experience replay, which enables the agents to remember and reuse experience from the past, has played a significant role in the success of off-policy reinforcement learning (RL). To utilize the experience replay efficiently, the existing sampling methods allow selecting out more meaningful experiences by imposing prio... | [
"reinforcement learning",
"experience replay buffer",
"off-policy RL"
] | null | 3,454 | 2007.07358 | title_snapshot | [
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Discovering a set of policies for the worst case reward | https://openreview.net/forum?id=PUkhWz65dy5 | [
"Tom Zahavy",
"Andre Barreto",
"Daniel J Mankowitz",
"Shaobo Hou",
"Brendan O'Donoghue",
"Iurii Kemaev",
"Satinder Singh"
] | Spotlight | null | We study the problem of how to construct a set of policies that can be composed together to solve a collection of reinforcement learning tasks. Each task is a different reward function defined as a linear combination of known features. We consider a specific class of policy compositions which we call set improving pol... | [] | null | 3,432 | 2102.04323 | title_snapshot | [
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Parameter-Based Value Functions | https://openreview.net/forum?id=tV6oBfuyLTQ | [
"Francesco Faccio",
"Louis Kirsch",
"Jürgen Schmidhuber"
] | Poster | null | Traditional off-policy actor-critic Reinforcement Learning (RL) algorithms learn value functions of a single target policy. However, when value functions are updated to track the learned policy, they forget potentially useful information about old policies. We introduce a class of value functions called Parameter-Based... | [
"Reinforcement Learning",
"Off-Policy Reinforcement Learning"
] | null | 3,426 | 2006.09226 | title_snapshot | [
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New Bounds For Distributed Mean Estimation and Variance Reduction | https://openreview.net/forum?id=t86MwoUCCNe | [
"Peter Davies",
"Vijaykrishna Gurunanthan",
"Niusha Moshrefi",
"Saleh Ashkboos",
"Dan Alistarh"
] | Poster | null | We consider the problem of distributed mean estimation (DME), in which $n$ machines are each given a local $d$-dimensional vector $\mathbf x_v \in \mathbb R^d$, and must cooperate to estimate the mean of their inputs $\mathbf \mu = \frac 1n\sum_{v = 1}^n \mathbf x_v$, while minimizing total communication cost. DME is ... | [
"distributed machine learning",
"mean estimation",
"variance reduction",
"lattices"
] | null | 3,418 | 2002.09268 | title_snapshot | [
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Learning to Set Waypoints for Audio-Visual Navigation | https://openreview.net/forum?id=cR91FAodFMe | [
"Changan Chen",
"Sagnik Majumder",
"Ziad Al-Halah",
"Ruohan Gao",
"Santhosh Kumar Ramakrishnan",
"Kristen Grauman"
] | Poster | null | In audio-visual navigation, an agent intelligently travels through a complex, unmapped 3D environment using both sights and sounds to find a sound source (e.g., a phone ringing in another room). Existing models learn to act at a fixed granularity of agent motion and rely on simple recurrent aggregations of the audio ob... | [
"visual navigation",
"audio visual learning",
"embodied vision"
] | null | 3,405 | 2008.09622 | title_snapshot | [
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Disambiguating Symbolic Expressions in Informal Documents | https://openreview.net/forum?id=K5j7D81ABvt | [
"Dennis Müller",
"Cezary Kaliszyk"
] | Poster | null | We propose the task of \emph{disambiguating} symbolic expressions in informal STEM documents in the form of \LaTeX files -- that is, determining their precise semantics and abstract syntax tree -- as a neural machine translation task. We discuss the distinct challenges involved and present a dataset with roughly 33,000... | [] | null | 3,399 | 2101.11716 | title_snapshot | [
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Colorization Transformer | https://openreview.net/forum?id=5NA1PinlGFu | [
"Manoj Kumar",
"Dirk Weissenborn",
"Nal Kalchbrenner"
] | Poster | null | We present the Colorization Transformer, a novel approach for diverse high fidelity image colorization based on self-attention. Given a grayscale image, the colorization proceeds in three steps. We first use a conditional autoregressive transformer to produce a low resolution coarse coloring of the grayscale image. Our... | [] | null | 3,388 | 2102.04432 | title_snapshot | [
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Theoretical bounds on estimation error for meta-learning | https://openreview.net/forum?id=SZ3wtsXfzQR | [
"James Lucas",
"Mengye Ren",
"Irene Raissa KAMENI KAMENI",
"Toniann Pitassi",
"Richard Zemel"
] | Poster | null | Machine learning models have traditionally been developed under the assumption that the training and test distributions match exactly. However, recent success in few-shot learning and related problems are encouraging signs that these models can be adapted to more realistic settings where train and test distributions di... | [
"meta learning",
"few-shot",
"minimax risk",
"lower bounds",
"learning theory"
] | null | 3,384 | 2010.07140 | title_snapshot | [
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Implicit Normalizing Flows | https://openreview.net/forum?id=8PS8m9oYtNy | [
"Cheng Lu",
"Jianfei Chen",
"Chongxuan Li",
"Qiuhao Wang",
"Jun Zhu"
] | Spotlight | null | Normalizing flows define a probability distribution by an explicit invertible transformation $\boldsymbol{\mathbf{z}}=f(\boldsymbol{\mathbf{x}})$. In this work, we present implicit normalizing flows (ImpFlows), which generalize normalizing flows by allowing the mapping to be implicitly defined by the roots of an equati... | [
"Normalizing flows",
"deep generative models",
"probabilistic inference",
"implicit functions"
] | null | 3,381 | 2103.09527 | title_snapshot | [
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Variational Information Bottleneck for Effective Low-Resource Fine-Tuning | https://openreview.net/forum?id=kvhzKz-_DMF | [
"Rabeeh Karimi mahabadi",
"Yonatan Belinkov",
"James Henderson"
] | Poster | null | While large-scale pretrained language models have obtained impressive results when fine-tuned on a wide variety of tasks, they still often suffer from overfitting in low-resource scenarios. Since such models are general-purpose feature extractors, many of these features are inevitably irrelevant for a given target task... | [
"Transfer learning",
"NLP",
"large-scale pre-trained language models",
"over-fitting",
"robust",
"biases",
"variational information bottleneck"
] | null | 3,366 | 2106.05469 | title_snapshot | [
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TropEx: An Algorithm for Extracting Linear Terms in Deep Neural Networks | https://openreview.net/forum?id=IqtonxWI0V3 | [
"Martin Trimmel",
"Henning Petzka",
"Cristian Sminchisescu"
] | Poster | null | Deep neural networks with rectified linear (ReLU) activations are piecewise linear functions, where hyperplanes partition the input space into an astronomically high number of linear regions. Previous work focused on counting linear regions to measure the network's expressive power and on analyzing geometric properties... | [
"linear regions",
"linear terms",
"deep learning theory",
"deep neural networks",
"rectified linear unit",
"relu network",
"piecewise linear function",
"tropical function"
] | null | 3,365 | null | null | [
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Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor Projections | https://openreview.net/forum?id=dx4b7lm8jMM | [
"Csaba Toth",
"Patric Bonnier",
"Harald Oberhauser"
] | Poster | null | Sequential data such as time series, video, or text can be challenging to analyse as the ordered structure gives rise to complex dependencies. At the heart of this is non-commutativity, in the sense that reordering the elements of a sequence can completely change its meaning. We use a classical mathematical object -- t... | [
"time series",
"sequential data",
"representation learning",
"low-rank tensors",
"classification",
"generative modelling"
] | null | 3,363 | 2006.07027 | title_snapshot | [
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Representation learning for improved interpretability and classification accuracy of clinical factors from EEG | https://openreview.net/forum?id=TVjLza1t4hI | [
"Garrett Honke",
"Irina Higgins",
"Nina Thigpen",
"Vladimir Miskovic",
"Katie Link",
"Sunny Duan",
"Pramod Gupta",
"Julia Klawohn",
"Greg Hajcak"
] | Poster | null | Despite extensive standardization, diagnostic interviews for mental health disorders encompass substantial subjective judgment. Previous studies have demonstrated that EEG-based neural measures can function as reliable objective correlates of depression, or even predictors of depression and its course. However, their c... | [
"EEG",
"ERP",
"electroencephalography",
"depression",
"representation learning",
"disentanglement",
"beta-VAE"
] | null | 3,359 | 2010.15274 | title_snapshot | [
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Language-Agnostic Representation Learning of Source Code from Structure and Context | https://openreview.net/forum?id=Xh5eMZVONGF | [
"Daniel Zügner",
"Tobias Kirschstein",
"Michele Catasta",
"Jure Leskovec",
"Stephan Günnemann"
] | Poster | null | Source code (Context) and its parsed abstract syntax tree (AST; Structure) are two complementary representations of the same computer program. Traditionally, designers of machine learning models have relied predominantly either on Structure or Context. We propose a new model, which jointly learns on Context and Structu... | [
"machine learning for code",
"code summarization"
] | null | 3,338 | 2103.11318 | title_snapshot | [
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Generalized Multimodal ELBO | https://openreview.net/forum?id=5Y21V0RDBV | [
"Thomas M. Sutter",
"Imant Daunhawer",
"Julia E Vogt"
] | Poster | null | Multiple data types naturally co-occur when describing real-world phenomena and learning from them is a long-standing goal in machine learning research. However, existing self-supervised generative models approximating an ELBO are not able to fulfill all desired requirements of multimodal models: their posterior approx... | [
"Multimodal",
"VAE",
"ELBO",
"self-supervised",
"generative learning"
] | null | 3,332 | 2105.02470 | title_snapshot | [
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Model-based micro-data reinforcement learning: what are the crucial model properties and which model to choose? | https://openreview.net/forum?id=p5uylG94S68 | [
"Balázs Kégl",
"Gabriel Hurtado",
"Albert Thomas"
] | Poster | null | We contribute to micro-data model-based reinforcement learning (MBRL) by rigorously comparing popular generative models using a fixed (random shooting) control agent. We find that on an environment that requires multimodal posterior predictives, mixture density nets outperform all other models by a large margin. When m... | [
"model-based reinforcement learning",
"generative models",
"mixture density nets",
"dynamic systems",
"heteroscedasticity"
] | null | 3,299 | 2107.11587 | title_snapshot | [
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Set Prediction without Imposing Structure as Conditional Density Estimation | https://openreview.net/forum?id=04ArenGOz3 | [
"David W Zhang",
"Gertjan J. Burghouts",
"Cees G. M. Snoek"
] | Poster | null | Set prediction is about learning to predict a collection of unordered variables with unknown interrelations. Training such models with set losses imposes the structure of a metric space over sets. We focus on stochastic and underdefined cases, where an incorrectly chosen loss function leads to implausible predictions. ... | [
"set prediction",
"energy based models"
] | null | 3,297 | 2010.04109 | title_snapshot | [
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Learning Value Functions in Deep Policy Gradients using Residual Variance | https://openreview.net/forum?id=NX1He-aFO_F | [
"Yannis Flet-Berliac",
"reda ouhamma",
"odalric-ambrym maillard",
"Philippe Preux"
] | Poster | null | Policy gradient algorithms have proven to be successful in diverse decision making and control tasks. However, these methods suffer from high sample complexity and instability issues. In this paper, we address these challenges by providing a different approach for training the critic in the actor-critic framework. Our ... | [] | null | 3,292 | 2010.04440 | title_snapshot | [
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IDF++: Analyzing and Improving Integer Discrete Flows for Lossless Compression | https://openreview.net/forum?id=MBOyiNnYthd | [
"Rianne van den Berg",
"Alexey A. Gritsenko",
"Mostafa Dehghani",
"Casper Kaae Sønderby",
"Tim Salimans"
] | Poster | null | In this paper we analyse and improve integer discrete flows for lossless compression. Integer discrete flows are a recently proposed class of models that learn invertible transformations for integer-valued random variables. Their discrete nature makes them particularly suitable for lossless compression with entropy cod... | [
"normalizing flows",
"lossless source compression",
"generative modeling"
] | null | 3,291 | 2006.12459 | title_snapshot | [
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Fully Unsupervised Diversity Denoising with Convolutional Variational Autoencoders | https://openreview.net/forum?id=agHLCOBM5jP | [
"Mangal Prakash",
"Alexander Krull",
"Florian Jug"
] | Poster | null | Deep Learning based methods have emerged as the indisputable leaders for virtually all image restoration tasks. Especially in the domain of microscopy images, various content-aware image restoration (CARE) approaches are now used to improve the interpretability of acquired data. Naturally, there are limitations to what... | [
"Diversity denoising",
"Unsupervised denoising",
"Variational Autoencoders",
"Noise model"
] | null | 3,284 | 2006.06072 | title_snapshot | [
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Is Attention Better Than Matrix Decomposition? | https://openreview.net/forum?id=1FvkSpWosOl | [
"Zhengyang Geng",
"Meng-Hao Guo",
"Hongxu Chen",
"Xia Li",
"Ke Wei",
"Zhouchen Lin"
] | Poster | null | As an essential ingredient of modern deep learning, attention mechanism, especially self-attention, plays a vital role in the global correlation discovery. However, is hand-crafted attention irreplaceable when modeling the global context? Our intriguing finding is that self-attention is not better than the matrix decom... | [
"attention models",
"matrix decomposition",
"computer vision"
] | null | 3,278 | 2109.04553 | title_snapshot | [
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Improving Transformation Invariance in Contrastive Representation Learning | https://openreview.net/forum?id=NomEDgIEBwE | [
"Adam Foster",
"Rattana Pukdee",
"Tom Rainforth"
] | Poster | null | We propose methods to strengthen the invariance properties of representations obtained by contrastive learning. While existing approaches implicitly induce a degree of invariance as representations are learned, we look to more directly enforce invariance in the encoding process. To this end, we first introduce a traini... | [
"contrastive learning",
"representation learning",
"transformation invariance"
] | null | 3,273 | 2010.09515 | title_snapshot | [
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On the Origin of Implicit Regularization in Stochastic Gradient Descent | https://openreview.net/forum?id=rq_Qr0c1Hyo | [
"Samuel L Smith",
"Benoit Dherin",
"David Barrett",
"Soham De"
] | Poster | null | For infinitesimal learning rates, stochastic gradient descent (SGD) follows the path of gradient flow on the full batch loss function. However moderately large learning rates can achieve higher test accuracies, and this generalization benefit is not explained by convergence bounds, since the learning rate which maximiz... | [
"SGD",
"learning rate",
"batch size",
"optimization",
"generalization",
"implicit regularization",
"backward error analysis",
"SDE",
"stochastic differential equation",
"ODE",
"ordinary differential equation"
] | null | 3,269 | 2101.12176 | title_snapshot | [
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Share or Not? Learning to Schedule Language-Specific Capacity for Multilingual Translation | https://openreview.net/forum?id=Wj4ODo0uyCF | [
"Biao Zhang",
"Ankur Bapna",
"Rico Sennrich",
"Orhan Firat"
] | Oral | null | Using a mix of shared and language-specific (LS) parameters has shown promise in multilingual neural machine translation (MNMT), but the question of when and where LS capacity matters most is still under-studied. We offer such a study by proposing conditional language-specific routing (CLSR). CLSR employs hard binary ... | [
"language-specific modeling",
"conditional computation",
"multilingual translation",
"multilingual transformer"
] | null | 3,265 | null | null | [
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Transient Non-stationarity and Generalisation in Deep Reinforcement Learning | https://openreview.net/forum?id=Qun8fv4qSby | [
"Maximilian Igl",
"Gregory Farquhar",
"Jelena Luketina",
"Wendelin Boehmer",
"Shimon Whiteson"
] | Poster | null | Non-stationarity can arise in Reinforcement Learning (RL) even in stationary environments. For example, most RL algorithms collect new data throughout training, using a non-stationary behaviour policy. Due to the transience of this non-stationarity, it is often not explicitly addressed in deep RL and a single neural ne... | [
"Reinforcement Learning",
"Generalization"
] | null | 3,261 | 2006.05826 | title_snapshot | [
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Lossless Compression of Structured Convolutional Models via Lifting | https://openreview.net/forum?id=oxnp2q-PGL4 | [
"Gustav Sourek",
"Filip Zelezny",
"Ondrej Kuzelka"
] | Poster | null | Lifting is an efficient technique to scale up graphical models generalized to relational domains by exploiting the underlying symmetries. Concurrently, neural models are continuously expanding from grid-like tensor data into structured representations, such as various attributed graphs and relational databases. To addr... | [
"weight sharing",
"graph neural networks",
"lifted inference",
"relational learning",
"dynamic computation graphs",
"convolutional models"
] | null | 3,256 | 2007.06567 | title_snapshot | [
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Analyzing the Expressive Power of Graph Neural Networks in a Spectral Perspective | https://openreview.net/forum?id=-qh0M9XWxnv | [
"Muhammet Balcilar",
"Guillaume Renton",
"Pierre Héroux",
"Benoit Gaüzère",
"Sébastien Adam",
"Paul Honeine"
] | Poster | null | In the recent literature of Graph Neural Networks (GNN), the expressive power of models has been studied through their capability to distinguish if two given graphs are isomorphic or not. Since the graph isomorphism problem is NP-intermediate, and Weisfeiler-Lehman (WL) test can give sufficient but not enough evidence ... | [
"Graph Neural Networks",
"Spectral Graph Filter",
"Spectral Analysis"
] | null | 3,251 | null | null | [
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End-to-end Adversarial Text-to-Speech | https://openreview.net/forum?id=rsf1z-JSj87 | [
"Jeff Donahue",
"Sander Dieleman",
"Mikolaj Binkowski",
"Erich Elsen",
"Karen Simonyan"
] | Oral | null | Modern text-to-speech synthesis pipelines typically involve multiple processing stages, each of which is designed or learnt independently from the rest. In this work, we take on the challenging task of learning to synthesise speech from normalised text or phonemes in an end-to-end manner, resulting in models which oper... | [
"text-to-speech",
"speech synthesis",
"adversarial",
"GAN",
"end-to-end",
"feed-forward",
"generative model"
] | null | 3,246 | 2006.03575 | title_snapshot | [
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A unifying view on implicit bias in training linear neural networks | https://openreview.net/forum?id=ZsZM-4iMQkH | [
"Chulhee Yun",
"Shankar Krishnan",
"Hossein Mobahi"
] | Poster | null | We study the implicit bias of gradient flow (i.e., gradient descent with infinitesimal step size) on linear neural network training. We propose a tensor formulation of neural networks that includes fully-connected, diagonal, and convolutional networks as special cases, and investigate the linear version of the formulat... | [
"implicit bias",
"implicit regularization",
"convergence",
"gradient flow",
"gradient descent"
] | null | 3,245 | 2010.02501 | title_snapshot | [
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Balancing Constraints and Rewards with Meta-Gradient D4PG | https://openreview.net/forum?id=TQt98Ya7UMP | [
"Dan A. Calian",
"Daniel J Mankowitz",
"Tom Zahavy",
"Zhongwen Xu",
"Junhyuk Oh",
"Nir Levine",
"Timothy Mann"
] | Poster | null | Deploying Reinforcement Learning (RL) agents to solve real-world applications often requires satisfying complex system constraints. Often the constraint thresholds are incorrectly set due to the complex nature of a system or the inability to verify the thresholds offline (e.g, no simulator or reasonable offline evaluat... | [
"reinforcement learning",
"meta-gradients",
"constraints"
] | null | 3,232 | 2010.06324 | title_snapshot | [
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Robust Curriculum Learning: from clean label detection to noisy label self-correction | https://openreview.net/forum?id=lmTWnm3coJJ | [
"Tianyi Zhou",
"Shengjie Wang",
"Jeff Bilmes"
] | Poster | null | Neural network training can easily overfit noisy labels resulting in poor generalization performance. Existing methods address this problem by (1) filtering out the noisy data and only using the clean data for training or (2) relabeling the noisy data by the model during training or by another model trained only on a c... | [
"curriculum learning",
"noisy label",
"robust learning",
"training dynamics",
"neural networks"
] | null | 3,222 | null | null | [
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Clairvoyance: A Pipeline Toolkit for Medical Time Series | https://openreview.net/forum?id=xnC8YwKUE3k | [
"Daniel Jarrett",
"Jinsung Yoon",
"Ioana Bica",
"Zhaozhi Qian",
"Ari Ercole",
"Mihaela van der Schaar"
] | Poster | null | Time-series learning is the bread and butter of data-driven *clinical decision support*, and the recent explosion in ML research has demonstrated great potential in various healthcare settings. At the same time, medical time-series problems in the wild are challenging due to their highly *composite* nature: They entail... | [
"reproducibility",
"healthcare",
"medical time series",
"pipeline toolkit",
"software"
] | null | 3,220 | 2310.18688 | title_snapshot | [
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Plan-Based Relaxed Reward Shaping for Goal-Directed Tasks | https://openreview.net/forum?id=w2Z2OwVNeK | [
"Ingmar Schubert",
"Ozgur S Oguz",
"Marc Toussaint"
] | Poster | null | In high-dimensional state spaces, the usefulness of Reinforcement Learning (RL) is limited by the problem of exploration. This issue has been addressed using potential-based reward shaping (PB-RS) previously. In the present work, we introduce Final-Volume-Preserving Reward Shaping (FV-RS). FV-RS relaxes the strict opti... | [
"reinforcement learning",
"reward shaping",
"plan-based reward shaping",
"robotics",
"robotic manipulation"
] | null | 3,201 | 2107.06661 | title_snapshot | [
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Improving VAEs' Robustness to Adversarial Attack | https://openreview.net/forum?id=-Hs_otp2RB | [
"Matthew JF Willetts",
"Alexander Camuto",
"Tom Rainforth",
"S Roberts",
"Christopher C Holmes"
] | Poster | null | Variational autoencoders (VAEs) have recently been shown to be vulnerable to adversarial attacks, wherein they are fooled into reconstructing a chosen target image. However, how to defend against such attacks remains an open problem. We make significant advances in addressing this issue by introducing methods for produ... | [
"deep generative models",
"variational autoencoders",
"robustness",
"adversarial attack"
] | null | 3,195 | 1906.00230 | title_snapshot | [
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Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphs | https://openreview.net/forum?id=Jnspzp-oIZE | [
"Pim De Haan",
"Maurice Weiler",
"Taco Cohen",
"Max Welling"
] | Spotlight | null | A common approach to define convolutions on meshes is to interpret them as a graph and apply graph convolutional networks (GCNs). Such GCNs utilize isotropic kernels and are therefore insensitive to the relative orientation of vertices and thus to the geometry of the mesh as a whole. We propose Gauge Equivariant Mesh ... | [
"symmetry",
"equivariance",
"mesh",
"geometric",
"convolution"
] | null | 3,192 | 2003.05425 | title_snapshot | [
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Differentiable Segmentation of Sequences | https://openreview.net/forum?id=4T489T4yav | [
"Erik Scharwächter",
"Jonathan Lennartz",
"Emmanuel Müller"
] | Poster | null | Segmented models are widely used to describe non-stationary sequential data with discrete change points. Their estimation usually requires solving a mixed discrete-continuous optimization problem, where the segmentation is the discrete part and all other model parameters are continuous. A number of estimation algorithm... | [
"segmented models",
"segmentation",
"change point detection",
"concept drift",
"warping functions",
"gradient descent"
] | null | 3,185 | 2006.13105 | title_snapshot | [
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Generalization bounds via distillation | https://openreview.net/forum?id=EGdFhBzmAwB | [
"Daniel Hsu",
"Ziwei Ji",
"Matus Telgarsky",
"Lan Wang"
] | Spotlight | null | This paper theoretically investigates the following empirical phenomenon: given a high-complexity network with poor generalization bounds, one can distill it into a network with nearly identical predictions but low complexity and vastly smaller generalization bounds. The main contribution is an analysis showing that t... | [
"Generalization",
"statistical learning theory",
"theory",
"distillation"
] | null | 3,178 | 2104.05641 | title_snapshot | [
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-... |
Learning Mesh-Based Simulation with Graph Networks | https://openreview.net/forum?id=roNqYL0_XP | [
"Tobias Pfaff",
"Meire Fortunato",
"Alvaro Sanchez-Gonzalez",
"Peter Battaglia"
] | Spotlight | null | Mesh-based simulations are central to modeling complex physical systems in many disciplines across science and engineering. Mesh representations support powerful numerical integration methods and their resolution can be adapted to strike favorable trade-offs between accuracy and efficiency. However, high-dimensional sc... | [
"graph networks",
"simulation",
"mesh",
"physics"
] | null | 3,177 | 2010.03409 | title_snapshot | [
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0.0... |
GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing | https://openreview.net/forum?id=kyaIeYj4zZ | [
"Tao Yu",
"Chien-Sheng Wu",
"Xi Victoria Lin",
"bailin wang",
"Yi Chern Tan",
"Xinyi Yang",
"Dragomir Radev",
"richard socher",
"Caiming Xiong"
] | Poster | null | We present GraPPa, an effective pre-training approach for table semantic parsing that learns a compositional inductive bias in the joint representations of textual and tabular data. We construct synthetic question-SQL pairs over high-quality tables via a synchronous context-free grammar (SCFG). We pre-train our model o... | [
"text-to-sql",
"semantic parsing",
"pre-training",
"nlp"
] | null | 3,175 | 2009.13845 | title_snapshot | [
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0.016526808962225914,
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-0... |
Sliced Kernelized Stein Discrepancy | https://openreview.net/forum?id=t0TaKv0Gx6Z | [
"Wenbo Gong",
"Yingzhen Li",
"José Miguel Hernández-Lobato"
] | Poster | null | Kernelized Stein discrepancy (KSD), though being extensively used in goodness-of-fit tests and model learning, suffers from the curse-of-dimensionality. We address this issue by proposing the sliced Stein discrepancy and its scalable and kernelized variants, which employs kernel-based test functions defined on the opti... | [
"kernel methods",
"variational inference",
"particle inference"
] | null | 3,174 | 2006.16531 | title_snapshot | [
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-0.05529491603374481,
0.... |
Variational Intrinsic Control Revisited | https://openreview.net/forum?id=P0p33rgyoE | [
"Taehwan Kwon"
] | Poster | null | In this paper, we revisit variational intrinsic control (VIC), an unsupervised reinforcement learning method for finding the largest set of intrinsic options available to an agent. In the original work by Gregor et al. (2016), two VIC algorithms were proposed: one that represents the options explicitly, and the other t... | [
"Unsupervised reinforcement learning",
"Information theory"
] | null | 3,173 | 2010.03281 | title_snapshot | [
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0.03710036724805832,
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0.... |
On Statistical Bias In Active Learning: How and When to Fix It | https://openreview.net/forum?id=JiYq3eqTKY | [
"Sebastian Farquhar",
"Yarin Gal",
"Tom Rainforth"
] | Spotlight | null | Active learning is a powerful tool when labelling data is expensive, but it introduces a bias because the training data no longer follows the population distribution. We formalize this bias and investigate the situations in which it can be harmful and sometimes even helpful. We further introduce novel corrective weight... | [
"Active Learning",
"Monte Carlo",
"Risk Estimation"
] | null | 3,171 | 2101.11665 | title_snapshot | [
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-0.030669376254081726,
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0.014825025573372841,
-0.0719435065984726,
-... |
Winning the L2RPN Challenge: Power Grid Management via Semi-Markov Afterstate Actor-Critic | https://openreview.net/forum?id=LmUJqB1Cz8 | [
"Deunsol Yoon",
"Sunghoon Hong",
"Byung-Jun Lee",
"Kee-Eung Kim"
] | Spotlight | null | Safe and reliable electricity transmission in power grids is crucial for modern society. It is thus quite natural that there has been a growing interest in the automatic management of power grids, exemplified by the Learning to Run a Power Network Challenge (L2RPN), modeling the problem as a reinforcement learning (RL) ... | [
"power grid management",
"deep reinforcement learning",
"graph neural network"
] | null | 3,169 | null | null | [
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... |
HyperDynamics: Meta-Learning Object and Agent Dynamics with Hypernetworks | https://openreview.net/forum?id=pHXfe1cOmA | [
"Zhou Xian",
"Shamit Lal",
"Hsiao-Yu Tung",
"Emmanouil Antonios Platanios",
"Katerina Fragkiadaki"
] | Poster | null | We propose HyperDynamics, a dynamics meta-learning framework that conditions on an agent’s interactions with the environment and optionally its visual observations, and generates the parameters of neural dynamics models based on inferred properties of the dynamical system. Physical and visual properties of the environm... | [] | null | 3,167 | 2103.09439 | title_snapshot | [
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-0.008442762307822704,
0.010131536051630974,
-0.05053221806883812,
-0.... |
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