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07/17/2023
Meta-Value Learning: a General Framework for Learning with Learning Awareness
Gradient-based learning in multi-agent systems is difficult because the ...
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05/30/2023
Bigger, Better, Faster: Human-level Atari with human-level efficiency
We introduce a value-based RL agent, which we call BBF, that achieves su...
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05/26/2023
Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets
Combinatorial optimization (CO) problems are often NP-hard and thus out ...
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02/11/2023
Distributional GFlowNets with Quantile Flows
Generative Flow Networks (GFlowNets) are a new family of probabilistic s...
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02/01/2023
Versatile Energy-Based Models for High Energy Physics
Energy-based models have the natural advantage of flexibility in the for...
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11/15/2022
On the Compositional Generalization Gap of In-Context Learning
Pretrained large generative language models have shown great performance...
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11/15/2022
Teaching Algorithmic Reasoning via In-context Learning
Large language models (LLMs) have shown increasing in-context learning c...
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10/07/2022
Generative Augmented Flow Networks
The Generative Flow Network is a probabilistic framework where an agent ...
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10/03/2022
Latent State Marginalization as a Low-cost Approach for Improving Exploration
While the maximum entropy (MaxEnt) reinforcement learning (RL) framework...
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09/24/2022
Unsupervised Model-based Pre-training for Data-efficient Control from Pixels
Controlling artificial agents from visual sensory data is an arduous tas...
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08/16/2022
Riemannian Diffusion Models
Diffusion models are recent state-of-the-art methods for image generatio...
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06/30/2022
R-MelNet: Reduced Mel-Spectral Modeling for Neural TTS
This paper introduces R-MelNet, a two-part autoregressive architecture w...
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06/03/2022
Beyond Tabula Rasa: Reincarnating Reinforcement Learning
Learning tabula rasa, that is without any prior knowledge, is the preval...
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06/02/2022
Expressiveness and Learnability: A Unifying View for Evaluating Self-Supervised Learning
We propose a unifying view to analyze the representation quality of self...
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06/01/2022
Cascaded Video Generation for Videos In-the-Wild
Videos can be created by first outlining a global view of the scene and ...
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05/16/2022
The Primacy Bias in Deep Reinforcement Learning
This work identifies a common flaw of deep reinforcement learning (RL) a...
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04/01/2022
Simplicial Embeddings in Self-Supervised Learning and Downstream Classification
We introduce Simplicial Embeddings (SEMs) as a way to constrain the enco...
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02/03/2022
Generative Flow Networks for Discrete Probabilistic Modeling
We present energy-based generative flow networks (EB-GFN), a novel proba...
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02/01/2022
Fortuitous Forgetting in Connectionist Networks
Forgetting is often seen as an unwanted characteristic in both human and...
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01/18/2022
Invariant Representation Driven Neural Classifier for Anti-QCD Jet Tagging
We leverage representation learning and the inductive bias in neural-net...
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12/17/2021
MIDI-DDSP: Detailed Control of Musical Performance via Hierarchical Modeling
Musical expression requires control of both what notes are played, and h...
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12/09/2021
DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization
Despite overparameterization, deep networks trained via supervised learn...
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11/23/2021
Multi-label Iterated Learning for Image Classification with Label Ambiguity
Transfer learning from large-scale pre-trained models has become essenti...
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10/06/2021
Unifying Likelihood-free Inference with Black-box Sequence Design and Beyond
Black-box optimization formulations for biological sequence design have ...
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09/22/2021
On Bonus-Based Exploration Methods in the Arcade Learning Environment
Research on exploration in reinforcement learning, as applied to Atari 2...
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08/30/2021
Deep Reinforcement Learning at the Edge of the Statistical Precipice
Deep reinforcement learning (RL) algorithms are predominantly evaluated ...
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06/09/2021
Pretraining Representations for Data-Efficient Reinforcement Learning
Data efficiency is a key challenge for deep reinforcement learning. We a...
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06/05/2021
Can Subnetwork Structure be the Key to Out-of-Distribution Generalization?
Can models with particular structure avoid being biased towards spurious...
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06/05/2021
A Variational Perspective on Diffusion-Based Generative Models and Score Matching
Discrete-time diffusion-based generative models and score matching metho...
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06/04/2021
Hierarchical Video Generation for Complex Data
Videos can often be created by first outlining a global description of t...
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05/07/2021
Understanding by Understanding Not: Modeling Negation in Language Models
Negation is a core construction in natural language. Despite being very ...
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05/03/2021
Iterated learning for emergent systematicity in VQA
Although neural module networks have an architectural bias towards compo...
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04/01/2021
Touch-based Curiosity for Sparse-Reward Tasks
Robots in many real-world settings have access to force/torque sensors i...
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03/19/2021
Learning Task Decomposition with Ordered Memory Policy Network
Many complex real-world tasks are composed of several levels of sub-task...
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03/04/2021
Continuous Coordination As a Realistic Scenario for Lifelong Learning
Current deep reinforcement learning (RL) algorithms are still highly tas...
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01/25/2021
Emergent Communication under Competition
The literature in modern machine learning has only negative results for ...
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12/10/2020
Convex Potential Flows: Universal Probability Distributions with Optimal Transport and Convex Optimization
Flow-based models are powerful tools for designing probabilistic models ...
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12/01/2020
StructFormer: Joint Unsupervised Induction of Dependency and Constituency Structure from Masked Language Modeling
There are two major classes of natural language grammars – the dependenc...
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11/18/2020
Gradient Starvation: A Learning Proclivity in Neural Networks
We identify and formalize a fundamental gradient descent phenomenon resu...
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11/11/2020
Unsupervised Learning of Dense Visual Representations
Contrastive self-supervised learning has emerged as a promising approach...
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10/22/2020
NU-GAN: High resolution neural upsampling with GAN
In this paper, we propose NU-GAN, a new method for resampling audio from...
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10/20/2020
Neural Approximate Sufficient Statistics for Implicit Models
We consider the fundamental problem of how to automatically construct su...
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10/09/2020
Recursive Top-Down Production for Sentence Generation with Latent Trees
We model the recursive production property of context-free grammars for ...
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10/06/2020
Supervised Seeded Iterated Learning for Interactive Language Learning
Language drift has been one of the major obstacles to train language mod...
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10/03/2020
Integrating Categorical Semantics into Unsupervised Domain Translation
While unsupervised domain translation (UDT) has seen a lot of success re...
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07/12/2020
Data-Efficient Reinforcement Learning with Momentum Predictive Representations
While deep reinforcement learning excels at solving tasks where large am...
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07/11/2020
Generative Graph Perturbations for Scene Graph Prediction
Inferring objects and their relationships from an image is useful in man...
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06/09/2020
AR-DAE: Towards Unbiased Neural Entropy Gradient Estimation
Entropy is ubiquitous in machine learning, but it is in general intracta...
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05/17/2020
Graph Density-Aware Losses for Novel Compositions in Scene Graph Generation
Scene graph generation (SGG) aims to predict graph-structured descriptio...
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05/06/2020