b'Anirudh Goyal'

research

∙ 07/29/2023

A Theory for Emergence of Complex Skills in Language Models

A major driver of AI products today is the fact that new skills emerge i...

0 Sanjeev Arora, et al. ∙

research

∙ 06/03/2023

Cycle Consistency Driven Object Discovery

Developing deep learning models that effectively learn object-centric re...

0 Aniket Didolkar, et al. ∙

research

∙ 05/31/2023

Spotlight Attention: Robust Object-Centric Learning With a Spatial Locality Prior

The aim of object-centric vision is to construct an explicit representat...

0 Ayush Chakravarthy, et al. ∙

research

∙ 04/12/2023

DiscoGen: Learning to Discover Gene Regulatory Networks

Accurately inferring Gene Regulatory Networks (GRNs) is a critical and c...

0 Nan Rosemary Ke, et al. ∙

research

∙ 04/08/2023

TC-VAE: Uncovering Out-of-Distribution Data Generative Factors

Uncovering data generative factors is the ultimate goal of disentangleme...

0 Cristian Meo, et al. ∙

research

∙ 01/27/2023

Leveraging the Third Dimension in Contrastive Learning

Self-Supervised Learning (SSL) methods operate on unlabeled data to lear...

0 Sumukh Aithal, et al. ∙

research

∙ 12/28/2022

Representation Learning in Deep RL via Discrete Information Bottleneck

Several self-supervised representation learning methods have been propos...

0 Riashat Islam, et al. ∙

research

∙ 11/01/2022

Discrete Factorial Representations as an Abstraction for Goal Conditioned Reinforcement Learning

Goal-conditioned reinforcement learning (RL) is a promising direction fo...

0 Riashat Islam, et al. ∙

research

∙ 10/24/2022

GFlowOut: Dropout with Generative Flow Networks

Bayesian Inference offers principled tools to tackle many critical probl...

7 Dianbo Liu, et al. ∙

research

∙ 10/04/2022

Stateful active facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learning

In cooperative multi-agent reinforcement learning, a team of agents work...

2 Dianbo Liu, et al. ∙

research

∙ 07/22/2022

Discrete Key-Value Bottleneck

Deep neural networks perform well on prediction and classification tasks...

4 Frederik Träuble, et al. ∙

research

∙ 06/09/2022

On the Generalization and Adaption Performance of Causal Models

Learning models that offer robust out-of-distribution generalization and...

11 Nino Scherrer, et al. ∙

research

∙ 05/30/2022

Temporal Latent Bottleneck: Synthesis of Fast and Slow Processing Mechanisms in Sequence Learning

Recurrent neural networks have a strong inductive bias towards learning ...

14 Aniket Didolkar, et al. ∙

research

∙ 05/21/2022

Coordinating Policies Among Multiple Agents via an Intelligent Communication Channel

In Multi-Agent Reinforcement Learning (MARL), specialized channels are o...

41 Dianbo Liu, et al. ∙

research

∙ 04/11/2022

Learning to Induce Causal Structure

The fundamental challenge in causal induction is to infer the underlying...

0 Nan Rosemary Ke, et al. ∙

research

∙ 03/21/2022

Generating Fast and Slow: Scene Decomposition via Reconstruction

We consider the problem of segmenting scenes into constituent entities, ...

0 Mihir Prabhudesai, et al. ∙

research

∙ 02/17/2022

Retrieval-Augmented Reinforcement Learning

Most deep reinforcement learning (RL) algorithms distill experience into...

0 Anirudh Goyal, et al. ∙

research

∙ 09/06/2021

Learning Neural Causal Models with Active Interventions

Discovering causal structures from data is a challenging inference probl...

7 Nino Scherrer, et al. ∙

research

∙ 07/06/2021

Discrete-Valued Neural Communication

Deep learning has advanced from fully connected architectures to structu...

8 Dianbo Liu, et al. ∙

research

∙ 07/02/2021

Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning

Inducing causal relationships from observations is a classic problem in ...

7 Nan Rosemary Ke, et al. ∙

research

∙ 06/14/2021

Variational Causal Networks: Approximate Bayesian Inference over Causal Structures

Learning the causal structure that underlies data is a crucial step towa...

0 Yashas Annadani, et al. ∙

research

∙ 06/11/2021

Robust Representation Learning via Perceptual Similarity Metrics

A fundamental challenge in artificial intelligence is learning useful re...

0 Saeid Asgari Taghanaki, et al. ∙

research

∙ 05/18/2021

Fast and Slow Learning of Recurrent Independent Mechanisms

Decomposing knowledge into interchangeable pieces promises a generalizat...

9 Kanika Madan, et al. ∙

research

∙ 03/02/2021

Neural Production Systems

Visual environments are structured, consisting of distinct objects or en...

12 Anirudh Goyal, et al. ∙

research

∙ 03/01/2021

Coordination Among Neural Modules Through a Shared Global Workspace

Deep learning has seen a movement away from representing examples with a...

14 Anirudh Goyal, et al. ∙

research

∙ 02/27/2021

Transformers with Competitive Ensembles of Independent Mechanisms

An important development in deep learning from the earliest MLPs has bee...

8 Alex Lamb, et al. ∙

research

∙ 02/22/2021

Towards Causal Representation Learning

The two fields of machine learning and graphical causality arose and dev...

44 Bernhard Schölkopf, et al. ∙

research

∙ 11/30/2020

Inductive Biases for Deep Learning of Higher-Level Cognition

A fascinating hypothesis is that human and animal intelligence could be ...

73 Anirudh Goyal, et al. ∙

research

∙ 10/08/2020

CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning

Despite recent successes of reinforcement learning (RL), it remains a ch...

5 Ossama Ahmed, et al. ∙

research

∙ 07/13/2020

S2RMs: Spatially Structured Recurrent Modules

Capturing the structure of a data-generating process by means of appropr...

5 Nasim Rahaman, et al. ∙

research

∙ 06/30/2020

Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules

Robust perception relies on both bottom-up and top-down signals. Bottom-...

11 Sarthak Mittal, et al. ∙

research

∙ 06/30/2020

Maximum Entropy Models for Fast Adaptation

Deep Neural Networks have shown great promise on a variety of downstream...

12 Samarth Sinha, et al. ∙

research

∙ 06/29/2020

Object Files and Schemata: Factorizing Declarative and Procedural Knowledge in Dynamical Systems

Modeling a structured, dynamic environment like a video game requires ke...

0 Anirudh Goyal, et al. ∙

research

∙ 06/16/2020

Untangling tradeoffs between recurrence and self-attention in neural networks

Attention and self-attention mechanisms, inspired by cognitive processes...

0 Giancarlo Kerg, et al. ∙

research

∙ 06/14/2020

Is Independence all you need? On the Generalization of Representations Learned from Correlated Data

Despite impressive progress in the last decade, it still remains an open...

14 Frederik Träuble, et al. ∙

research

∙ 04/24/2020

The Variational Bandwidth Bottleneck: Stochastic Evaluation on an Information Budget

In many applications, it is desirable to extract only the relevant infor...

0 Anirudh Goyal, et al. ∙

research

∙ 03/10/2020

DIBS: Diversity inducing Information Bottleneck in Model Ensembles

Although deep learning models have achieved state-of-the-art performance...

7 Samarth Sinha, et al. ∙

research

∙ 02/14/2020

Top-K Training of GANs: Improving Generators by Making Critics Less Critical

We introduce a simple (one line of code) modification to the Generative ...

16 Samarth Sinha, et al. ∙

research

∙ 10/29/2019

Small-GAN: Speeding Up GAN Training Using Core-sets

Recent work by Brock et al. (2018) suggests that Generative Adversarial ...

22 Samarth Sinha, et al. ∙

research

∙ 10/02/2019

Learning Neural Causal Models from Unknown Interventions

Meta-learning over a set of distributions can be interpreted as learning...

31 Nan Rosemary Ke, et al. ∙

research

∙ 09/24/2019

Recurrent Independent Mechanisms

Learning modular structures which reflect the dynamics of the environmen...

35 Anirudh Goyal, et al. ∙

research

∙ 07/02/2019

Learning the Arrow of Time

We humans seem to have an innate understanding of the asymmetric progres...

9 Nasim Rahaman, et al. ∙

research

∙ 06/25/2019

Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives

Reinforcement learning agents that operate in diverse and complex enviro...

4 Anirudh Goyal, et al. ∙

research

∙ 06/11/2019

Learning Powerful Policies by Using Consistent Dynamics Model

Model-based Reinforcement Learning approaches have the promise of being ...

10 Shagun Sodhani, et al. ∙

research

∙ 05/26/2019

State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations

Machine learning promises methods that generalize well from finite label...

0 Alex Lamb, et al. ∙

research

∙ 03/05/2019

Learning Dynamics Model in Reinforcement Learning by Incorporating the Long Term Future

In model-based reinforcement learning, the agent interleaves between mod...

14 Nan Rosemary Ke, et al. ∙

research

∙ 02/16/2019

Communication Topologies Between Learning Agents in Deep Reinforcement Learning

A common technique to improve speed and robustness of learning in deep r...

14 Dhaval Adjodah, et al. ∙

research

∙ 01/30/2019

A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms

We propose to meta-learn causal structures based on how fast a learner a...

0 Yoshua Bengio, et al. ∙

research

∙ 01/30/2019

InfoBot: Transfer and Exploration via the Information Bottleneck

A central challenge in reinforcement learning is discovering effective p...

18 Anirudh Goyal, et al. ∙

research

∙ 01/24/2019

Maximum Entropy Generators for Energy-Based Models

Unsupervised learning is about capturing dependencies between variables ...

2 Rithesh Kumar, et al. ∙

Anirudh Goyal

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