Search Results for author: Anirudh Goyal

Found 61 papers, 23 papers with code

Learning to Induce Causal Structure

no code implementations11 Apr 2022 Nan Rosemary Ke, Silvia Chiappa, Jane Wang, Jorg Bornschein, Theophane Weber, Anirudh Goyal, Matthew Botvinic, Michael Mozer, Danilo Jimenez Rezende

The fundamental challenge in causal induction is to infer the underlying graph structure given observational and/or interventional data.

Generating Fast and Slow: Scene Decomposition via Reconstruction

no code implementations21 Mar 2022 Mihir Prabhudesai, Anirudh Goyal, Deepak Pathak, Katerina Fragkiadaki

We show the proposed curriculum suffices to break the reconstruction-segmentation trade-off, and slow inference greatly improves segmentation in out-of-distribution scenes.

Object Discovery Scene Segmentation

Learning by Directional Gradient Descent

no code implementations ICLR 2022 David Silver, Anirudh Goyal, Ivo Danihelka, Matteo Hessel, Hado van Hasselt

How should state be constructed from a sequence of observations, so as to best achieve some objective?

Discrete-Valued Neural Communication

no code implementations NeurIPS 2021 Dianbo Liu, Alex Lamb, Kenji Kawaguchi, Anirudh Goyal, Chen Sun, Michael Curtis Mozer, Yoshua Bengio

Deep learning has advanced from fully connected architectures to structured models organized into components, e. g., the transformer composed of positional elements, modular architectures divided into slots, and graph neural nets made up of nodes.

Quantization Systematic Generalization

Variational Causal Networks: Approximate Bayesian Inference over Causal Structures

1 code implementation14 Jun 2021 Yashas Annadani, Jonas Rothfuss, Alexandre Lacoste, Nino Scherrer, Anirudh Goyal, Yoshua Bengio, Stefan Bauer

However, a crucial aspect to acting intelligently upon the knowledge about causal structure which has been inferred from finite data demands reasoning about its uncertainty.

Bayesian Inference Causal Inference +2

Robust Representation Learning via Perceptual Similarity Metrics

no code implementations11 Jun 2021 Saeid Asgari Taghanaki, Kristy Choi, Amir Khasahmadi, Anirudh Goyal

A fundamental challenge in artificial intelligence is learning useful representations of data that yield good performance on a downstream task, without overfitting to spurious input features.

Out-of-Distribution Generalization Representation Learning

Fast and Slow Learning of Recurrent Independent Mechanisms

no code implementations18 May 2021 Kanika Madan, Nan Rosemary Ke, Anirudh Goyal, Bernhard Schölkopf, Yoshua Bengio

To study these ideas, we propose a particular training framework in which we assume that the pieces of knowledge an agent needs and its reward function are stationary and can be re-used across tasks.

Meta-Learning

Transformers with Competitive Ensembles of Independent Mechanisms

no code implementations27 Feb 2021 Alex Lamb, Di He, Anirudh Goyal, Guolin Ke, Chien-Feng Liao, Mirco Ravanelli, Yoshua Bengio

In this work we explore a way in which the Transformer architecture is deficient: it represents each position with a large monolithic hidden representation and a single set of parameters which are applied over the entire hidden representation.

Speech Enhancement

Learning Task-Relevant Features via Contrastive Input Morphing

no code implementations1 Jan 2021 Saeid Asgari, Kristy Choi, Amir Hosein Khasahmadi, Anirudh Goyal

A fundamental challenge in artificial intelligence is learning useful representations of data that yield good performance on a downstream classification task, without overfitting to spurious input features.

Representation Learning

Spatially Structured Recurrent Modules

no code implementations ICLR 2021 Nasim Rahaman, Anirudh Goyal, Muhammad Waleed Gondal, Manuel Wuthrich, Stefan Bauer, Yash Sharma, Yoshua Bengio, Bernhard Schölkopf

Capturing the structure of a data-generating process by means of appropriate inductive biases can help in learning models that generalise well and are robust to changes in the input distribution.

Starcraft II Video Prediction

Dependency Structure Discovery from Interventions

no code implementations1 Jan 2021 Nan Rosemary Ke, Olexa Bilaniuk, Anirudh Goyal, Stefan Bauer, Bernhard Schölkopf, Michael Curtis Mozer, Hugo Larochelle, Christopher Pal, Yoshua Bengio

Promising results have driven a recent surge of interest in continuous optimization methods for Bayesian network structure learning from observational data.

Factorizing Declarative and Procedural Knowledge in Structured, Dynamical Environments

no code implementations ICLR 2021 Anirudh Goyal, Alex Lamb, Phanideep Gampa, Philippe Beaudoin, Charles Blundell, Sergey Levine, Yoshua Bengio, Michael Curtis Mozer

To use a video game as an illustration, two enemies of the same type will share schemata but will have separate object files to encode their distinct state (e. g., health, position).

Inductive Biases for Deep Learning of Higher-Level Cognition

no code implementations30 Nov 2020 Anirudh Goyal, Yoshua Bengio

A fascinating hypothesis is that human and animal intelligence could be explained by a few principles (rather than an encyclopedic list of heuristics).

Systematic Generalization

Neural Function Modules with Sparse Arguments: A Dynamic Approach to Integrating Information across Layers

no code implementations15 Oct 2020 Alex Lamb, Anirudh Goyal, Agnieszka Słowik, Michael Mozer, Philippe Beaudoin, Yoshua Bengio

Feed-forward neural networks consist of a sequence of layers, in which each layer performs some processing on the information from the previous layer.

Domain Generalization

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

no code implementations ICLR 2021 Ossama Ahmed, Frederik Träuble, Anirudh Goyal, Alexander Neitz, Yoshua Bengio, Bernhard Schölkopf, Manuel Wüthrich, Stefan Bauer

To facilitate research addressing this problem, we propose CausalWorld, a benchmark for causal structure and transfer learning in a robotic manipulation environment.

Transfer Learning

S2RMs: Spatially Structured Recurrent Modules

no code implementations13 Jul 2020 Nasim Rahaman, Anirudh Goyal, Muhammad Waleed Gondal, Manuel Wuthrich, Stefan Bauer, Yash Sharma, Yoshua Bengio, Bernhard Schölkopf

Capturing the structure of a data-generating process by means of appropriate inductive biases can help in learning models that generalize well and are robust to changes in the input distribution.

Starcraft II Video Prediction

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

1 code implementation ICML 2020 Sarthak Mittal, Alex Lamb, Anirudh Goyal, Vikram Voleti, Murray Shanahan, Guillaume Lajoie, Michael Mozer, Yoshua Bengio

To effectively utilize the wealth of potential top-down information available, and to prevent the cacophony of intermixed signals in a bidirectional architecture, mechanisms are needed to restrict information flow.

Language Modelling Sequential Image Classification +1

Uniform Priors for Data-Efficient Transfer

no code implementations30 Jun 2020 Samarth Sinha, Karsten Roth, Anirudh Goyal, Marzyeh Ghassemi, Hugo Larochelle, Animesh Garg

Deep Neural Networks have shown great promise on a variety of downstream applications; but their ability to adapt and generalize to new data and tasks remains a challenge.

Domain Adaptation Meta-Learning +1

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

no code implementations29 Jun 2020 Anirudh Goyal, Alex Lamb, Phanideep Gampa, Philippe Beaudoin, Sergey Levine, Charles Blundell, Yoshua Bengio, Michael Mozer

To use a video game as an illustration, two enemies of the same type will share schemata but will have separate object files to encode their distinct state (e. g., health, position).

Untangling tradeoffs between recurrence and self-attention in neural networks

no code implementations16 Jun 2020 Giancarlo Kerg, Bhargav Kanuparthi, Anirudh Goyal, Kyle Goyette, Yoshua Bengio, Guillaume Lajoie

Attention and self-attention mechanisms, are now central to state-of-the-art deep learning on sequential tasks.

Learning the Arrow of Time for Problems in Reinforcement Learning

no code implementations ICLR 2020 Nasim Rahaman, Steffen Wolf, Anirudh Goyal, Roman Remme, Yoshua Bengio

We humans have an innate understanding of the asymmetric progression of time, which we use to efficiently and safely perceive and manipulate our environment.

reinforcement-learning

The Variational Bandwidth Bottleneck: Stochastic Evaluation on an Information Budget

1 code implementation ICLR 2020 Anirudh Goyal, Yoshua Bengio, Matthew Botvinick, Sergey Levine

This is typically the case when we have a standard conditioning input, such as a state observation, and a "privileged" input, which might correspond to the goal of a task, the output of a costly planning algorithm, or communication with another agent.

reinforcement-learning Variational Inference

Diversity inducing Information Bottleneck in Model Ensembles

1 code implementation10 Mar 2020 Samarth Sinha, Homanga Bharadhwaj, Anirudh Goyal, Hugo Larochelle, Animesh Garg, Florian Shkurti

Although deep learning models have achieved state-of-the-art performance on a number of vision tasks, generalization over high dimensional multi-modal data, and reliable predictive uncertainty estimation are still active areas of research.

Out-of-Distribution Detection

Top-k Training of GANs: Improving GAN Performance by Throwing Away Bad Samples

2 code implementations NeurIPS 2020 Samarth Sinha, Zhengli Zhao, Anirudh Goyal, Colin Raffel, Augustus Odena

We introduce a simple (one line of code) modification to the Generative Adversarial Network (GAN) training algorithm that materially improves results with no increase in computational cost: When updating the generator parameters, we simply zero out the gradient contributions from the elements of the batch that the critic scores as `least realistic'.

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

no code implementations ICML 2020 Samarth Sinha, Han Zhang, Anirudh Goyal, Yoshua Bengio, Hugo Larochelle, Augustus Odena

Recent work by Brock et al. (2018) suggests that Generative Adversarial Networks (GANs) benefit disproportionately from large mini-batch sizes.

Active Learning Anomaly Detection +1

Learning Neural Causal Models from Unknown Interventions

2 code implementations2 Oct 2019 Nan Rosemary Ke, Olexa Bilaniuk, Anirudh Goyal, Stefan Bauer, Hugo Larochelle, Bernhard Schölkopf, Michael C. Mozer, Chris Pal, Yoshua Bengio

Promising results have driven a recent surge of interest in continuous optimization methods for Bayesian network structure learning from observational data.

Meta-Learning

Recurrent Independent Mechanisms

4 code implementations ICLR 2021 Anirudh Goyal, Alex Lamb, Jordan Hoffmann, Shagun Sodhani, Sergey Levine, Yoshua Bengio, Bernhard Schölkopf

Learning modular structures which reflect the dynamics of the environment can lead to better generalization and robustness to changes which only affect a few of the underlying causes.

Learning the Arrow of Time

no code implementations2 Jul 2019 Nasim Rahaman, Steffen Wolf, Anirudh Goyal, Roman Remme, Yoshua Bengio

We humans seem to have an innate understanding of the asymmetric progression of time, which we use to efficiently and safely perceive and manipulate our environment.

Learning Powerful Policies by Using Consistent Dynamics Model

1 code implementation11 Jun 2019 Shagun Sodhani, Anirudh Goyal, Tristan Deleu, Yoshua Bengio, Sergey Levine, Jian Tang

There is enough evidence that humans build a model of the environment, not only by observing the environment but also by interacting with the environment.

Atari Games Model-based Reinforcement Learning

Transfer and Exploration via the Information Bottleneck

1 code implementation ICLR 2019 Anirudh Goyal, Riashat Islam, DJ Strouse, Zafarali Ahmed, Hugo Larochelle, Matthew Botvinick, Yoshua Bengio, Sergey Levine

In new environments, this model can then identify novel subgoals for further exploration, guiding the agent through a sequence of potential decision states and through new regions of the state space.

EnGAN: Latent Space MCMC and Maximum Entropy Generators for Energy-based Models

no code implementations ICLR 2019 Rithesh Kumar, Anirudh Goyal, Aaron Courville, Yoshua Bengio

Unsupervised learning is about capturing dependencies between variables and is driven by the contrast between the probable vs improbable configurations of these variables, often either via a generative model which only samples probable ones or with an energy function (unnormalized log-density) which is low for probable ones and high for improbable ones.

Anomaly Detection

Leveraging Communication Topologies Between Learning Agents in Deep Reinforcement Learning

no code implementations16 Feb 2019 Dhaval Adjodah, Dan Calacci, Abhimanyu Dubey, Anirudh Goyal, Peter Krafft, Esteban Moro, Alex Pentland

A common technique to improve learning performance in deep reinforcement learning (DRL) and many other machine learning algorithms is to run multiple learning agents in parallel.

reinforcement-learning

InfoBot: Transfer and Exploration via the Information Bottleneck

no code implementations30 Jan 2019 Anirudh Goyal, Riashat Islam, Daniel Strouse, Zafarali Ahmed, Matthew Botvinick, Hugo Larochelle, Yoshua Bengio, Sergey Levine

In new environments, this model can then identify novel subgoals for further exploration, guiding the agent through a sequence of potential decision states and through new regions of the state space.

Maximum Entropy Generators for Energy-Based Models

2 code implementations24 Jan 2019 Rithesh Kumar, Sherjil Ozair, Anirudh Goyal, Aaron Courville, Yoshua Bengio

Maximum likelihood estimation of energy-based models is a challenging problem due to the intractability of the log-likelihood gradient.

Anomaly Detection

Learning powerful policies and better dynamics models by encouraging consistency

no code implementations27 Sep 2018 Shagun Sodhani, Anirudh Goyal, Tristan Deleu, Yoshua Bengio, Jian Tang

Analogously, we would expect such interaction to be helpful for a learning agent while learning to model the environment dynamics.

Model-based Reinforcement Learning

Sparse Attentive Backtracking: Temporal CreditAssignment Through Reminding

no code implementations11 Sep 2018 Nan Rosemary Ke, Anirudh Goyal, Olexa Bilaniuk, Jonathan Binas, Michael C. Mozer, Chris Pal, Yoshua Bengio

We consider the hypothesis that such memory associations between past and present could be used for credit assignment through arbitrarily long sequences, propagating the credit assigned to the current state to the associated past state.

Generalization of Equilibrium Propagation to Vector Field Dynamics

3 code implementations14 Aug 2018 Benjamin Scellier, Anirudh Goyal, Jonathan Binas, Thomas Mesnard, Yoshua Bengio

The biological plausibility of the backpropagation algorithm has long been doubted by neuroscientists.

ACtuAL: Actor-Critic Under Adversarial Learning

no code implementations13 Nov 2017 Anirudh Goyal, Nan Rosemary Ke, Alex Lamb, R. Devon Hjelm, Chris Pal, Joelle Pineau, Yoshua Bengio

This makes it fundamentally difficult to train GANs with discrete data, as generation in this case typically involves a non-differentiable function.

Language Modelling

Variational Walkback: Learning a Transition Operator as a Stochastic Recurrent Net

1 code implementation NeurIPS 2017 Anirudh Goyal, Nan Rosemary Ke, Surya Ganguli, Yoshua Bengio

The energy function is then modified so the model and data distributions match, with no guarantee on the number of steps required for the Markov chain to converge.

Sparse Attentive Backtracking: Long-Range Credit Assignment in Recurrent Networks

no code implementations ICLR 2018 Nan Rosemary Ke, Anirudh Goyal, Olexa Bilaniuk, Jonathan Binas, Laurent Charlin, Chris Pal, Yoshua Bengio

A major drawback of backpropagation through time (BPTT) is the difficulty of learning long-term dependencies, coming from having to propagate credit information backwards through every single step of the forward computation.

Professor Forcing: A New Algorithm for Training Recurrent Networks

1 code implementation NeurIPS 2016 Alex Lamb, Anirudh Goyal, Ying Zhang, Saizheng Zhang, Aaron Courville, Yoshua Bengio

We introduce the Professor Forcing algorithm, which uses adversarial domain adaptation to encourage the dynamics of the recurrent network to be the same when training the network and when sampling from the network over multiple time steps.

Domain Adaptation Handwriting generation +2

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