Anirudh Goyal

Affiliations:
  • Mila - Quebec AI Institute, Montreal, QC, Canada
  • University of Montreal, QC, Canada


According to our database1, Anirudh Goyal authored at least 110 papers between 2014 and 2024.

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Bibliography

2024
Physical Reasoning and Object Planning for Household Embodied Agents.
Trans. Mach. Learn. Res., 2024

α-TCVAE: On the relationship between Disentanglement and Diversity.
CoRR, 2024

SWE-Search: Enhancing Software Agents with Monte Carlo Tree Search and Iterative Refinement.
CoRR, 2024

Object-Centric Temporal Consistency via Conditional Autoregressive Inductive Biases.
CoRR, 2024

COrAL: Order-Agnostic Language Modeling for Efficient Iterative Refinement.
CoRR, 2024

Masked Generative Priors Improve World Models Sequence Modelling Capabilities.
CoRR, 2024

Can Models Learn Skill Composition from Examples?
CoRR, 2024

Instruct-SkillMix: A Powerful Pipeline for LLM Instruction Tuning.
CoRR, 2024

Narrowing the Focus: Learned Optimizers for Pretrained Models.
CoRR, 2024

Zero-Shot Object-Centric Representation Learning.
CoRR, 2024

The Llama 3 Herd of Models.
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et al.
CoRR, 2024

AI-Assisted Generation of Difficult Math Questions.
CoRR, 2024

Bayesian-LoRA: LoRA based Parameter Efficient Fine-Tuning using Optimal Quantization levels and Rank Values trough Differentiable Bayesian Gates.
CoRR, 2024

Learning Beyond Pattern Matching? Assaying Mathematical Understanding in LLMs.
CoRR, 2024

Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving.
CoRR, 2024

Monte Carlo Tree Search Boosts Reasoning via Iterative Preference Learning.
CoRR, 2024

Accelerating Greedy Coordinate Gradient via Probe Sampling.
CoRR, 2024

Keeping LLMs Aligned After Fine-tuning: The Crucial Role of Prompt Templates.
CoRR, 2024

Can AI Be as Creative as Humans?
CoRR, 2024

SKILL-MIX: a Flexible and Expandable Family of Evaluations for AI Models.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

αTC-VAE: On the relationship between Disentanglement and Diversity.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Cycle Consistency Driven Object Discovery.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Reasoning Robustness of LLMs to Adversarial Typographical Errors.
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

2023
A novel privacy protection approach with better human imperceptibility.
Appl. Intell., October, 2023

Neural Causal Structure Discovery from Interventions.
Trans. Mach. Learn. Res., 2023

Unlearning via Sparse Representations.
CoRR, 2023

A Theory for Emergence of Complex Skills in Language Models.
CoRR, 2023

Spotlight Attention: Robust Object-Centric Learning With a Spatial Locality Prior.
CoRR, 2023

DiscoGen: Learning to Discover Gene Regulatory Networks.
CoRR, 2023

Leveraging the Third Dimension in Contrastive Learning.
CoRR, 2023

Discrete Key-Value Bottleneck.
Proceedings of the International Conference on Machine Learning, 2023

Test-time Adaptation with Slot-Centric Models.
Proceedings of the International Conference on Machine Learning, 2023

GFlowOut: Dropout with Generative Flow Networks.
Proceedings of the International Conference on Machine Learning, 2023

Stateful Active Facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learning.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Learning to Induce Causal Structure.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Representation Learning in Deep RL via Discrete Information Bottleneck.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022
Discrete Factorial Representations as an Abstraction for Goal Conditioned Reinforcement Learning.
CoRR, 2022

On the Generalization and Adaption Performance of Causal Models.
CoRR, 2022

Temporal Latent Bottleneck: Synthesis of Fast and Slow Processing Mechanisms in Sequence Learning.
CoRR, 2022

Coordinating Policies Among Multiple Agents via an Intelligent Communication Channel.
CoRR, 2022

Learning to Induce Causal Structure.
CoRR, 2022

Generating Fast and Slow: Scene Decomposition via Reconstruction.
CoRR, 2022

Retrieval-Augmented Reinforcement Learning.
CoRR, 2022

Discrete Compositional Representations as an Abstraction for Goal Conditioned Reinforcement Learning.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Temporal Latent Bottleneck: Synthesis of Fast and Slow Processing Mechanisms in Sequence Learning.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022


Learning by Directional Gradient Descent.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Coordination Among Neural Modules Through a Shared Global Workspace.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Uniform Priors for Data-Efficient Learning.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022

2021
Toward Causal Representation Learning.
Proc. IEEE, 2021

Learning Neural Causal Models with Active Interventions.
CoRR, 2021

Variational Causal Networks: Approximate Bayesian Inference over Causal Structures.
CoRR, 2021

Transformers with Competitive Ensembles of Independent Mechanisms.
CoRR, 2021

Towards Causal Representation Learning.
CoRR, 2021

Discrete-Valued Neural Communication.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning.
Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

Neural Production Systems.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021


On Disentangled Representations Learned from Correlated Data.
Proceedings of the 38th International Conference on Machine Learning, 2021

Robust Representation Learning via Perceptual Similarity Metrics.
Proceedings of the 38th International Conference on Machine Learning, 2021

Spatially Structured Recurrent Modules.
Proceedings of the 9th International Conference on Learning Representations, 2021

Fast And Slow Learning Of Recurrent Independent Mechanisms.
Proceedings of the 9th International Conference on Learning Representations, 2021

Recurrent Independent Mechanisms.
Proceedings of the 9th International Conference on Learning Representations, 2021

Factorizing Declarative and Procedural Knowledge in Structured, Dynamical Environments.
Proceedings of the 9th International Conference on Learning Representations, 2021

CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning.
Proceedings of the 9th International Conference on Learning Representations, 2021

Neural Function Modules with Sparse Arguments: A Dynamic Approach to Integrating Information across Layers.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

DIBS: Diversity Inducing Information Bottleneck in Model Ensembles.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020
Inductive Biases for Deep Learning of Higher-Level Cognition.
CoRR, 2020

S2RMs: Spatially Structured Recurrent Modules.
CoRR, 2020

Maximum Entropy Models for Fast Adaptation.
CoRR, 2020

Object Files and Schemata: Factorizing Declarative and Procedural Knowledge in Dynamical Systems.
CoRR, 2020

Untangling tradeoffs between recurrence and self-attention in neural networks.
CoRR, 2020

Is Independence all you need? On the Generalization of Representations Learned from Correlated Data.
CoRR, 2020

Top-K Training of GANs: Improving Generators by Making Critics Less Critical.
CoRR, 2020

Top-k Training of GANs: Improving GAN Performance by Throwing Away Bad Samples.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Untangling tradeoffs between recurrence and self-attention in artificial neural networks.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Small-GAN: Speeding up GAN Training using Core-Sets.
Proceedings of the 37th International Conference on Machine Learning, 2020

Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules.
Proceedings of the 37th International Conference on Machine Learning, 2020

Learning the Arrow of Time for Problems in Reinforcement Learning.
Proceedings of the 8th International Conference on Learning Representations, 2020

Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives.
Proceedings of the 8th International Conference on Learning Representations, 2020

The Variational Bandwidth Bottleneck: Stochastic Evaluation on an Information Budget.
Proceedings of the 8th International Conference on Learning Representations, 2020

A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms.
Proceedings of the 8th International Conference on Learning Representations, 2020

Leveraging Communication Topologies Between Learning Agents in Deep Reinforcement Learning.
Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, 2020

2019
Learning Neural Causal Models from Unknown Interventions.
CoRR, 2019

Learning the Arrow of Time.
CoRR, 2019

Learning Powerful Policies by Using Consistent Dynamics Model.
CoRR, 2019

State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations.
CoRR, 2019

Learning Dynamics Model in Reinforcement Learning by Incorporating the Long Term Future.
CoRR, 2019

Communication Topologies Between Learning Agents in Deep Reinforcement Learning.
CoRR, 2019

Maximum Entropy Generators for Energy-Based Models.
CoRR, 2019

State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations.
Proceedings of the 36th International Conference on Machine Learning, 2019

Modeling the Long Term Future in Model-Based Reinforcement Learning.
Proceedings of the 7th International Conference on Learning Representations, 2019

InfoBot: Transfer and Exploration via the Information Bottleneck.
Proceedings of the 7th International Conference on Learning Representations, 2019

Recall Traces: Backtracking Models for Efficient Reinforcement Learning.
Proceedings of the 7th International Conference on Learning Representations, 2019

2018
Sparse Attentive Backtracking: Temporal CreditAssignment Through Reminding.
CoRR, 2018

Generalization of Equilibrium Propagation to Vector Field Dynamics.
CoRR, 2018

Fortified Networks: Improving the Robustness of Deep Networks by Modeling the Manifold of Hidden Representations.
CoRR, 2018

Recall Traces: Backtracking Models for Efficient Reinforcement Learning.
CoRR, 2018

Sparse Attentive Backtracking: Temporal Credit Assignment Through Reminding.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Extending the Framework of Equilibrium Propagation to General Dynamics.
Proceedings of the 6th International Conference on Learning Representations, 2018

2017
ACtuAL: Actor-Critic Under Adversarial Learning.
CoRR, 2017

Sparse Attentive Backtracking: Long-Range Credit Assignment in Recurrent Networks.
CoRR, 2017

Z-Forcing: Training Stochastic Recurrent Networks.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Variational Walkback: Learning a Transition Operator as a Stochastic Recurrent Net.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations.
Proceedings of the 5th International Conference on Learning Representations, 2017

An Actor-Critic Algorithm for Sequence Prediction.
Proceedings of the 5th International Conference on Learning Representations, 2017

2016
Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations.
CoRR, 2016

Professor Forcing: A New Algorithm for Training Recurrent Networks.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2015
Stories in the Eye: Contextual Visual Interactions for Efficient Video to Language Translation.
CoRR, 2015

2014
Monocular vision based road marking recognition for driver assistance and safety.
Proceedings of the IEEE International Conference on Vehicular Electronics and Safety, 2014


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