Alex Lamb

According to our database1, Alex Lamb authored at least 60 papers between 2012 and 2024.

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Bibliography

2024
Video Occupancy Models.
CoRR, 2024

Generalizing Multi-Step Inverse Models for Representation Learning to Finite-Memory POMDPs.
CoRR, 2024

Can AI Be as Creative as Humans?
CoRR, 2024

PcLast: Discovering Plannable Continuous Latent States.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Towards Principled Representation Learning from Videos for Reinforcement Learning.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023
Guaranteed Discovery of Control-Endogenous Latent States with Multi-Step Inverse Models.
Trans. Mach. Learn. Res., 2023

Leveraging the Third Dimension in Contrastive Learning.
CoRR, 2023

Principled Offline RL in the Presence of Rich Exogenous Information.
Proceedings of the International Conference on Machine Learning, 2023

Understanding and Improving Neural Active Learning on Heteroskedastic Distributions.
Proceedings of the ECAI 2023 - 26th European Conference on Artificial Intelligence, September 30 - October 4, 2023, Kraków, Poland, 2023

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

Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization for Heterogeneous Representational Coarseness.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022
Interpolation consistency training for semi-supervised learning.
Neural Networks, 2022

Interpolated Adversarial Training: Achieving robust neural networks without sacrificing too much accuracy.
Neural Networks, 2022

Towards Data-Driven Offline Simulations for Online Reinforcement Learning.
CoRR, 2022

Neural Active Learning on Heteroskedastic Distributions.
CoRR, 2022

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

Agent-Controller Representations: Principled Offline RL with Rich Exogenous Information.
CoRR, 2022

CNT (Conditioning on Noisy Targets): A new Algorithm for Leveraging Top-Down Feedback.
CoRR, 2022

Guaranteed Discovery of Controllable Latent States with Multi-Step Inverse Models.
CoRR, 2022

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

Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization.
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

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

2021
Predicting the Ordering of Characters in Japanese Historical Documents.
CoRR, 2021

Transformers with Competitive Ensembles of Independent Mechanisms.
CoRR, 2021

A Brief Introduction to Generative Models.
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

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

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

GraphMix: Improved Training of GNNs for Semi-Supervised Learning.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020
KuroNet: Regularized Residual U-Nets for End-to-End Kuzushiji Character Recognition.
SN Comput. Sci., 2020

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

Jigsaw-VAE: Towards Balancing Features in Variational Autoencoders.
CoRR, 2020

SketchTransfer: A Challenging New Task for Exploring Detail-Invariance and the Abstractions Learned by Deep Networks.
Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 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

KaoKore: A Pre-modern Japanese Art Facial Expression Dataset.
Proceedings of the Eleventh International Conference on Computational Creativity, 2020

2019
GraphMix: Regularized Training of Graph Neural Networks for Semi-Supervised Learning.
CoRR, 2019

Interpolated Adversarial Training: Achieving Robust Neural Networks without Sacrificing Accuracy.
CoRR, 2019

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

On Adversarial Mixup Resynthesis.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Interpolation Consistency Training for Semi-supervised Learning.
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Manifold Mixup: Better Representations by Interpolating Hidden States.
Proceedings of the 36th International Conference on Machine Learning, 2019

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

Adversarial Mixup Resynthesizers.
Proceedings of the Deep Generative Models for Highly Structured Data, 2019

KuroNet: Pre-Modern Japanese Kuzushiji Character Recognition with Deep Learning.
Proceedings of the 2019 International Conference on Document Analysis and Recognition, 2019

Interpolated Adversarial Training: Achieving Robust Neural Networks Without Sacrificing Too Much Accuracy.
Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security, 2019

2018
Deep Learning for Classical Japanese Literature.
CoRR, 2018

Manifold Mixup: Encouraging Meaningful On-Manifold Interpolation as a Regularizer.
CoRR, 2018

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

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

GibbsNet: Iterative Adversarial Inference for Deep Graphical Models.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Adversarially Learned Inference.
Proceedings of the 5th International Conference on Learning Representations, 2017

2016
Discriminative Regularization for Generative Models.
CoRR, 2016

Theano: A Python framework for fast computation of mathematical expressions.
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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
Variance Reduction in SGD by Distributed Importance Sampling.
CoRR, 2015

2013
Separating Fact from Fear: Tracking Flu Infections on Twitter.
Proceedings of the Human Language Technologies: Conference of the North American Chapter of the Association of Computational Linguistics, 2013

2012
Investigating Twitter as a Source for Studying Behavioral Responses to Epidemics.
Proceedings of the Information Retrieval and Knowledge Discovery in Biomedical Text, 2012


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