b'Alex Lamb'

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

∙ 11/14/2022

Towards Data-Driven Offline Simulations for Online Reinforcement Learning

Modern decision-making systems, from robots to web recommendation engine...

0 Shengpu Tang, et al. ∙

research

∙ 11/02/2022

Neural Active Learning on Heteroskedastic Distributions

Models that can actively seek out the best quality training data hold th...

0 Savya Khosla, 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

∙ 10/31/2022

Agent-Controller Representations: Principled Offline RL with Rich Exogenous Information

Learning to control an agent from data collected offline in a rich pixel...

0 Riashat Islam, et al. ∙

research

∙ 10/18/2022

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

We propose a novel regularizer for supervised learning called Conditioni...

11 Alexia Jolicoeur-Martineau, et al. ∙

research

∙ 07/17/2022

Guaranteed Discovery of Controllable Latent States with Multi-Step Inverse Models

A person walking along a city street who tries to model all aspects of t...

17 Alex Lamb, 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

∙ 02/02/2022

Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization

Vector Quantization (VQ) is a method for discretizing latent representat...

5 Dianbo Liu, 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

∙ 06/12/2021

Predicting the Ordering of Characters in Japanese Historical Documents

Japan is a unique country with a distinct cultural heritage, which is re...

0 Alex Lamb, 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

∙ 02/27/2021

A Brief Introduction to Generative Models

We introduce and motivate generative modeling as a central task for mach...

0 Alex Lamb, 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

∙ 06/29/2020

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

∙ 05/12/2020

Jigsaw-VAE: Towards Balancing Features in Variational Autoencoders

The latent variables learned by VAEs have seen considerable interest as ...

5 Saeid Asgari Taghanaki, et al. ∙

research

∙ 02/20/2020

KaoKore: A Pre-modern Japanese Art Facial Expression Dataset

From classifying handwritten digits to generating strings of text, the d...

21 Yingtao Tian, et al. ∙

research

∙ 12/25/2019

SketchTransfer: A Challenging New Task for Exploring Detail-Invariance and the Abstractions Learned by Deep Networks

Deep networks have achieved excellent results in perceptual tasks, yet t...

40 Alex Lamb, et al. ∙

research

∙ 10/21/2019

KuroNet: Pre-Modern Japanese Kuzushiji Character Recognition with Deep Learning

Kuzushiji, a cursive writing style, had been used in Japan for over a th...

26 Tarin Clanuwat, et al. ∙

research

∙ 09/25/2019

GraphMix: Regularized Training of Graph Neural Networks for Semi-Supervised Learning

We present GraphMix, a regularization technique for Graph Neural Network...

4 Vikas Verma, 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

∙ 06/16/2019

Interpolated Adversarial Training: Achieving Robust Neural Networks without Sacrificing Accuracy

Adversarial robustness has become a central goal in deep learning, both ...

11 Alex Lamb, 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

∙ 03/09/2019

Interpolation Consistency Training for Semi-Supervised Learning

We introduce Interpolation Consistency Training (ICT), a simple and comp...

10 Vikas Verma, et al. ∙

research

∙ 03/07/2019

Adversarial Mixup Resynthesizers

In this paper, we explore new approaches to combining information encode...

12 Christopher Beckham, et al. ∙

research

∙ 12/03/2018

Deep Learning for Classical Japanese Literature

Much of machine learning research focuses on producing models which perf...

0 Tarin Clanuwat, et al. ∙

research

∙ 06/13/2018

Manifold Mixup: Encouraging Meaningful On-Manifold Interpolation as a Regularizer

Deep networks often perform well on the data manifold on which they are ...

0 Vikas Verma, et al. ∙

research

∙ 04/07/2018

Fortified Networks: Improving the Robustness of Deep Networks by Modeling the Manifold of Hidden Representations

Deep networks have achieved impressive results across a variety of impor...

0 Alex Lamb, et al. ∙

research

∙ 12/12/2017

GibbsNet: Iterative Adversarial Inference for Deep Graphical Models

Directed latent variable models that formulate the joint distribution as...

0 Alex Lamb, et al. ∙

research

∙ 11/13/2017

ACtuAL: Actor-Critic Under Adversarial Learning

Generative Adversarial Networks (GANs) are a powerful framework for deep...

0 Anirudh Goyal, et al. ∙

research

∙ 10/27/2016

Professor Forcing: A New Algorithm for Training Recurrent Networks

The Teacher Forcing algorithm trains recurrent networks by supplying obs...

0 Alex Lamb, et al. ∙

research

∙ 06/02/2016

Adversarially Learned Inference

We introduce the adversarially learned inference (ALI) model, which join...

0 Vincent Dumoulin, et al. ∙

research

∙ 05/09/2016

Theano: A Python framework for fast computation of mathematical expressions

Theano is a Python library that allows to define, optimize, and evaluate...

0 The Theano Development Team, et al. ∙

research

∙ 02/09/2016

Discriminative Regularization for Generative Models

We explore the question of whether the representations learned by classi...

0 Alex Lamb, et al. ∙

research

∙ 11/20/2015

Variance Reduction in SGD by Distributed Importance Sampling

Humans are able to accelerate their learning by selecting training mater...

0 Guillaume Alain, et al. ∙

Alex Lamb

Featured Co-authors