Search Results for author: Alex Lamb

Found 49 papers, 25 papers with code

Video Occupancy Models

1 code implementation25 Jun 2024 Manan Tomar, Philippe Hansen-Estruch, Philip Bachman, Alex Lamb, John Langford, Matthew E. Taylor, Sergey Levine

We introduce a new family of video prediction models designed to support downstream control tasks.

Video Prediction

Generalizing Multi-Step Inverse Models for Representation Learning to Finite-Memory POMDPs

no code implementations22 Apr 2024 Lili Wu, Ben Evans, Riashat Islam, Raihan Seraj, Yonathan Efroni, Alex Lamb

In this work, we consider the problem of discovering the agent-centric state in the more challenging high-dimensional non-Markovian setting, when the state can be decoded from a sequence of past observations.

Representation Learning

Towards Principled Representation Learning from Videos for Reinforcement Learning

1 code implementation20 Mar 2024 Dipendra Misra, Akanksha Saran, Tengyang Xie, Alex Lamb, John Langford

We study two types of settings: one where there is iid noise in the observation, and a more challenging setting where there is also the presence of exogenous noise, which is non-iid noise that is temporally correlated, such as the motion of people or cars in the background.

Contrastive Learning reinforcement-learning +1

Can AI Be as Creative as Humans?

no code implementations3 Jan 2024 Haonan Wang, James Zou, Michael Mozer, Anirudh Goyal, Alex Lamb, Linjun Zhang, Weijie J Su, Zhun Deng, Michael Qizhe Xie, Hannah Brown, Kenji Kawaguchi

With the rise of advanced generative AI models capable of tasks once reserved for human creativity, the study of AI's creative potential becomes imperative for its responsible development and application.

Leveraging the Third Dimension in Contrastive Learning

no code implementations27 Jan 2023 Sumukh Aithal, Anirudh Goyal, Alex Lamb, Yoshua Bengio, Michael Mozer

We evaluate these two approaches on three different SSL methods -- BYOL, SimSiam, and SwAV -- using ImageNette (10 class subset of ImageNet), ImageNet-100 and ImageNet-1k datasets.

Contrastive Learning Depth Estimation +2

Towards Data-Driven Offline Simulations for Online Reinforcement Learning

1 code implementation14 Nov 2022 Shengpu Tang, Felipe Vieira Frujeri, Dipendra Misra, Alex Lamb, John Langford, Paul Mineiro, Sebastian Kochman

Modern decision-making systems, from robots to web recommendation engines, are expected to adapt: to user preferences, changing circumstances or even new tasks.

Decision Making reinforcement-learning +1

Neural Active Learning on Heteroskedastic Distributions

1 code implementation2 Nov 2022 Savya Khosla, Chew Kin Whye, Jordan T. Ash, Cyril Zhang, Kenji Kawaguchi, Alex Lamb

To this end, we demonstrate the catastrophic failure of these active learning algorithms on heteroskedastic distributions and propose a fine-tuning-based approach to mitigate these failures.

Active Learning

Discrete Factorial Representations as an Abstraction for Goal Conditioned Reinforcement Learning

no code implementations1 Nov 2022 Riashat Islam, Hongyu Zang, Anirudh Goyal, Alex Lamb, Kenji Kawaguchi, Xin Li, Romain Laroche, Yoshua Bengio, Remi Tachet des Combes

Goal-conditioned reinforcement learning (RL) is a promising direction for training agents that are capable of solving multiple tasks and reach a diverse set of objectives.

reinforcement-learning Reinforcement Learning (RL)

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

1 code implementation31 Oct 2022 Riashat Islam, Manan Tomar, Alex Lamb, Yonathan Efroni, Hongyu Zang, Aniket Didolkar, Dipendra Misra, Xin Li, Harm van Seijen, Remi Tachet des Combes, John Langford

We find that contemporary representation learning techniques can fail on datasets where the noise is a complex and time dependent process, which is prevalent in practical applications.

Offline RL Reinforcement Learning (RL) +1

Guaranteed Discovery of Control-Endogenous Latent States with Multi-Step Inverse Models

no code implementations17 Jul 2022 Alex Lamb, Riashat Islam, Yonathan Efroni, Aniket Didolkar, Dipendra Misra, Dylan Foster, Lekan Molu, Rajan Chari, Akshay Krishnamurthy, John Langford

In many sequential decision-making tasks, the agent is not able to model the full complexity of the world, which consists of multitudes of relevant and irrelevant information.

Decision Making

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

2 code implementations30 May 2022 Aniket Didolkar, Kshitij Gupta, Anirudh Goyal, Nitesh B. Gundavarapu, Alex Lamb, Nan Rosemary Ke, Yoshua Bengio

A slow stream that is recurrent in nature aims to learn a specialized and compressed representation, by forcing chunks of $K$ time steps into a single representation which is divided into multiple vectors.

Decision Making Inductive Bias

Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization

no code implementations2 Feb 2022 Dianbo Liu, Alex Lamb, Xu Ji, Pascal Notsawo, Mike Mozer, Yoshua Bengio, Kenji Kawaguchi

Vector Quantization (VQ) is a method for discretizing latent representations and has become a major part of the deep learning toolkit.

Quantization reinforcement-learning +2

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

A Brief Introduction to Generative Models

no code implementations27 Feb 2021 Alex Lamb

We overview how generative modeling can be defined mathematically as trying to make an estimating distribution the same as an unknown ground truth distribution.

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

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).

Object

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

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 Open-Ended Question Answering +2

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).

Object

Jigsaw-VAE: Towards Balancing Features in Variational Autoencoders

no code implementations12 May 2020 Saeid Asgari Taghanaki, Mohammad Havaei, Alex Lamb, Aditya Sanghi, Ara Danielyan, Tonya Custis

The latent variables learned by VAEs have seen considerable interest as an unsupervised way of extracting features, which can then be used for downstream tasks.

KaoKore: A Pre-modern Japanese Art Facial Expression Dataset

1 code implementation20 Feb 2020 Yingtao Tian, Chikahiko Suzuki, Tarin Clanuwat, Mikel Bober-Irizar, Alex Lamb, Asanobu Kitamoto

From classifying handwritten digits to generating strings of text, the datasets which have received long-time focus from the machine learning community vary greatly in their subject matter.

BIG-bench Machine Learning Image Classification

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

1 code implementation25 Dec 2019 Alex Lamb, Sherjil Ozair, Vikas Verma, David Ha

In this work we focus on their ability to have invariance towards the presence or absence of details.

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

no code implementations21 Oct 2019 Tarin Clanuwat, Alex Lamb, Asanobu Kitamoto

However, following a change to the Japanese writing system in 1900, Kuzushiji has not been included in regular school curricula.

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

no code implementations25 Sep 2019 Vikas Verma, Meng Qu, Alex Lamb, Yoshua Bengio, Juho Kannala, Jian Tang

We present GraphMix, a regularization technique for Graph Neural Network based semi-supervised object classification, leveraging the recent advances in the regularization of classical deep neural networks.

Graph Neural Network

GraphMix: Improved Training of GNNs for Semi-Supervised Learning

1 code implementation25 Sep 2019 Vikas Verma, Meng Qu, Kenji Kawaguchi, Alex Lamb, Yoshua Bengio, Juho Kannala, Jian Tang

We present GraphMix, a regularization method for Graph Neural Network based semi-supervised object classification, whereby we propose to train a fully-connected network jointly with the graph neural network via parameter sharing and interpolation-based regularization.

Generalization Bounds Graph Attention +2

Recurrent Independent Mechanisms

3 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.

Atari Games

Manifold Mixup: Learning Better Representations by Interpolating Hidden States

1 code implementation ICLR 2019 Vikas Verma, Alex Lamb, Christopher Beckham, Amir Najafi, Aaron Courville, Ioannis Mitliagkas, Yoshua Bengio

Because the hidden states are learned, this has an important effect of encouraging the hidden states for a class to be concentrated in such a way so that interpolations within the same class or between two different classes do not intersect with the real data points from other classes.

Adversarial Mixup Resynthesizers

1 code implementation ICLR Workshop DeepGenStruct 2019 Christopher Beckham, Sina Honari, Alex Lamb, Vikas Verma, Farnoosh Ghadiri, R Devon Hjelm, Christopher Pal

In this paper, we explore new approaches to combining information encoded within the learned representations of autoencoders.

Interpolation Consistency Training for Semi-Supervised Learning

4 code implementations9 Mar 2019 Vikas Verma, Kenji Kawaguchi, Alex Lamb, Juho Kannala, Arno Solin, Yoshua Bengio, David Lopez-Paz

We introduce Interpolation Consistency Training (ICT), a simple and computation efficient algorithm for training Deep Neural Networks in the semi-supervised learning paradigm.

General Classification Semi-Supervised Image Classification

On Adversarial Mixup Resynthesis

1 code implementation NeurIPS 2019 Christopher Beckham, Sina Honari, Vikas Verma, Alex Lamb, Farnoosh Ghadiri, R. Devon Hjelm, Yoshua Bengio, Christopher Pal

In this paper, we explore new approaches to combining information encoded within the learned representations of auto-encoders.

Resynthesis

Deep Learning for Classical Japanese Literature

10 code implementations3 Dec 2018 Tarin Clanuwat, Mikel Bober-Irizar, Asanobu Kitamoto, Alex Lamb, Kazuaki Yamamoto, David Ha

Much of machine learning research focuses on producing models which perform well on benchmark tasks, in turn improving our understanding of the challenges associated with those tasks.

BIG-bench Machine Learning Image Classification

Manifold Mixup: Better Representations by Interpolating Hidden States

12 code implementations ICLR 2019 Vikas Verma, Alex Lamb, Christopher Beckham, Amir Najafi, Ioannis Mitliagkas, Aaron Courville, David Lopez-Paz, Yoshua Bengio

Deep neural networks excel at learning the training data, but often provide incorrect and confident predictions when evaluated on slightly different test examples.

Image Classification

Learning Generative Models with Locally Disentangled Latent Factors

no code implementations ICLR 2018 Brady Neal, Alex Lamb, Sherjil Ozair, Devon Hjelm, Aaron Courville, Yoshua Bengio, Ioannis Mitliagkas

One of the most successful techniques in generative models has been decomposing a complicated generation task into a series of simpler generation tasks.

GibbsNet: Iterative Adversarial Inference for Deep Graphical Models

no code implementations NeurIPS 2017 Alex Lamb, Devon Hjelm, Yaroslav Ganin, Joseph Paul Cohen, Aaron Courville, Yoshua Bengio

Directed latent variable models that formulate the joint distribution as $p(x, z) = p(z) p(x \mid z)$ have the advantage of fast and exact sampling.

Attribute

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

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

Adversarially Learned Inference

9 code implementations2 Jun 2016 Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Olivier Mastropietro, Alex Lamb, Martin Arjovsky, Aaron Courville

We introduce the adversarially learned inference (ALI) model, which jointly learns a generation network and an inference network using an adversarial process.

Image-to-Image Translation

Theano: A Python framework for fast computation of mathematical expressions

1 code implementation9 May 2016 The Theano Development Team, Rami Al-Rfou, Guillaume Alain, Amjad Almahairi, Christof Angermueller, Dzmitry Bahdanau, Nicolas Ballas, Frédéric Bastien, Justin Bayer, Anatoly Belikov, Alexander Belopolsky, Yoshua Bengio, Arnaud Bergeron, James Bergstra, Valentin Bisson, Josh Bleecher Snyder, Nicolas Bouchard, Nicolas Boulanger-Lewandowski, Xavier Bouthillier, Alexandre de Brébisson, Olivier Breuleux, Pierre-Luc Carrier, Kyunghyun Cho, Jan Chorowski, Paul Christiano, Tim Cooijmans, Marc-Alexandre Côté, Myriam Côté, Aaron Courville, Yann N. Dauphin, Olivier Delalleau, Julien Demouth, Guillaume Desjardins, Sander Dieleman, Laurent Dinh, Mélanie Ducoffe, Vincent Dumoulin, Samira Ebrahimi Kahou, Dumitru Erhan, Ziye Fan, Orhan Firat, Mathieu Germain, Xavier Glorot, Ian Goodfellow, Matt Graham, Caglar Gulcehre, Philippe Hamel, Iban Harlouchet, Jean-Philippe Heng, Balázs Hidasi, Sina Honari, Arjun Jain, Sébastien Jean, Kai Jia, Mikhail Korobov, Vivek Kulkarni, Alex Lamb, Pascal Lamblin, Eric Larsen, César Laurent, Sean Lee, Simon Lefrancois, Simon Lemieux, Nicholas Léonard, Zhouhan Lin, Jesse A. Livezey, Cory Lorenz, Jeremiah Lowin, Qianli Ma, Pierre-Antoine Manzagol, Olivier Mastropietro, Robert T. McGibbon, Roland Memisevic, Bart van Merriënboer, Vincent Michalski, Mehdi Mirza, Alberto Orlandi, Christopher Pal, Razvan Pascanu, Mohammad Pezeshki, Colin Raffel, Daniel Renshaw, Matthew Rocklin, Adriana Romero, Markus Roth, Peter Sadowski, John Salvatier, François Savard, Jan Schlüter, John Schulman, Gabriel Schwartz, Iulian Vlad Serban, Dmitriy Serdyuk, Samira Shabanian, Étienne Simon, Sigurd Spieckermann, S. Ramana Subramanyam, Jakub Sygnowski, Jérémie Tanguay, Gijs van Tulder, Joseph Turian, Sebastian Urban, Pascal Vincent, Francesco Visin, Harm de Vries, David Warde-Farley, Dustin J. Webb, Matthew Willson, Kelvin Xu, Lijun Xue, Li Yao, Saizheng Zhang, Ying Zhang

Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements.

BIG-bench Machine Learning Clustering +2

Discriminative Regularization for Generative Models

1 code implementation9 Feb 2016 Alex Lamb, Vincent Dumoulin, Aaron Courville

We propose to take advantage of this by using the representations from discriminative classifiers to augment the objective function corresponding to a generative model.

Variance Reduction in SGD by Distributed Importance Sampling

1 code implementation20 Nov 2015 Guillaume Alain, Alex Lamb, Chinnadhurai Sankar, Aaron Courville, Yoshua Bengio

This leads the model to update using an unbiased estimate of the gradient which also has minimum variance when the sampling proposal is proportional to the L2-norm of the gradient.

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