Search Results for author: Christopher Pal

Found 78 papers, 40 papers with code

Block-State Transformer

no code implementations15 Jun 2023 Mahan Fathi, Jonathan Pilault, Pierre-Luc Bacon, Christopher Pal, Orhan Firat, Ross Goroshin

Originally designed for continuous signals, SSMs have shown superior performance on a plethora of tasks, in vision and audio; however, SSMs still lag Transformer performance in Language Modeling tasks.

Language Modelling

Goal-conditioned GFlowNets for Controllable Multi-Objective Molecular Design

no code implementations7 Jun 2023 Julien Roy, Pierre-Luc Bacon, Christopher Pal, Emmanuel Bengio

In recent years, in-silico molecular design has received much attention from the machine learning community.

Improving Generalization in Task-oriented Dialogues with Workflows and Action Plans

no code implementations2 Jun 2023 Stefania Raimondo, Christopher Pal, Xiaotian Liu, David Vazquez, Hector Palacios

We perform extensive experiments on the Action-Based Conversations Dataset (ABCD) with T5-small, base and large models, and show that such models: a) are able to more readily generalize to unseen workflows by following the provided plan, and b) are able to generalize to executing unseen actions if they are provided in the plan.

Are Diffusion Models Vision-And-Language Reasoners?

1 code implementation25 May 2023 Benno Krojer, Elinor Poole-Dayan, Vikram Voleti, Christopher Pal, Siva Reddy

We also measure the stereotypical bias in diffusion models, and find that Stable Diffusion 2. 1 is, for the most part, less biased than Stable Diffusion 1. 5.

Denoising Image Generation +2

Conservative objective models are a special kind of contrastive divergence-based energy model

1 code implementation7 Apr 2023 Christopher Beckham, Christopher Pal

In this work we theoretically show that conservative objective models (COMs) for offline model-based optimisation (MBO) are a special kind of contrastive divergence-based energy model, one where the energy function represents both the unconditional probability of the input and the conditional probability of the reward variable.

Score-based Diffusion Models in Function Space

no code implementations14 Feb 2023 Jae Hyun Lim, Nikola B. Kovachki, Ricardo Baptista, Christopher Beckham, Kamyar Azizzadenesheli, Jean Kossaifi, Vikram Voleti, Jiaming Song, Karsten Kreis, Jan Kautz, Christopher Pal, Arash Vahdat, Anima Anandkumar

They consist of a forward process that perturbs input data with Gaussian white noise and a reverse process that learns a score function to generate samples by denoising.


Long-Context Language Decision Transformers and Exponential Tilt for Interactive Text Environments

no code implementations10 Feb 2023 Nicolas Gontier, Pau Rodriguez, Issam Laradji, David Vazquez, Christopher Pal

LLDTs extend DTs with 3 components: (1) exponential tilt to guide the agent towards high obtainable goals, (2) novel goal conditioning methods yielding significantly better results than the traditional return-to-go (sum of all future rewards), and (3) a model of future observations.

Offline RL

Receptive Field Refinement for Convolutional Neural Networks Reliably Improves Predictive Performance

no code implementations26 Nov 2022 Mats L. Richter, Christopher Pal

By further developing and formalizing the analysis of receptive field expansion in convolutional neural networks, we can predict unproductive layers in an automated manner before ever training a model.

Exploring validation metrics for offline model-based optimisation

no code implementations19 Nov 2022 Christopher Beckham, Alexandre Piche, David Vazquez, Christopher Pal

Offline MBO tries to approximate this expensive scoring function and use that to evaluate generated designs, however evaluation is non-exact because one approximation is being evaluated with another.

Denoising Model Selection

Attention-based Neural Cellular Automata

no code implementations2 Nov 2022 Mattie Tesfaldet, Derek Nowrouzezahrai, Christopher Pal

We introduce an instance of this class named $\textit{Vision Transformer Cellular Automata}$ (ViTCA).


Bridging the Gap Between Target Networks and Functional Regularization

no code implementations21 Oct 2022 Alexandre Piche, Valentin Thomas, Joseph Marino, Rafael Pardinas, Gian Maria Marconi, Christopher Pal, Mohammad Emtiyaz Khan

However, learning the value function via bootstrapping often leads to unstable training due to fast-changing target values.

Score-based Denoising Diffusion with Non-Isotropic Gaussian Noise Models

no code implementations21 Oct 2022 Vikram Voleti, Christopher Pal, Adam Oberman

Generative models based on denoising diffusion techniques have led to an unprecedented increase in the quality and diversity of imagery that is now possible to create with neural generative models.


Using Graph Algorithms to Pretrain Graph Completion Transformers

no code implementations14 Oct 2022 Jonathan Pilault, Michael Galkin, Bahare Fatemi, Perouz Taslakian, David Vasquez, Christopher Pal

While using our new path-finding algorithm as a pretraining signal provides 2-3% MRR improvements, we show that pretraining on all signals together gives the best knowledge graph completion results.

Knowledge Graph Completion Knowledge Graph Embedding +1

SMPL-IK: Learned Morphology-Aware Inverse Kinematics for AI Driven Artistic Workflows

no code implementations16 Aug 2022 Vikram Voleti, Boris N. Oreshkin, Florent Bocquelet, Félix G. Harvey, Louis-Simon Ménard, Christopher Pal

Inverse Kinematics (IK) systems are often rigid with respect to their input character, thus requiring user intervention to be adapted to new skeletons.

Pose Estimation

Improving Meta-Learning Generalization with Activation-Based Early-Stopping

1 code implementation3 Aug 2022 Simon Guiroy, Christopher Pal, Gonçalo Mordido, Sarath Chandar

Specifically, we analyze the evolution, during meta-training, of the neural activations at each hidden layer, on a small set of unlabelled support examples from a single task of the target tasks distribution, as this constitutes a minimal and justifiably accessible information from the target problem.

Few-Shot Learning Transfer Learning

Overcoming challenges in leveraging GANs for few-shot data augmentation

1 code implementation30 Mar 2022 Christopher Beckham, Issam Laradji, Pau Rodriguez, David Vazquez, Derek Nowrouzezahrai, Christopher Pal

In this paper, we explore the use of GAN-based few-shot data augmentation as a method to improve few-shot classification performance.

Classification Data Augmentation +1

Does Entity Abstraction Help Generative Transformers Reason?

no code implementations5 Jan 2022 Nicolas Gontier, Siva Reddy, Christopher Pal

We study the utility of incorporating entity type abstractions into pre-trained Transformers and test these methods on four NLP tasks requiring different forms of logical reasoning: (1) compositional language understanding with text-based relational reasoning (CLUTRR), (2) abductive reasoning (ProofWriter), (3) multi-hop question answering (HotpotQA), and (4) conversational question answering (CoQA).

Conversational Question Answering Logical Reasoning +2

Direct Behavior Specification via Constrained Reinforcement Learning

1 code implementation22 Dec 2021 Julien Roy, Roger Girgis, Joshua Romoff, Pierre-Luc Bacon, Christopher Pal

The standard formulation of Reinforcement Learning lacks a practical way of specifying what are admissible and forbidden behaviors.

Continuous Control reinforcement-learning +1

Learning to Guide and to Be Guided in the Architect-Builder Problem

1 code implementation ICLR 2022 Paul Barde, Tristan Karch, Derek Nowrouzezahrai, Clément Moulin-Frier, Christopher Pal, Pierre-Yves Oudeyer

ABIG results in a low-level, high-frequency, guiding communication protocol that not only enables an architect-builder pair to solve the task at hand, but that can also generalize to unseen tasks.

Imitation Learning

Early-Stopping for Meta-Learning: Estimating Generalization from the Activation Dynamics

no code implementations29 Sep 2021 Simon Guiroy, Christopher Pal, Sarath Chandar

To this end, we empirically show that as meta-training progresses, a model's generalization to a target distribution of novel tasks can be estimated by analysing the dynamics of its neural activations.

Few-Shot Learning Transfer Learning

Simple Video Generation using Neural ODEs

no code implementations7 Sep 2021 David Kanaa, Vikram Voleti, Samira Ebrahimi Kahou, Christopher Pal

Despite having been studied to a great extent, the task of conditional generation of sequences of frames, or videos, remains extremely challenging.

Video Generation

Multi-Resolution Continuous Normalizing Flows

1 code implementation15 Jun 2021 Vikram Voleti, Chris Finlay, Adam Oberman, Christopher Pal

In this work we introduce a Multi-Resolution variant of such models (MRCNF), by characterizing the conditional distribution over the additional information required to generate a fine image that is consistent with the coarse image.

Density Estimation Image Generation

Bridging the Gap Between Target Networks and Functional Regularization

1 code implementation4 Jun 2021 Alexandre Piché, Valentin Thomas, Rafael Pardinas, Joseph Marino, Gian Maria Marconi, Christopher Pal, Mohammad Emtiyaz Khan

Our findings emphasize that Functional Regularization can be used as a drop-in replacement for Target Networks and result in performance improvement.


Improving Continuous Normalizing Flows using a Multi-Resolution Framework

no code implementations ICML Workshop INNF 2021 Vikram Voleti, Chris Finlay, Adam M Oberman, Christopher Pal

Recent work has shown that Continuous Normalizing Flows (CNFs) can serve as generative models of images with exact likelihood calculation and invertible generation/density estimation.

Density Estimation

Robust Motion In-betweening

1 code implementation9 Feb 2021 Félix G. Harvey, Mike Yurick, Derek Nowrouzezahrai, Christopher Pal

To quantitatively evaluate performance on transitions and generalizations to longer time horizons, we present well-defined in-betweening benchmarks on a subset of the widely used Human3. 6M dataset and on LaFAN1, a novel high quality motion capture dataset that is more appropriate for transition generation.

Human Pose Forecasting motion prediction +1

Mem2Mem: Learning to Summarize Long Texts with Memory Compression and Transfer

no code implementations1 Jan 2021 Jonathan Pilault, Jaehong Park, Christopher Pal

We introduce Mem2Mem, a memory-to-memory mechanism for hierarchical recurrent neural network based encoder decoder architectures and we explore its use for abstractive document summarization.

Abstractive Text Summarization Document Summarization

Visual Imitation with Reinforcement Learning using Recurrent Siamese Networks

no code implementations1 Jan 2021 Glen Berseth, Florian Golemo, Christopher Pal

It would be desirable for a reinforcement learning (RL) based agent to learn behaviour by merely watching a demonstration.

One-Shot Learning reinforcement-learning +1

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.

Bijective-Contrastive Estimation

no code implementations pproximateinference AABI Symposium 2021 Jae Hyun Lim, Chin-wei Huang, Aaron Courville, Christopher Pal

In this work, we propose Bijective-Contrastive Estimation (BCE), a classification-based learning criterion for energy-based models.


Learning to Summarize Long Texts with Memory Compression and Transfer

no code implementations21 Oct 2020 Jaehong Park, Jonathan Pilault, Christopher Pal

We introduce Mem2Mem, a memory-to-memory mechanism for hierarchical recurrent neural network based encoder decoder architectures and we explore its use for abstractive document summarization.

Abstractive Text Summarization Document Summarization

Reinforcement Learning with Random Delays

2 code implementations ICLR 2021 Simon Ramstedt, Yann Bouteiller, Giovanni Beltrame, Christopher Pal, Jonathan Binas

Action and observation delays commonly occur in many Reinforcement Learning applications, such as remote control scenarios.

Anatomy Continuous Control +2

Conditionally Adaptive Multi-Task Learning: Improving Transfer Learning in NLP Using Fewer Parameters & Less Data

1 code implementation ICLR 2021 Jonathan Pilault, Amine Elhattami, Christopher Pal

Through this construction (a hypernetwork adapter), we achieve more efficient parameter sharing and mitigate forgetting by keeping half of the weights of a pretrained model fixed.

Multi-Task Learning Natural Language Inference

Action-Based Representation Learning for Autonomous Driving

1 code implementation21 Aug 2020 Yi Xiao, Felipe Codevilla, Christopher Pal, Antonio M. Lopez

Human drivers produce a vast amount of data which could, in principle, be used to improve autonomous driving systems.

Autonomous Driving Representation Learning

Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization

3 code implementations NeurIPS 2020 Paul Barde, Julien Roy, Wonseok Jeon, Joelle Pineau, Christopher Pal, Derek Nowrouzezahrai

Adversarial Imitation Learning alternates between learning a discriminator -- which tells apart expert's demonstrations from generated ones -- and a generator's policy to produce trajectories that can fool this discriminator.

Imitation Learning reinforcement-learning +1

AR-DAE: Towards Unbiased Neural Entropy Gradient Estimation

2 code implementations ICML 2020 Jae Hyun Lim, Aaron Courville, Christopher Pal, Chin-wei Huang

Entropy is ubiquitous in machine learning, but it is in general intractable to compute the entropy of the distribution of an arbitrary continuous random variable.

Continuous Control Denoising +1

On the impressive performance of randomly weighted encoders in summarization tasks

no code implementations21 Feb 2020 Jonathan Pilault, Jae-hong Park, Christopher Pal

In this work, we investigate the performance of untrained randomly initialized encoders in a general class of sequence to sequence models and compare their performance with that of fully-trained encoders on the task of abstractive summarization.

Abstractive Text Summarization

Structural Inductive Biases in Emergent Communication

no code implementations4 Feb 2020 Agnieszka Słowik, Abhinav Gupta, William L. Hamilton, Mateja Jamnik, Sean B. Holden, Christopher Pal

In order to communicate, humans flatten a complex representation of ideas and their attributes into a single word or a sentence.

Representation Learning

Real-Time Reinforcement Learning

3 code implementations NeurIPS 2019 Simon Ramstedt, Christopher Pal

Markov Decision Processes (MDPs), the mathematical framework underlying most algorithms in Reinforcement Learning (RL), are often used in a way that wrongfully assumes that the state of an agent's environment does not change during action selection.

Continuous Control reinforcement-learning +1

Neural Multisensory Scene Inference

2 code implementations NeurIPS 2019 Jae Hyun Lim, Pedro O. Pinheiro, Negar Rostamzadeh, Christopher Pal, Sungjin Ahn

For embodied agents to infer representations of the underlying 3D physical world they inhabit, they should efficiently combine multisensory cues from numerous trials, e. g., by looking at and touching objects.

Representation Learning

Interactive Machine Comprehension with Information Seeking Agents

1 code implementation ACL 2020 Xingdi Yuan, Jie Fu, Marc-Alexandre Cote, Yi Tay, Christopher Pal, Adam Trischler

Existing machine reading comprehension (MRC) models do not scale effectively to real-world applications like web-level information retrieval and question answering (QA).

Decision Making Information Retrieval +3

Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning

no code implementations NeurIPS 2020 Julien Roy, Paul Barde, Félix G. Harvey, Derek Nowrouzezahrai, Christopher Pal

Finally, we analyze the effects of our proposed methods on the policies that our agents learn and show that our methods successfully enforce the qualities that we propose as proxies for coordinated behaviors.

Continuous Control Inductive Bias +3

Towards Understanding Generalization in Gradient-Based Meta-Learning

no code implementations16 Jul 2019 Simon Guiroy, Vikas Verma, Christopher Pal

We also show that coherence of meta-test gradients, measured by the average inner product between the task-specific gradient vectors evaluated at meta-train solution, is also correlated with generalization.


Supervise Thyself: Examining Self-Supervised Representations in Interactive Environments

2 code implementations27 Jun 2019 Evan Racah, Christopher Pal

Self-supervised methods, wherein an agent learns representations solely by observing the results of its actions, become crucial in environments which do not provide a dense reward signal or have labels.

Imitation Learning

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.

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.


Towards Learning to Imitate from a Single Video Demonstration

no code implementations22 Jan 2019 Glen Berseth, Florian Golemo, Christopher Pal

We approach this challenge using contrastive training to learn a reward function comparing an agent's behaviour with a single demonstration.

Imitation Learning One-Shot Learning +1

The Liver Tumor Segmentation Benchmark (LiTS)

6 code implementations13 Jan 2019 Patrick Bilic, Patrick Christ, Hongwei Bran Li, Eugene Vorontsov, Avi Ben-Cohen, Georgios Kaissis, Adi Szeskin, Colin Jacobs, Gabriel Efrain Humpire Mamani, Gabriel Chartrand, Fabian Lohöfer, Julian Walter Holch, Wieland Sommer, Felix Hofmann, Alexandre Hostettler, Naama Lev-Cohain, Michal Drozdzal, Michal Marianne Amitai, Refael Vivantik, Jacob Sosna, Ivan Ezhov, Anjany Sekuboyina, Fernando Navarro, Florian Kofler, Johannes C. Paetzold, Suprosanna Shit, Xiaobin Hu, Jana Lipková, Markus Rempfler, Marie Piraud, Jan Kirschke, Benedikt Wiestler, Zhiheng Zhang, Christian Hülsemeyer, Marcel Beetz, Florian Ettlinger, Michela Antonelli, Woong Bae, Míriam Bellver, Lei Bi, Hao Chen, Grzegorz Chlebus, Erik B. Dam, Qi Dou, Chi-Wing Fu, Bogdan Georgescu, Xavier Giró-i-Nieto, Felix Gruen, Xu Han, Pheng-Ann Heng, Jürgen Hesser, Jan Hendrik Moltz, Christian Igel, Fabian Isensee, Paul Jäger, Fucang Jia, Krishna Chaitanya Kaluva, Mahendra Khened, Ildoo Kim, Jae-Hun Kim, Sungwoong Kim, Simon Kohl, Tomasz Konopczynski, Avinash Kori, Ganapathy Krishnamurthi, Fan Li, Hongchao Li, Junbo Li, Xiaomeng Li, John Lowengrub, Jun Ma, Klaus Maier-Hein, Kevis-Kokitsi Maninis, Hans Meine, Dorit Merhof, Akshay Pai, Mathias Perslev, Jens Petersen, Jordi Pont-Tuset, Jin Qi, Xiaojuan Qi, Oliver Rippel, Karsten Roth, Ignacio Sarasua, Andrea Schenk, Zengming Shen, Jordi Torres, Christian Wachinger, Chunliang Wang, Leon Weninger, Jianrong Wu, Daguang Xu, Xiaoping Yang, Simon Chun-Ho Yu, Yading Yuan, Miao Yu, Liping Zhang, Jorge Cardoso, Spyridon Bakas, Rickmer Braren, Volker Heinemann, Christopher Pal, An Tang, Samuel Kadoury, Luc Soler, Bram van Ginneken, Hayit Greenspan, Leo Joskowicz, Bjoern Menze

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018.

Benchmarking Computed Tomography (CT) +2

Dataflow-based Joint Quantization of Weights and Activations for Deep Neural Networks

no code implementations4 Jan 2019 Xue Geng, Jie Fu, Bin Zhao, Jie Lin, Mohamed M. Sabry Aly, Christopher Pal, Vijay Chandrasekhar

This paper addresses a challenging problem - how to reduce energy consumption without incurring performance drop when deploying deep neural networks (DNNs) at the inference stage.


Recurrent Transition Networks for Character Locomotion

2 code implementations4 Oct 2018 Félix G. Harvey, Christopher Pal

Manually authoring transition animations for a complete locomotion system can be a tedious and time-consuming task, especially for large games that allow complex and constrained locomotion movements, where the number of transitions grows exponentially with the number of states.


Unsupervised Depth Estimation, 3D Face Rotation and Replacement

1 code implementation NeurIPS 2018 Joel Ruben Antony Moniz, Christopher Beckham, Simon Rajotte, Sina Honari, Christopher Pal

We present an unsupervised approach for learning to estimate three dimensional (3D) facial structure from a single image while also predicting 3D viewpoint transformations that match a desired pose and facial geometry.

Depth Estimation Translation

Improving Landmark Localization with Semi-Supervised Learning

no code implementations CVPR 2018 Sina Honari, Pavlo Molchanov, Stephen Tyree, Pascal Vincent, Christopher Pal, Jan Kautz

First, we propose the framework of sequential multitasking and explore it here through an architecture for landmark localization where training with class labels acts as an auxiliary signal to guide the landmark localization on unlabeled data.

Face Alignment Small Data Image Classification

A step towards procedural terrain generation with GANs

1 code implementation11 Jul 2017 Christopher Beckham, Christopher Pal

Procedural terrain generation for video games has been traditionally been done with smartly designed but handcrafted algorithms that generate heightmaps.

Adversarial Generation of Natural Language

no code implementations WS 2017 Sai Rajeswar, Sandeep Subramanian, Francis Dutil, Christopher Pal, Aaron Courville

Generative Adversarial Networks (GANs) have gathered a lot of attention from the computer vision community, yielding impressive results for image generation.

Image Generation Language Modelling

Unimodal probability distributions for deep ordinal classification

no code implementations ICML 2017 Christopher Beckham, Christopher Pal

Probability distributions produced by the cross-entropy loss for ordinal classification problems can possess undesired properties.

Classification General Classification

ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events

1 code implementation NeurIPS 2017 Evan Racah, Christopher Beckham, Tegan Maharaj, Samira Ebrahimi Kahou, Prabhat, Christopher Pal

We present a dataset, ExtremeWeather, to encourage machine learning research in this area and to help facilitate further work in understanding and mitigating the effects of climate change.

BIG-bench Machine Learning Blocking +1

A simple squared-error reformulation for ordinal classification

1 code implementation2 Dec 2016 Christopher Beckham, Christopher Pal

In this paper, we explore ordinal classification (in the context of deep neural networks) through a simple modification of the squared error loss which not only allows it to not only be sensitive to class ordering, but also allows the possibility of having a discrete probability distribution over the classes.

Classification General Classification

A dataset and exploration of models for understanding video data through fill-in-the-blank question-answering

2 code implementations CVPR 2017 Tegan Maharaj, Nicolas Ballas, Anna Rohrbach, Aaron Courville, Christopher Pal

In addition to presenting statistics and a description of the dataset, we perform a detailed analysis of 5 different models' predictions, and compare these with human performance.

Descriptive Language Modelling +3

Convolutional Residual Memory Networks

no code implementations16 Jun 2016 Joel Moniz, Christopher Pal

Our experiments and analysis explore the importance of the memory mechanism, network depth, breadth, and predictive performance.

Movie Description

no code implementations12 May 2016 Anna Rohrbach, Atousa Torabi, Marcus Rohrbach, Niket Tandon, Christopher Pal, Hugo Larochelle, Aaron Courville, Bernt Schiele

In addition we also collected and aligned movie scripts used in prior work and compare the two sources of descriptions.


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

Recombinator Networks: Learning Coarse-to-Fine Feature Aggregation

1 code implementation CVPR 2016 Sina Honari, Jason Yosinski, Pascal Vincent, Christopher Pal

Deep neural networks with alternating convolutional, max-pooling and decimation layers are widely used in state of the art architectures for computer vision.

Image Classification

Recurrent Semi-supervised Classification and Constrained Adversarial Generation with Motion Capture Data

no code implementations20 Nov 2015 Félix G. Harvey, Julien Roy, David Kanaa, Christopher Pal

We find that using such constraints allow to stabilize the training of recurrent adversarial architectures for animation generation.

Clustering General Classification

Describing Videos by Exploiting Temporal Structure

5 code implementations ICCV 2015 Li Yao, Atousa Torabi, Kyunghyun Cho, Nicolas Ballas, Christopher Pal, Hugo Larochelle, Aaron Courville

In this context, we propose an approach that successfully takes into account both the local and global temporal structure of videos to produce descriptions.

Action Recognition Temporal Action Localization +1

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