Hugo Larochelle

research

∙ 05/01/2023

Bird Distribution Modelling using Remote Sensing and Citizen Science data

Climate change is a major driver of biodiversity loss, changing the geog...

0 Mélisande Teng, et al. ∙

research

∙ 11/15/2022

Teaching Algorithmic Reasoning via In-context Learning

Large language models (LLMs) have shown increasing in-context learning c...

0 Hattie Zhou, et al. ∙

research

∙ 06/26/2022

Repository-Level Prompt Generation for Large Language Models of Code

With the success of large language models (LLMs) of code and their use a...

0 Disha Shrivastava, et al. ∙

research

∙ 04/02/2022

Matching Feature Sets for Few-Shot Image Classification

In image classification, it is common practice to train deep networks to...

0 Arman Afrasiyabi, et al. ∙

research

∙ 03/07/2022

Static Prediction of Runtime Errors by Learning to Execute Programs with External Resource Descriptions

The execution behavior of a program often depends on external resources,...

0 David Bieber, et al. ∙

research

∙ 02/01/2022

Fortuitous Forgetting in Connectionist Networks

Forgetting is often seen as an unwanted characteristic in both human and...

0 Hattie Zhou, et al. ∙

research

∙ 01/10/2022

Head2Toe: Utilizing Intermediate Representations for Better Transfer Learning

Transfer-learning methods aim to improve performance in a data-scarce ta...

0 Utku Evci, et al. ∙

research

∙ 08/07/2021

Impact of Aliasing on Generalization in Deep Convolutional Networks

We investigate the impact of aliasing on generalization in Deep Convolut...

19 Cristina Vasconcelos, et al. ∙

research

∙ 06/14/2021

Learning to Combine Per-Example Solutions for Neural Program Synthesis

The goal of program synthesis from examples is to find a computer progra...

0 Disha Shrivastava, et al. ∙

research

∙ 05/14/2021

Learning a Universal Template for Few-shot Dataset Generalization

Few-shot dataset generalization is a challenging variant of the well-stu...

15 Eleni Triantafillou, et al. ∙

research

∙ 04/06/2021

Comparing Transfer and Meta Learning Approaches on a Unified Few-Shot Classification Benchmark

Meta and transfer learning are two successful families of approaches to ...

8 Vincent Dumoulin, et al. ∙

research

∙ 03/02/2021

Interpretable Multi-Modal Hate Speech Detection

With growing role of social media in shaping public opinions and beliefs...

0 Prashanth Vijayaraghavan, et al. ∙

research

∙ 02/01/2021

Self-Supervised Equivariant Scene Synthesis from Video

We propose a self-supervised framework to learn scene representations fr...

8 Cinjon Resnick, et al. ∙

research

∙ 11/11/2020

Learned Equivariant Rendering without Transformation Supervision

We propose a self-supervised framework to learn scene representations fr...

4 Cinjon Resnick, et al. ∙

research

∙ 10/23/2020

Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks

Graph neural networks (GNNs) have emerged as a powerful tool for learnin...

8 David Bieber, et al. ∙

research

∙ 07/13/2020

Revisiting Fundamentals of Experience Replay

Experience replay is central to off-policy algorithms in deep reinforcem...

5 William Fedus, et al. ∙

research

∙ 07/09/2020

Learning Graph Structure With A Finite-State Automaton Layer

Graph-based neural network models are producing strong results in a numb...

9 Daniel D. Johnson, et al. ∙

research

∙ 06/21/2020

A Universal Representation Transformer Layer for Few-Shot Image Classification

Few-shot classification aims to recognize unseen classes when presented ...

0 Lu Liu, et al. ∙

research

∙ 03/27/2020

Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)

One of the challenges in machine learning research is to ensure that pre...

0 Joelle Pineau, et al. ∙

research

∙ 03/26/2020

On-the-Fly Adaptation of Source Code Models using Meta-Learning

The ability to adapt to unseen, local contexts is an important challenge...

5 Disha Shrivastava, et al. ∙

research

∙ 03/12/2020

Your GAN is Secretly an Energy-based Model and You Should use Discriminator Driven Latent Sampling

We show that the sum of the implicit generator log-density log p_g of a ...

24 Tong Che, et al. ∙

research

∙ 03/10/2020

DIBS: Diversity inducing Information Bottleneck in Model Ensembles

Although deep learning models have achieved state-of-the-art performance...

7 Samarth Sinha, et al. ∙

research

∙ 03/03/2020

Curriculum By Texture

Convolutional Neural Networks (CNNs) have shown impressive performance i...

5 Samarth Sinha, et al. ∙

research

∙ 02/28/2020

On Catastrophic Interference in Atari 2600 Games

Model-free deep reinforcement learning algorithms are troubled with poor...

8 William Fedus, et al. ∙

research

∙ 11/28/2019

Algorithmic Improvements for Deep Reinforcement Learning applied to Interactive Fiction

Text-based games are a natural challenge domain for deep reinforcement l...

3 Vishal Jain, et al. ∙

research

∙ 10/29/2019

Small-GAN: Speeding Up GAN Training Using Core-sets

Recent work by Brock et al. (2018) suggests that Generative Adversarial ...

22 Samarth Sinha, et al. ∙

research

∙ 10/02/2019

Learning Neural Causal Models from Unknown Interventions

Meta-learning over a set of distributions can be interpreted as learning...

31 Nan Rosemary Ke, et al. ∙

research

∙ 03/18/2019

A RAD approach to deep mixture models

Flow based models such as Real NVP are an extremely powerful approach to...

4 Laurent Dinh, et al. ∙

research

∙ 03/07/2019

Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples

Few-shot classification refers to learning a classifier for new classes ...

43 Eleni Triantafillou, et al. ∙

research

∙ 02/22/2019

Centroid Networks for Few-Shot Clustering and Unsupervised Few-Shot Classification

Traditional clustering algorithms such as K-means rely heavily on the na...

1 Gabriel Huang, et al. ∙

research

∙ 02/19/2019

Hyperbolic Discounting and Learning over Multiple Horizons

Reinforcement learning (RL) typically defines a discount factor as part ...

4 William Fedus, et al. ∙

research

∙ 02/01/2019

The Hanabi Challenge: A New Frontier for AI Research

From the early days of computing, games have been important testbeds for...

54 Nolan Bard, et al. ∙

research

∙ 01/30/2019

InfoBot: Transfer and Exploration via the Information Bottleneck

A central challenge in reinforcement learning is discovering effective p...

18 Anirudh Goyal, et al. ∙

research

∙ 11/12/2018

Blindfold Baselines for Embodied QA

We explore blindfold (question-only) baselines for Embodied Question Ans...

6 Ankesh Anand, et al. ∙

research

∙ 11/06/2018

Language GANs Falling Short

Generating high-quality text with sufficient diversity is essential for ...

1 Massimo Caccia, et al. ∙

research

∙ 04/02/2018

Recall Traces: Backtracking Models for Efficient Reinforcement Learning

In many environments only a tiny subset of all states yield high reward....

0 Anirudh Goyal, et al. ∙

research

∙ 03/02/2018

Meta-Learning for Semi-Supervised Few-Shot Classification

In few-shot classification, we are interested in learning algorithms tha...

0 Mengye Ren, et al. ∙

research

∙ 02/26/2018

Disentangling the independently controllable factors of variation by interacting with the world

It has been postulated that a good representation is one that disentangl...

0 Valentin Thomas, et al. ∙

research

∙ 11/29/2017

HoME: a Household Multimodal Environment

We introduce HoME: a Household Multimodal Environment for artificial age...

0 Simon Brodeur, et al. ∙

research

∙ 07/03/2017

Multiscale sequence modeling with a learned dictionary

We propose a generalization of neural network sequence models. Instead o...

0 Bart van Merriënboer, et al. ∙

research

∙ 07/02/2017

Modulating early visual processing by language

It is commonly assumed that language refers to high-level visual concept...

0 Harm de Vries, et al. ∙

research

∙ 11/23/2016

GuessWhat?! Visual object discovery through multi-modal dialogue

We introduce GuessWhat?!, a two-player guessing game as a testbed for re...

0 Harm de Vries, et al. ∙

research

∙ 07/18/2016

Deep learning trends for focal brain pathology segmentation in MRI

Segmentation of focal (localized) brain pathologies such as brain tumors...

0 Mohammad Havaei, et al. ∙

research

∙ 05/24/2016

Hierarchical Memory Networks

Memory networks are neural networks with an explicit memory component th...

0 Sarath Chandar, et al. ∙

research

∙ 05/12/2016

Movie Description

Audio Description (AD) provides linguistic descriptions of movies and al...

0 Anna Rohrbach, et al. ∙

research

∙ 05/07/2016

Neural Autoregressive Distribution Estimation

We present Neural Autoregressive Distribution Estimation (NADE) models, ...

0 Benigno Uria, et al. ∙

research

∙ 03/27/2016

Recurrent Mixture Density Network for Spatiotemporal Visual Attention

In many computer vision tasks, the relevant information to solve the pro...

0 Loris Bazzani, et al. ∙

research

∙ 03/18/2016

Document Neural Autoregressive Distribution Estimation

We present an approach based on feed-forward neural networks for learnin...

0 Stanislas Lauly, et al. ∙

research

∙ 12/31/2015

Autoencoding beyond pixels using a learned similarity metric

We present an autoencoder that leverages learned representations to bett...

0 Anders Boesen Lindbo Larsen, et al. ∙

research

∙ 11/24/2015

Dynamic Capacity Networks

We introduce the Dynamic Capacity Network (DCN), a neural network that c...

0 Amjad Almahairi, et al. ∙

Hugo Larochelle

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