5.7k citations · 17.6k across the 75 of their papers we have counts for
76 papers
Teaching Algorithmic Reasoning via In-context Learning
Hattie Zhou, Azade Nova, Hugo Larochelle +3
Large language models (LLMs) have shown increasing in-context learning capabilities through scaling up model and data size. Despite this progress, LLMs are still unable to solve al…
Repository-Level Prompt Generation for Large Language Models of Code
Disha Shrivastava, Hugo Larochelle, Daniel Tarlow
With the success of large language models (LLMs) of code and their use as code assistants (e.g. Codex used in GitHub Copilot), techniques for introducing domain-specific knowledge…
Matching Feature Sets for Few-Shot Image Classification
Arman Afrasiyabi, Hugo Larochelle, Jean-François Lalonde +1
In image classification, it is common practice to train deep networks to extract a single feature vector per input image. Few-shot classification methods also mostly follow this tr…
Static Prediction of Runtime Errors by Learning to Execute Programs with External Resource Descriptions
David Bieber, Rishab Goel, Daniel Zheng +2
The execution behavior of a program often depends on external resources, such as program inputs or file contents, and so cannot be run in isolation. Nevertheless, software develope…
Fortuitous Forgetting in Connectionist Networks
Hattie Zhou, Ankit Vani, Hugo Larochelle +1
Forgetting is often seen as an unwanted characteristic in both human and machine learning. However, we propose that forgetting can in fact be favorable to learning. We introduce "f…
Head2Toe: Utilizing Intermediate Representations for Better Transfer Learning
Utku Evci, Vincent Dumoulin, Hugo Larochelle +1
Transfer-learning methods aim to improve performance in a data-scarce target domain using a model pretrained on a data-rich source domain. A cost-efficient strategy, linear probing…
Impact of Aliasing on Generalization in Deep Convolutional Networks
Cristina Vasconcelos, Hugo Larochelle, Vincent Dumoulin +3
We investigate the impact of aliasing on generalization in Deep Convolutional Networks and show that data augmentation schemes alone are unable to prevent it due to structural limi…
Learning a Universal Template for Few-shot Dataset Generalization
Eleni Triantafillou, Hugo Larochelle, Richard Zemel +1
Few-shot dataset generalization is a challenging variant of the well-studied few-shot classification problem where a diverse training set of several datasets is given, for the purp…
Learning to Combine Per-Example Solutions for Neural Program Synthesis
Disha Shrivastava, Hugo Larochelle, Daniel Tarlow
The goal of program synthesis from examples is to find a computer program that is consistent with a given set of input-output examples. Most learning-based approaches try to find a…
Comparing Transfer and Meta Learning Approaches on a Unified Few-Shot Classification Benchmark
Vincent Dumoulin, Neil Houlsby, Utku Evci +4
Meta and transfer learning are two successful families of approaches to few-shot learning. Despite highly related goals, state-of-the-art advances in each family are measured large…
Interpretable Multi-Modal Hate Speech Detection
Prashanth Vijayaraghavan, Hugo Larochelle, Deb Roy
With growing role of social media in shaping public opinions and beliefs across the world, there has been an increased attention to identify and counter the problem of hate speech…
Self-Supervised Equivariant Scene Synthesis from Video
Cinjon Resnick, Or Litany, Cosmas Heiß +3
We propose a self-supervised framework to learn scene representations from video that are automatically delineated into background, characters, and their animations. Our method cap…
An Effective Anti-Aliasing Approach for Residual Networks
Cristina Vasconcelos, Hugo Larochelle, Vincent Dumoulin +2
Image pre-processing in the frequency domain has traditionally played a vital role in computer vision and was even part of the standard pipeline in the early days of deep learning.…
Learned Equivariant Rendering without Transformation Supervision
Cinjon Resnick, Or Litany, Hugo Larochelle +2
We propose a self-supervised framework to learn scene representations from video that are automatically delineated into objects and background. Our method relies on moving objects…
Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks
David Bieber, Charles Sutton, Hugo Larochelle +1
Graph neural networks (GNNs) have emerged as a powerful tool for learning software engineering tasks including code completion, bug finding, and program repair. They benefit from l…
Revisiting Fundamentals of Experience Replay
William Fedus, Prajit Ramachandran, Rishabh Agarwal +4
Experience replay is central to off-policy algorithms in deep reinforcement learning (RL), but there remain significant gaps in our understanding. We therefore present a systematic…
Learning Graph Structure With A Finite-State Automaton Layer
Daniel D. Johnson, Hugo Larochelle, Daniel Tarlow
Graph-based neural network models are producing strong results in a number of domains, in part because graphs provide flexibility to encode domain knowledge in the form of relation…
A Universal Representation Transformer Layer for Few-Shot Image Classification
Lu Liu, William Hamilton, Guodong Long +2
Few-shot classification aims to recognize unseen classes when presented with only a small number of samples. We consider the problem of multi-domain few-shot image classification,…
Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)
Joelle Pineau, Philippe Vincent-Lamarre, Koustuv Sinha +5
One of the challenges in machine learning research is to ensure that presented and published results are sound and reliable. Reproducibility, that is obtaining similar results as p…
On-the-Fly Adaptation of Source Code Models using Meta-Learning
Disha Shrivastava, Hugo Larochelle, Daniel Tarlow
The ability to adapt to unseen, local contexts is an important challenge that successful models of source code must overcome. One of the most popular approaches for the adaptation…
Your GAN is Secretly an Energy-based Model and You Should use Discriminator Driven Latent Sampling
Tong Che, Ruixiang Zhang, Jascha Sohl-Dickstein +4
We show that the sum of the implicit generator log-density of a GAN with the logit score of the discriminator defines an energy function which yields the true data densi…
Diversity inducing Information Bottleneck in Model Ensembles
Samarth Sinha, Homanga Bharadhwaj, Anirudh Goyal +3
Although deep learning models have achieved state-of-the-art performance on a number of vision tasks, generalization over high dimensional multi-modal data, and reliable predictive…
Curriculum By Smoothing
Samarth Sinha, Animesh Garg, Hugo Larochelle
Convolutional Neural Networks (CNNs) have shown impressive performance in computer vision tasks such as image classification, detection, and segmentation. Moreover, recent work in…
On Catastrophic Interference in Atari 2600 Games
William Fedus, Dibya Ghosh, John D. Martin +3
Model-free deep reinforcement learning is sample inefficient. One hypothesis -- speculated, but not confirmed -- is that catastrophic interference within an environment inhibits le…