Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Centre National de la Recherche ScientifiqueFR2k papers
- University of British ColumbiaCA1.9k papers
- McGill UniversityCA1.9k papers
- University of VictoriaCA1.8k papers
- University of California, Santa CruzUS1.7k papers
- Rutherford Appleton LaboratoryGB1.7k papers
- University of ManchesterGB1.7k papers
- Queen Mary University of LondonGB1.7k papers
- University of PisaIT1.6k papers
- University of EdinburghGB1.6k papers
- University of BirminghamGB1.6k papers
- Heidelberg UniversityDE1.6k papers
24 papers · 2 filters
Quantized Guided Pruning for Efficient Hardware Implementations of Convolutional Neural Networks
Ghouthi Boukli Hacene, Vincent Gripon, Matthieu Arzel +2
Convolutional Neural Networks (CNNs) are state-of-the-art in numerous computer vision tasks such as object classification and detection. However, the large amount of parameters the…
Regularized Binary Network Training
Sajad Darabi, Mouloud Belbahri, Matthieu Courbariaux +1
There is a significant performance gap between Binary Neural Networks (BNNs) and floating point Deep Neural Networks (DNNs). We propose to improve the binary training method, by in…
Greedy Layerwise Learning Can Scale to ImageNet
Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon
Shallow supervised 1-hidden layer neural networks have a number of favorable properties that make them easier to interpret, analyze, and optimize than their deep counterparts, but…
Towards Deep Conversational Recommendations
Raymond Li, Samira Kahou, Hannes Schulz +3
There has been growing interest in using neural networks and deep learning techniques to create dialogue systems. Conversational recommendation is an interesting setting for the sc…
An Empirical Study of Example Forgetting during Deep Neural Network Learning
Mariya Toneva, Alessandro Sordoni, Remi Tachet des Combes +3
Inspired by the phenomenon of catastrophic forgetting, we investigate the learning dynamics of neural networks as they train on single classification tasks. Our goal is to understa…
Unsupervised Control Through Non-Parametric Discriminative Rewards
David Warde-Farley, Tom Van de Wiele, Tejas Kulkarni +3
Learning to control an environment without hand-crafted rewards or expert data remains challenging and is at the frontier of reinforcement learning research. We present an unsuperv…
DEFactor: Differentiable Edge Factorization-based Probabilistic Graph Generation
Rim Assouel, Mohamed Ahmed, Marwin H Segler +2
Generating novel molecules with optimal properties is a crucial step in many industries such as drug discovery. Recently, deep generative models have shown a promising way of perfo…
Improving Explorability in Variational Inference with Annealed Variational Objectives
Chin-Wei Huang, Shawn Tan, Alexandre Lacoste +1
Despite the advances in the representational capacity of approximate distributions for variational inference, the optimization process can still limit the density that is ultimatel…
Automatic differentiation in ML: Where we are and where we should be going
Bart van Merriënboer, Olivier Breuleux, Arnaud Bergeron +1
We review the current state of automatic differentiation (AD) for array programming in machine learning (ML), including the different approaches such as operator overloading (OO) a…
Geometric Scattering for Graph Data Analysis
Feng Gao, Guy Wolf, Matthew Hirn
We explore the generalization of scattering transforms from traditional (e.g., image or audio) signals to graph data, analogous to the generalization of ConvNets in geometric deep…
Interpretable Neuron Structuring with Graph Spectral Regularization
Alexander Tong, David van Dijk, Jay S. Stanley +6
While neural networks are powerful approximators used to classify or embed data into lower dimensional spaces, they are often regarded as black boxes with uninterpretable features.…
Adversarial Domain Adaptation for Stable Brain-Machine Interfaces
Ali Farshchian, Juan A. Gallego, Joseph P. Cohen +3
Brain-Machine Interfaces (BMIs) have recently emerged as a clinically viable option to restore voluntary movements after paralysis. These devices are based on the ability to extrac…
Scattering Networks for Hybrid Representation Learning
Edouard Oyallon, Sergey Zagoruyko, Gabriel Huang +4
Scattering networks are a class of designed Convolutional Neural Networks (CNNs) with fixed weights. We argue they can serve as generic representations for modelling images. In par…
On the Learning Dynamics of Deep Neural Networks
Remi Tachet, Mohammad Pezeshki, Samira Shabanian +2
While a lot of progress has been made in recent years, the dynamics of learning in deep nonlinear neural networks remain to this day largely misunderstood. In this work, we study t…
Approximate Exploration through State Abstraction
Adrien Ali Taïga, Aaron Courville, Marc G. Bellemare
Although exploration in reinforcement learning is well understood from a theoretical point of view, provably correct methods remain impractical. In this paper we study the interpla…
Predicting Tactical Solutions to Operational Planning Problems under Imperfect Information
Eric Larsen, Sébastien Lachapelle, Yoshua Bengio +3
This paper offers a methodological contribution at the intersection of machine learning and operations research. Namely, we propose a methodology to quickly predict expected tactic…
Negative Momentum for Improved Game Dynamics
Gauthier Gidel, Reyhane Askari Hemmat, Mohammad Pezeshki +4
Games generalize the single-objective optimization paradigm by introducing different objective functions for different players. Differentiable games often proceed by simultaneous o…
Connecting Weighted Automata and Recurrent Neural Networks through Spectral Learning
Guillaume Rabusseau, Tianyu Li, Doina Precup
In this paper, we unravel a fundamental connection between weighted finite automata~(WFAs) and second-order recurrent neural networks~(2-RNNs): in the case of sequences of discrete…
Learning Distributed Representations from Reviews for Collaborative Filtering
Amjad Almahairi, Kyle Kastner, Kyunghyun Cho +1
Recent work has shown that collaborative filter-based recommender systems can be improved by incorporating side information, such as natural language reviews, as a way of regulariz…
Fast Approximate Natural Gradient Descent in a Kronecker-factored Eigenbasis
Thomas George, César Laurent, Xavier Bouthillier +2
Optimization algorithms that leverage gradient covariance information, such as variants of natural gradient descent (Amari, 1998), offer the prospect of yielding more effective des…
Bayesian Model-Agnostic Meta-Learning
Taesup Kim, Jaesik Yoon, Ousmane Dia +3
Learning to infer Bayesian posterior from a few-shot dataset is an important step towards robust meta-learning due to the model uncertainty inherent in the problem. In this paper,…
Randomized Value Functions via Multiplicative Normalizing Flows
Ahmed Touati, Harsh Satija, Joshua Romoff +2
Randomized value functions offer a promising approach towards the challenge of efficient exploration in complex environments with high dimensional state and action spaces. Unlike t…
Quantitatively Evaluating GANs With Divergences Proposed for Training
Daniel Jiwoong Im, He Ma, Graham Taylor +1
Generative adversarial networks (GANs) have been extremely effective in approximating complex distributions of high-dimensional, input data samples, and substantial progress has be…
Neural Autoregressive Flows
Chin-Wei Huang, David Krueger, Alexandre Lacoste +1
Normalizing flows and autoregressive models have been successfully combined to produce state-of-the-art results in density estimation, via Masked Autoregressive Flows (MAF), and to…