927 citations · 2.7k across the 88 of their papers we have counts for
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Adaptive Stochastic Dual Coordinate Ascent for Conditional Random Fields
Rémi Le Priol, Alexandre Piché, Simon Lacoste-Julien
This work investigates the training of conditional random fields (CRFs) via the stochastic dual coordinate ascent (SDCA) algorithm of Shalev-Shwartz and Zhang (2016). SDCA enjoys a…
Parametric Adversarial Divergences are Good Losses for Generative Modeling
Gabriel Huang, Hugo Berard, Ahmed Touati +3
Parametric adversarial divergences, which are a generalization of the losses used to train generative adversarial networks (GANs), have often been described as being approximations…
Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization
Fabian Pedregosa, Rémi Leblond, Simon Lacoste-Julien
Due to their simplicity and excellent performance, parallel asynchronous variants of stochastic gradient descent have become popular methods to solve a wide range of large-scale op…
A Closer Look at Memorization in Deep Networks
Devansh Arpit, Stanisław Jastrzębski, Nicolas Ballas +8
We examine the role of memorization in deep learning, drawing connections to capacity, generalization, and adversarial robustness. While deep networks are capable of memorizing noi…
SEARNN: Training RNNs with Global-Local Losses
Rémi Leblond, Jean-Baptiste Alayrac, Anton Osokin +1
We propose SEARNN, a novel training algorithm for recurrent neural networks (RNNs) inspired by the "learning to search" (L2S) approach to structured prediction. RNNs have been wide…
On Structured Prediction Theory with Calibrated Convex Surrogate Losses
Anton Osokin, Francis Bach, Simon Lacoste-Julien
We provide novel theoretical insights on structured prediction in the context of efficient convex surrogate loss minimization with consistency guarantees. For any task loss, we con…