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08/17/2023
Dual Gauss-Newton Directions for Deep Learning
Inspired by Gauss-Newton-like methods, we study the benefit of leveragin...
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02/02/2023
Fast, Differentiable and Sparse Top-k: a Convex Analysis Perspective
The top-k operator returns a k-sparse vector, where the non-zero values ...
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09/30/2022
Sparsity-Constrained Optimal Transport
Regularized optimal transport (OT) is now increasingly used as a loss or...
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05/19/2022
Learning Energy Networks with Generalized Fenchel-Young Losses
Energy-based models, a.k.a. energy networks, perform inference by optimi...
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02/24/2022
Cutting Some Slack for SGD with Adaptive Polyak Stepsizes
Tuning the step size of stochastic gradient descent is tedious and error...
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10/22/2021
Sinkformers: Transformers with Doubly Stochastic Attention
Attention based models such as Transformers involve pairwise interaction...
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08/04/2021
Sparse Continuous Distributions and Fenchel-Young Losses
Exponential families are widely used in machine learning; they include m...
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05/31/2021
Efficient and Modular Implicit Differentiation
Automatic differentiation (autodiff) has revolutionized machine learning...
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05/04/2021
Implicit differentiation for fast hyperparameter selection in non-smooth convex learning
Finding the optimal hyperparameters of a model can be cast as a bilevel ...
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03/17/2021
Self-Supervised Learning of Audio Representations from Permutations with Differentiable Ranking
Self-supervised pre-training using so-called "pretext" tasks has recentl...
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02/15/2021
Momentum Residual Neural Networks
The training of deep residual neural networks (ResNets) with backpropaga...
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10/16/2020
Differentiable Divergences Between Time Series
Computing the discrepancy between time series of variable sizes is notor...
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02/20/2020
Implicit differentiation of Lasso-type models for hyperparameter optimization
Setting regularization parameters for Lasso-type estimators is notorious...
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02/20/2020
Fast Differentiable Sorting and Ranking
The sorting operation is one of the most basic and commonly used buildin...
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02/20/2020
Learning with Differentiable Perturbed Optimizers
Machine learning pipelines often rely on optimization procedures to make...
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10/24/2019
Structured Prediction with Projection Oracles
We propose in this paper a general framework for deriving loss functions...
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05/15/2019
Geometric Losses for Distributional Learning
Building upon recent advances in entropy-regularized optimal transport, ...
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01/08/2019
Learning with Fenchel-Young Losses
Over the past decades, numerous loss functions have been been proposed f...
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05/24/2018
Learning Classifiers with Fenchel-Young Losses: Generalized Entropies, Margins, and Algorithms
We study in this paper Fenchel-Young losses, a generic way to construct ...
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02/15/2018
Blind Source Separation with Optimal Transport Non-negative Matrix Factorization
Optimal transport as a loss for machine learning optimization problems h...
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02/12/2018
SparseMAP: Differentiable Sparse Structured Inference
Structured prediction requires searching over a combinatorial number of ...
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02/11/2018
Differentiable Dynamic Programming for Structured Prediction and Attention
Dynamic programming (DP) solves a variety of structured combinatorial pr...
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11/07/2017
Large-Scale Optimal Transport and Mapping Estimation
This paper presents a novel two-step approach for the fundamental proble...
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10/17/2017
Smooth and Sparse Optimal Transport
Entropic regularization is quickly emerging as a new standard in optimal...
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05/22/2017
A Regularized Framework for Sparse and Structured Neural Attention
Modern neural networks are often augmented with an attention mechanism, ...
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05/22/2017
Multi-output Polynomial Networks and Factorization Machines
Factorization machines and polynomial networks are supervised polynomial...
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03/05/2017
Soft-DTW: a Differentiable Loss Function for Time-Series
We propose in this paper a differentiable learning loss between time ser...
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07/29/2016
Polynomial Networks and Factorization Machines: New Insights and Efficient Training Algorithms
Polynomial networks and factorization machines are two recently-proposed...
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07/25/2016
