wovepaper — the arXiv observatory

NewEvery arXiv paper, its researchers & institutions — mapped.

papers

Publications (14)

Polynomial Networks and Factorization Machines: New Insights and Efficient Training Algorithms

Mathieu Blondel, Masakazu Ishihata, Akinori Fujino +1

SparseMAP: Differentiable Sparse Structured Inference

Vlad Niculae, AndrÃ© F. T. Martins, Mathieu Blondel +1

Large-Scale Optimal Transport and Mapping Estimation

Vivien Seguy, Bharath Bhushan Damodaran, RÃ©mi Flamary +3

Scikit-learn: Machine Learning in Python

Fabian Pedregosa, GaÃ«l Varoquaux, Alexandre Gramfort +16

API design for machine learning software: experiences from the scikit-learn project

Lars Buitinck, Gilles Louppe, Mathieu Blondel +12

A Regularized Framework for Sparse and Structured Neural Attention

Vlad Niculae, Mathieu Blondel

Multi-output Polynomial Networks and Factorization Machines

Mathieu Blondel, Vlad Niculae, Takuma Otsuka +1

Geometric Losses for Distributional Learning

Arthur Mensch, Mathieu Blondel, Gabriel PeyrÃ©

Differentiable Dynamic Programming for Structured Prediction and Attention

Arthur Mensch, Mathieu Blondel

Learning Classifiers with Fenchel-Young Losses: Generalized Entropies, Margins, and Algorithms

Mathieu Blondel, AndrÃ© F. T. Martins, Vlad Niculae

Blind Source Separation with Optimal Transport Non-negative Matrix Factorization

Antoine Rolet, Vivien Seguy, Mathieu Blondel +1

Higher-Order Factorization Machines

Mathieu Blondel, Akinori Fujino, Naonori Ueda +1

Soft-DTW: a Differentiable Loss Function for Time-Series

Marco Cuturi, Mathieu Blondel

Smooth and Sparse Optimal Transport

Mathieu Blondel, Vivien Seguy, Antoine Rolet