5.6k citations · 17.4k across the 77 of their papers we have counts for
6 papers · 1 filter
Geometric deep learning: going beyond Euclidean data
Michael M. Bronstein, Joan Bruna, Yann LeCun +2
Many scientific fields study data with an underlying structure that is a non-Euclidean space. Some examples include social networks in computational social sciences, sensor network…
Divide and Conquer Networks
Alex Nowak-Vila, David Folqué, Joan Bruna
We consider the learning of algorithmic tasks by mere observation of input-output pairs. Rather than studying this as a black-box discrete regression problem with no assumption wha…
Topology and Geometry of Half-Rectified Network Optimization
C. Daniel Freeman, Joan Bruna
The loss surface of deep neural networks has recently attracted interest in the optimization and machine learning communities as a prime example of high-dimensional non-convex prob…
Voice Conversion using Convolutional Neural Networks
Shariq Mobin, Joan Bruna
The human auditory system is able to distinguish the vocal source of thousands of speakers, yet not much is known about what features the auditory system uses to do this. Fourier T…
Inverse Problems with Invariant Multiscale Statistics
Ivan Dokmanić, Joan Bruna, Stéphane Mallat +1
We propose a new approach to linear ill-posed inverse problems. Our algorithm alternates between enforcing two constraints: the measurements and the statistical correlation structu…
Understanding Trainable Sparse Coding via Matrix Factorization
Thomas Moreau, Joan Bruna
Sparse coding is a core building block in many data analysis and machine learning pipelines. Typically it is solved by relying on generic optimization techniques, that are optimal…