5.6k citations · 17.4k across the 77 of their papers we have counts for
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Kymatio: Scattering Transforms in Python
Mathieu Andreux, Tomás Angles, Georgios Exarchakis +15
The wavelet scattering transform is an invariant signal representation suitable for many signal processing and machine learning applications. We present the Kymatio software packag…
Deep Geometric Prior for Surface Reconstruction
Francis Williams, Teseo Schneider, Claudio Silva +3
The reconstruction of a discrete surface from a point cloud is a fundamental geometry processing problem that has been studied for decades, with many methods developed. We propose…
Graph Neural Networks for IceCube Signal Classification
Nicholas Choma, Federico Monti, Lisa Gerhardt +7
Tasks involving the analysis of geometric (graph- and manifold-structured) data have recently gained prominence in the machine learning community, giving birth to a rapidly develop…
Planning with Arithmetic and Geometric Attributes
David Folqué, Sainbayar Sukhbaatar, Arthur Szlam +1
A desirable property of an intelligent agent is its ability to understand its environment to quickly generalize to novel tasks and compose simpler tasks into more complex ones. If…
Pommerman: A Multi-Agent Playground
Cinjon Resnick, Wes Eldridge, David Ha +5
We present Pommerman, a multi-agent environment based on the classic console game Bomberman. Pommerman consists of a set of scenarios, each having at least four players and contain…
Backplay: "Man muss immer umkehren"
Cinjon Resnick, Roberta Raileanu, Sanyam Kapoor +3
Model-free reinforcement learning (RL) requires a large number of trials to learn a good policy, especially in environments with sparse rewards. We explore a method to improve the…
Diffusion Scattering Transforms on Graphs
Fernando Gama, Alejandro Ribeiro, Joan Bruna
Stability is a key aspect of data analysis. In many applications, the natural notion of stability is geometric, as illustrated for example in computer vision. Scattering transforms…
Spurious Valleys in Two-layer Neural Network Optimization Landscapes
Luca Venturi, Afonso S. Bandeira, Joan Bruna
Neural networks provide a rich class of high-dimensional, non-convex optimization problems. Despite their non-convexity, gradient-descent methods often successfully optimize these…
Multiscale Sparse Microcanonical Models
Joan Bruna, Stephane Mallat
We study approximations of non-Gaussian stationary processes having long range correlations with microcanonical models. These models are conditioned by the empirical value of an en…