142 citations · 226 across the 24 of their papers we have counts for
14 papers · 1 filter
On Graph Neural Networks versus Graph-Augmented MLPs
Lei Chen, Zhengdao Chen, Joan Bruna
From the perspective of expressive power, this work compares multi-layer Graph Neural Networks (GNNs) with a simplified alternative that we call Graph-Augmented Multi-Layer Percept…
Learned Equivariant Rendering without Transformation Supervision
Cinjon Resnick, Or Litany, Hugo Larochelle +2
We propose a self-supervised framework to learn scene representations from video that are automatically delineated into objects and background. Our method relies on moving objects…
Adaptive Test Allocation for Outbreak Detection and Tracking in Social Contact Networks
Pau Batlle, Joan Bruna, Carlos Fernandez-Granda +1
We present a general framework for adaptive allocation of viral tests in social contact networks. We pose and solve several complementary problems. First, we consider the design of…
Kernel-Based Smoothness Analysis of Residual Networks
Tom Tirer, Joan Bruna, Raja Giryes
A major factor in the success of deep neural networks is the use of sophisticated architectures rather than the classical multilayer perceptron (MLP). Residual networks (ResNets) s…
A Dynamical Central Limit Theorem for Shallow Neural Networks
Zhengdao Chen, Grant M. Rotskoff, Joan Bruna +1
Recent theoretical works have characterized the dynamics of wide shallow neural networks trained via gradient descent in an asymptotic mean-field limit when the width tends towards…
A Functional Perspective on Learning Symmetric Functions with Neural Networks
Aaron Zweig, Joan Bruna
Symmetric functions, which take as input an unordered, fixed-size set, are known to be universally representable by neural networks that enforce permutation invariance. These archi…
Depth separation for reduced deep networks in nonlinear model reduction: Distilling shock waves in nonlinear hyperbolic problems
Donsub Rim, Luca Venturi, Joan Bruna +1
Classical reduced models are low-rank approximations using a fixed basis designed to achieve dimensionality reduction of large-scale systems. In this work, we introduce reduced dee…
In-Distribution Interpretability for Challenging Modalities
Cosmas Heiß, Ron Levie, Cinjon Resnick +2
It is widely recognized that the predictions of deep neural networks are difficult to parse relative to simpler approaches. However, the development of methods to investigate the m…
On Sparsity in Overparametrised Shallow ReLU Networks
Jaume de Dios, Joan Bruna
The analysis of neural network training beyond their linearization regime remains an outstanding open question, even in the simplest setup of a single hidden-layer. The limit of in…
IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method
Yossi Arjevani, Joan Bruna, Bugra Can +3
We introduce a framework for designing primal methods under the decentralized optimization setting where local functions are smooth and strongly convex. Our approach consists of ap…
Offline Contextual Bandits with Overparameterized Models
David Brandfonbrener, William F. Whitney, Rajesh Ranganath +1
Recent results in supervised learning suggest that while overparameterized models have the capacity to overfit, they in fact generalize quite well. We ask whether the same phenomen…
Neural Splines: Fitting 3D Surfaces with Infinitely-Wide Neural Networks
Francis Williams, Matthew Trager, Joan Bruna +1
We present Neural Splines, a technique for 3D surface reconstruction that is based on random feature kernels arising from infinitely-wide shallow ReLU networks. Our method achieves…
A new approach to observational cosmology using the scattering transform
Sihao Cheng, Yuan-Sen Ting, Brice Ménard +1
Parameter estimation with non-Gaussian stochastic fields is a common challenge in astrophysics and cosmology. In this paper, we advocate performing this task using the scattering t…
Continuous LWE
Joan Bruna, Oded Regev, Min Jae Song +1
We introduce a continuous analogue of the Learning with Errors (LWE) problem, which we name CLWE. We give a polynomial-time quantum reduction from worst-case lattice problems to CL…