Joan Bruna

is this you? claim profile

∙ 0 followers
Facebook ∙ NYU college ∙ berkeley college

Assistant Professor at Courant Institute, New York University

Featured Co-authors

Kyunghyun Cho
155 publications
Yann LeCun
80 publications
Julian Togelius
76 publications
Xiang Li
70 publications
Alejandro Ribeiro
69 publications
Stefano Soatto
68 publications
Raja Giryes
62 publications
Rene Vidal
52 publications
Lei Chen
50 publications
Arthur Szlam
50 publications
Ian Goodfellow
49 publications

Kernel-Based Smoothness Analysis of Residual Networks

A major factor in the success of deep neural networks is the use of soph...
09/21/2020 ∙ by Tom Tirer, et al. ∙ 0 ∙ share

read it
A Dynamical Central Limit Theorem for Shallow Neural Networks

Recent theoretical work has characterized the dynamics of wide shallow n...
08/21/2020 ∙ by Zhengdao Chen, et al. ∙ 8 ∙ share

read it
A Functional Perspective on Learning Symmetric Functions with Neural Networks

Symmetric functions, which take as input an unordered, fixed-size set, a...
08/16/2020 ∙ by Aaron Zweig, et al. ∙ 13 ∙ share

read it
Depth separation for reduced deep networks in nonlinear model reduction: Distilling shock waves in nonlinear hyperbolic problems

Classical reduced models are low-rank approximations using a fixed basis...
07/28/2020 ∙ by Donsub Rim, et al. ∙ 0 ∙ share

read it
In-Distribution Interpretability for Challenging Modalities

It is widely recognized that the predictions of deep neural networks are...
07/01/2020 ∙ by Cosmas Heiß, et al. ∙ 16 ∙ share

read it
Overfitting and Optimization in Offline Policy Learning

We consider the task of policy learning from an offline dataset generate...
06/27/2020 ∙ by David Brandfonbrener, et al. ∙ 11 ∙ share

read it
Neural Splines: Fitting 3D Surfaces with Infinitely-Wide Neural Networks

We present Neural Splines, a technique for 3D surface reconstruction tha...
06/24/2020 ∙ by Francis Williams, et al. ∙ 6 ∙ share

read it
On Sparsity in Overparametrised Shallow ReLU Networks

The analysis of neural network training beyond their linearization regim...
06/18/2020 ∙ by Jaume de Dios, et al. ∙ 0 ∙ share

read it
IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method

We introduce a framework for designing primal methods under the decentra...
06/11/2020 ∙ by Yossi Arjevani, et al. ∙ 0 ∙ share

read it
Continuous LWE

We introduce a continuous analogue of the Learning with Errors (LWE) pro...
05/19/2020 ∙ by Joan Bruna, et al. ∙ 0 ∙ share

read it
A Permutation-Equivariant Neural Network Architecture For Auction Design

Designing an incentive compatible auction that maximizes expected revenu...
03/02/2020 ∙ by Jad Rahme, et al. ∙ 23 ∙ share

read it
Provably Efficient Third-Person Imitation from Offline Observation

Domain adaptation in imitation learning represents an essential step tow...
02/27/2020 ∙ by Aaron Zweig, et al. ∙ 4 ∙ share

read it
A mean-field analysis of two-player zero-sum games

Finding Nash equilibria in two-player zero-sum continuous games is a cen...
02/14/2020 ∙ by Carles Domingo Enrich, et al. ∙ 5 ∙ share

read it
Can graph neural networks count substructures?

The ability to detect and count certain substructures in graphs is impor...
02/10/2020 ∙ by Zhengdao Chen, et al. ∙ 37 ∙ share

read it
Probing the State of the Art: A Critical Look at Visual Representation Evaluation

Self-supervised research improved greatly over the past half decade, wit...
11/30/2019 ∙ by Cinjon Resnick, et al. ∙ 12 ∙ share

read it
Stability of Graph Neural Networks to Relative Perturbations

Graph neural networks (GNNs), consisting of a cascade of layers applying...
10/21/2019 ∙ by Fernando Gama, et al. ∙ 24 ∙ share

read it
Pure and Spurious Critical Points: a Geometric Study of Linear Networks

The critical locus of the loss function of a neural network is determine...
10/03/2019 ∙ by Matthew Trager, et al. ∙ 7 ∙ share

read it
Gradient Dynamics of Shallow Univariate ReLU Networks

We present a theoretical and empirical study of the gradient dynamics of...
06/18/2019 ∙ by Francis Williams, et al. ∙ 3 ∙ share

read it
Finding the Needle in the Haystack with Convolutions: on the benefits of architectural bias

Despite the phenomenal success of deep neural networks in a broad range ...
06/16/2019 ∙ by Stéphane d'Ascoli, et al. ∙ 0 ∙ share

read it
Stability of Graph Scattering Transforms

Scattering transforms are non-trainable deep convolutional architectures...
06/11/2019 ∙ by Fernando Gama, et al. ∙ 4 ∙ share

read it
On the equivalence between graph isomorphism testing and function approximation with GNNs

Graph neural networks (GNNs) have achieved lots of success on graph-stru...
05/29/2019 ∙ by Zhengdao Chen, et al. ∙ 2 ∙ share

read it
Extra-gradient with player sampling for provable fast convergence in n-player games

Data-driven model training is increasingly relying on finding Nash equil...
05/29/2019 ∙ by Samy Jelassi, et al. ∙ 0 ∙ share

read it
On the Expressive Power of Deep Polynomial Neural Networks

We study deep neural networks with polynomial activations, particularly ...
05/29/2019 ∙ by Joe Kileel, et al. ∙ 5 ∙ share

read it
On the Expected Dynamics of Nonlinear TD Learning

While there are convergence guarantees for temporal difference (TD) lear...
05/29/2019 ∙ by David Brandfonbrener, et al. ∙ 8 ∙ share

read it
Stability Properties of Graph Neural Networks

Data stemming from networks exhibit an irregular support, whereby each d...
05/11/2019 ∙ by Fernando Gama, et al. ∙ 22 ∙ share

read it
Advancing GraphSAGE with A Data-Driven Node Sampling

As an efficient and scalable graph neural network, GraphSAGE has enabled...
04/29/2019 ∙ by Jihun Oh, et al. ∙ 30 ∙ share

read it
Global convergence of neuron birth-death dynamics

Neural networks with a large number of parameters admit a mean-field des...
02/05/2019 ∙ by Grant Rotskoff, et al. ∙ 4 ∙ share

read it
Kymatio: Scattering Transforms in Python

The wavelet scattering transform is an invariant signal representation s...
12/28/2018 ∙ by Mathieu Andreux, et al. ∙ 12 ∙ share

read it
Deep Geometric Prior for Surface Reconstruction

The reconstruction of a discrete surface from a point cloud is a fundame...
11/27/2018 ∙ by Francis Williams, et al. ∙ 8 ∙ share

read it
Pommerman: A Multi-Agent Playground

We present Pommerman, a multi-agent environment based on the classic con...
09/19/2018 ∙ by Cinjon Resnick, et al. ∙ 0 ∙ share

read it
Graph Neural Networks for IceCube Signal Classification

Tasks involving the analysis of geometric (graph- and manifold-structure...
09/17/2018 ∙ by Nicholas Choma, et al. ∙ 2 ∙ share

read it
Planning with Arithmetic and Geometric Attributes

A desirable property of an intelligent agent is its ability to understan...
09/06/2018 ∙ by David Folqué, et al. ∙ 10 ∙ share

read it
Backplay: "Man muss immer umkehren"

A long-standing problem in model free reinforcement learning (RL) is tha...
07/18/2018 ∙ by Cinjon Resnick, et al. ∙ 2 ∙ share

read it
Diffusion Scattering Transforms on Graphs

Stability is a key aspect of data analysis. In many applications, the na...
06/22/2018 ∙ by Fernando Gama, et al. ∙ 0 ∙ share

read it
Neural Networks with Finite Intrinsic Dimension have no Spurious Valleys

Neural networks provide a rich class of high-dimensional, non-convex opt...
02/18/2018 ∙ by Luca Venturi, et al. ∙ 0 ∙ share

read it
Multiscale Sparse Microcanonical Models

We study density estimation of stationary processes defined over an infi...
01/06/2018 ∙ by Joan Bruna, et al. ∙ 0 ∙ share

read it
Mathematics of Deep Learning

Recently there has been a dramatic increase in the performance of recogn...
12/13/2017 ∙ by Rene Vidal, et al. ∙ 0 ∙ share

read it
Few-Shot Learning with Graph Neural Networks

We propose to study the problem of few-shot learning with the prism of i...
11/10/2017 ∙ by Victor Garcia, et al. ∙ 0 ∙ share

read it
A Note on Learning Algorithms for Quadratic Assignment with Graph Neural Networks

Many inverse problems are formulated as optimization problems over certa...
06/22/2017 ∙ by Alex Nowak, et al. ∙ 0 ∙ share

read it
Understanding the Learned Iterative Soft Thresholding Algorithm with matrix factorization

Sparse coding is a core building block in many data analysis and machine...
06/02/2017 ∙ by Thomas Moreau, et al. ∙ 0 ∙ share

read it
Surface Networks

We study data-driven representations for three-dimensional triangle mesh...
05/30/2017 ∙ by Ilya Kostrikov, et al. ∙ 0 ∙ share

read it
Community Detection with Graph Neural Networks

We study data-driven methods for community detection in graphs. This est...
05/23/2017 ∙ by Joan Bruna, et al. ∙ 0 ∙ share

read it
Geometric deep learning: going beyond Euclidean data

Many scientific fields study data with an underlying structure that is a...
11/24/2016 ∙ by Michael M. Bronstein, et al. ∙ 0 ∙ share

read it
Divide and Conquer Networks

We consider the learning of algorithmic tasks by mere observation of inp...
11/08/2016 ∙ by Alex Nowak, et al. ∙ 0 ∙ share

read it
Topology and Geometry of Half-Rectified Network Optimization

The loss surface of deep neural networks has recently attracted interest...
11/04/2016 ∙ by C. Daniel Freeman, et al. ∙ 0 ∙ share

read it
Voice Conversion using Convolutional Neural Networks

The human auditory system is able to distinguish the vocal source of tho...
10/27/2016 ∙ by Shariq Mobin, et al. ∙ 0 ∙ share

read it
Understanding Trainable Sparse Coding via Matrix Factorization

Sparse coding is a core building block in many data analysis and machine...
09/01/2016 ∙ by Thomas Moreau, et al. ∙ 0 ∙ share

read it
Super-Resolution with Deep Convolutional Sufficient Statistics

Inverse problems in image and audio, and super-resolution in particular,...
11/18/2015 ∙ by Joan Bruna, et al. ∙ 0 ∙ share

read it
Deep Convolutional Networks on Graph-Structured Data

Deep Learning's recent successes have mostly relied on Convolutional Net...
06/16/2015 ∙ by Mikael Henaff, et al. ∙ 0 ∙ share

read it
Unsupervised Feature Learning from Temporal Data

Current state-of-the-art classification and detection algorithms rely on...
04/09/2015 ∙ by Ross Goroshin, et al. ∙ 0 ∙ share

read it