Joan Bruna

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Facebook ∙ NYU college ∙ berkeley college

Assistant Professor at Courant Institute, New York University

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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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Stability of Graph Scattering Transforms

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

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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

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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

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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

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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

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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

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Audio Source Separation with Discriminative Scattering Networks

In this report we describe an ongoing line of research for solving singl...
12/22/2014 ∙ by Pablo Sprechmann, et al. ∙ 0 ∙ share

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Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation

We present techniques for speeding up the test-time evaluation of large ...
04/02/2014 ∙ by Emily Denton, et al. ∙ 0 ∙ share

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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A mathematical motivation for complex-valued convolutional networks

A complex-valued convolutional network (convnet) implements the repeated...
03/11/2015 ∙ by Joan Bruna, et al. ∙ 0 ∙ share

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Learning Stable Group Invariant Representations with Convolutional Networks

Transformation groups, such as translations or rotations, effectively ex...
01/16/2013 ∙ by Joan Bruna, et al. ∙ 0 ∙ share

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Signal Recovery from Pooling Representations

In this work we compute lower Lipschitz bounds of ℓ_p pooling operators ...
11/16/2013 ∙ by Joan Bruna, et al. ∙ 0 ∙ share

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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

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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

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Video (language) modeling: a baseline for generative models of natural videos

We propose a strong baseline model for unsupervised feature learning usi...
12/20/2014 ∙ by MarcAurelio Ranzato, et al. ∙ 0 ∙ share

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Unsupervised Learning of Spatiotemporally Coherent Metrics

Current state-of-the-art classification and detection algorithms rely on...
12/18/2014 ∙ by Ross Goroshin, et al. ∙ 0 ∙ share

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Training Convolutional Networks with Noisy Labels

The availability of large labeled datasets has allowed Convolutional Net...
06/09/2014 ∙ by Sainbayar Sukhbaatar, et al. ∙ 0 ∙ share

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Spectral Networks and Locally Connected Networks on Graphs

Convolutional Neural Networks are extremely efficient architectures in i...
12/21/2013 ∙ by Joan Bruna, et al. ∙ 0 ∙ share

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Intriguing properties of neural networks

Deep neural networks are highly expressive models that have recently ach...
12/21/2013 ∙ by Christian Szegedy, et al. ∙ 0 ∙ share

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Blind Deconvolution with Non-local Sparsity Reweighting

Blind deconvolution has made significant progress in the past decade. Mo...
11/16/2013 ∙ by Dilip Krishnan, et al. ∙ 0 ∙ share

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Classification with Invariant Scattering Representations

A scattering transform defines a signal representation which is invarian...
12/05/2011 ∙ by Joan Bruna, et al. ∙ 0 ∙ share

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Geometric Models with Co-occurrence Groups

A geometric model of sparse signal representations is introduced for cla...
01/30/2011 ∙ by Joan Bruna, et al. ∙ 0 ∙ share

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Classification with Scattering Operators

A scattering vector is a local descriptor including multiscale and multi...
11/12/2010 ∙ by Joan Bruna, et al. ∙ 0 ∙ share

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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

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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

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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

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Surface Networks

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

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Joan Bruna

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