
Advancing GraphSAGE with A DataDriven Node Sampling
As an efficient and scalable graph neural network, GraphSAGE has enabled...
04/29/2019 ∙ by Jihun Oh, et al. ∙ 30 ∙ shareread 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 ∙ shareread 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 ∙ shareread 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 ∙ shareread it

Probing the State of the Art: A Critical Look at Visual Representation Evaluation
Selfsupervised research improved greatly over the past half decade, wit...
11/30/2019 ∙ by Cinjon Resnick, et al. ∙ 12 ∙ shareread 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 ∙ shareread 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 ∙ shareread 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 ∙ shareread 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 ∙ shareread 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 ∙ shareread it

Global convergence of neuron birthdeath dynamics
Neural networks with a large number of parameters admit a meanfield des...
02/05/2019 ∙ by Grant Rotskoff, et al. ∙ 4 ∙ shareread it

Stability of Graph Scattering Transforms
Scattering transforms are nontrainable deep convolutional architectures...
06/11/2019 ∙ by Fernando Gama, et al. ∙ 4 ∙ shareread 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 ∙ shareread it

Backplay: "Man muss immer umkehren"
A longstanding problem in model free reinforcement learning (RL) is tha...
07/18/2018 ∙ by Cinjon Resnick, et al. ∙ 2 ∙ shareread it

Graph Neural Networks for IceCube Signal Classification
Tasks involving the analysis of geometric (graph and manifoldstructure...
09/17/2018 ∙ by Nicholas Choma, et al. ∙ 2 ∙ shareread it

On the equivalence between graph isomorphism testing and function approximation with GNNs
Graph neural networks (GNNs) have achieved lots of success on graphstru...
05/29/2019 ∙ by Zhengdao Chen, et al. ∙ 2 ∙ shareread it

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 ∙ shareread it

Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation
We present techniques for speeding up the testtime evaluation of large ...
04/02/2014 ∙ by Emily Denton, et al. ∙ 0 ∙ shareread it

SuperResolution with Deep Convolutional Sufficient Statistics
Inverse problems in image and audio, and superresolution in particular,...
11/18/2015 ∙ by Joan Bruna, et al. ∙ 0 ∙ shareread it

FewShot Learning with Graph Neural Networks
We propose to study the problem of fewshot learning with the prism of i...
11/10/2017 ∙ by Victor Garcia, et al. ∙ 0 ∙ shareread 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 ∙ shareread 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 ∙ shareread it

Community Detection with Graph Neural Networks
We study datadriven methods for community detection in graphs. This est...
05/23/2017 ∙ by Joan Bruna, et al. ∙ 0 ∙ shareread 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 ∙ shareread it

Topology and Geometry of HalfRectified Network Optimization
The loss surface of deep neural networks has recently attracted interest...
11/04/2016 ∙ by C. Daniel Freeman, et al. ∙ 0 ∙ shareread 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 ∙ shareread 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 ∙ shareread 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 ∙ shareread it

A mathematical motivation for complexvalued convolutional networks
A complexvalued convolutional network (convnet) implements the repeated...
03/11/2015 ∙ by Joan Bruna, et al. ∙ 0 ∙ shareread it

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 ∙ shareread it

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 ∙ shareread it

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

Unsupervised Feature Learning from Temporal Data
Current stateoftheart classification and detection algorithms rely on...
04/09/2015 ∙ by Ross Goroshin, et al. ∙ 0 ∙ shareread it

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 ∙ shareread it

Unsupervised Learning of Spatiotemporally Coherent Metrics
Current stateoftheart classification and detection algorithms rely on...
12/18/2014 ∙ by Ross Goroshin, et al. ∙ 0 ∙ shareread it

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 ∙ shareread it

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 ∙ shareread it

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 ∙ shareread it

Blind Deconvolution with Nonlocal Sparsity Reweighting
Blind deconvolution has made significant progress in the past decade. Mo...
11/16/2013 ∙ by Dilip Krishnan, et al. ∙ 0 ∙ shareread it

Classification with Invariant Scattering Representations
A scattering transform defines a signal representation which is invarian...
12/05/2011 ∙ by Joan Bruna, et al. ∙ 0 ∙ shareread it

Geometric Models with Cooccurrence Groups
A geometric model of sparse signal representations is introduced for cla...
01/30/2011 ∙ by Joan Bruna, et al. ∙ 0 ∙ shareread it

Classification with Scattering Operators
A scattering vector is a local descriptor including multiscale and multi...
11/12/2010 ∙ by Joan Bruna, et al. ∙ 0 ∙ shareread 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 ∙ shareread 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 ∙ shareread it

Neural Networks with Finite Intrinsic Dimension have no Spurious Valleys
Neural networks provide a rich class of highdimensional, nonconvex opt...
02/18/2018 ∙ by Luca Venturi, et al. ∙ 0 ∙ shareread it

Surface Networks
We study datadriven representations for threedimensional triangle mesh...
05/30/2017 ∙ by Ilya Kostrikov, et al. ∙ 0 ∙ shareread 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 ∙ shareread it

Pommerman: A MultiAgent Playground
We present Pommerman, a multiagent environment based on the classic con...
09/19/2018 ∙ by Cinjon Resnick, et al. ∙ 0 ∙ shareread it

Extragradient with player sampling for provable fast convergence in nplayer games
Datadriven model training is increasingly relying on finding Nash equil...
05/29/2019 ∙ by Samy Jelassi, et al. ∙ 0 ∙ shareread 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 ∙ shareread it