
Can graph neural networks count substructures?
The ability to detect and count certain substructures in graphs is impor...
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Advancing GraphSAGE with A DataDriven Node Sampling
As an efficient and scalable graph neural network, GraphSAGE has enabled...
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Stability of Graph Neural Networks to Relative Perturbations
Graph neural networks (GNNs), consisting of a cascade of layers applying...
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A PermutationEquivariant Neural Network Architecture For Auction Design
Designing an incentive compatible auction that maximizes expected revenu...
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Stability Properties of Graph Neural Networks
Data stemming from networks exhibit an irregular support, whereby each d...
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Kymatio: Scattering Transforms in Python
The wavelet scattering transform is an invariant signal representation s...
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Probing the State of the Art: A Critical Look at Visual Representation Evaluation
Selfsupervised research improved greatly over the past half decade, wit...
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Planning with Arithmetic and Geometric Attributes
A desirable property of an intelligent agent is its ability to understan...
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Deep Geometric Prior for Surface Reconstruction
The reconstruction of a discrete surface from a point cloud is a fundame...
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On the Expected Dynamics of Nonlinear TD Learning
While there are convergence guarantees for temporal difference (TD) lear...
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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...
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On the Expressive Power of Deep Polynomial Neural Networks
We study deep neural networks with polynomial activations, particularly ...
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A meanfield analysis of twoplayer zerosum games
Finding Nash equilibria in twoplayer zerosum continuous games is a cen...
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Global convergence of neuron birthdeath dynamics
Neural networks with a large number of parameters admit a meanfield des...
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Stability of Graph Scattering Transforms
Scattering transforms are nontrainable deep convolutional architectures...
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Provably Efficient ThirdPerson Imitation from Offline Observation
Domain adaptation in imitation learning represents an essential step tow...
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Gradient Dynamics of Shallow Univariate ReLU Networks
We present a theoretical and empirical study of the gradient dynamics of...
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Backplay: "Man muss immer umkehren"
A longstanding problem in model free reinforcement learning (RL) is tha...
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Graph Neural Networks for IceCube Signal Classification
Tasks involving the analysis of geometric (graph and manifoldstructure...
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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 graphstru...
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Audio Source Separation with Discriminative Scattering Networks
In this report we describe an ongoing line of research for solving singl...
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Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation
We present techniques for speeding up the testtime evaluation of large ...
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SuperResolution with Deep Convolutional Sufficient Statistics
Inverse problems in image and audio, and superresolution in particular,...
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FewShot Learning with Graph Neural Networks
We propose to study the problem of fewshot learning with the prism of i...
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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...
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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...
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Community Detection with Graph Neural Networks
We study datadriven methods for community detection in graphs. This est...
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Divide and Conquer Networks
We consider the learning of algorithmic tasks by mere observation of inp...
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Topology and Geometry of HalfRectified Network Optimization
The loss surface of deep neural networks has recently attracted interest...
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Voice Conversion using Convolutional Neural Networks
The human auditory system is able to distinguish the vocal source of tho...
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Understanding Trainable Sparse Coding via Matrix Factorization
Sparse coding is a core building block in many data analysis and machine...
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Geometric deep learning: going beyond Euclidean data
Many scientific fields study data with an underlying structure that is a...
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A mathematical motivation for complexvalued convolutional networks
A complexvalued convolutional network (convnet) implements the repeated...
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Learning Stable Group Invariant Representations with Convolutional Networks
Transformation groups, such as translations or rotations, effectively ex...
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Signal Recovery from Pooling Representations
In this work we compute lower Lipschitz bounds of ℓ_p pooling operators ...
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Deep Convolutional Networks on GraphStructured Data
Deep Learning's recent successes have mostly relied on Convolutional Net...
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Unsupervised Feature Learning from Temporal Data
Current stateoftheart classification and detection algorithms rely on...
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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...
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Unsupervised Learning of Spatiotemporally Coherent Metrics
Current stateoftheart classification and detection algorithms rely on...
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Training Convolutional Networks with Noisy Labels
The availability of large labeled datasets has allowed Convolutional Net...
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Spectral Networks and Locally Connected Networks on Graphs
Convolutional Neural Networks are extremely efficient architectures in i...
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Intriguing properties of neural networks
Deep neural networks are highly expressive models that have recently ach...
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Blind Deconvolution with Nonlocal Sparsity Reweighting
Blind deconvolution has made significant progress in the past decade. Mo...
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Classification with Invariant Scattering Representations
A scattering transform defines a signal representation which is invarian...
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Geometric Models with Cooccurrence Groups
A geometric model of sparse signal representations is introduced for cla...
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Classification with Scattering Operators
A scattering vector is a local descriptor including multiscale and multi...
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Mathematics of Deep Learning
Recently there has been a dramatic increase in the performance of recogn...
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Multiscale Sparse Microcanonical Models
We study density estimation of stationary processes defined over an infi...
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Neural Networks with Finite Intrinsic Dimension have no Spurious Valleys
Neural networks provide a rich class of highdimensional, nonconvex opt...
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Surface Networks
We study datadriven representations for threedimensional triangle mesh...
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