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Learned Equivariant Rendering without Transformation Supervision
We propose a self-supervised framework to learn scene representations fr...
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On Graph Neural Networks versus Graph-Augmented MLPs
From the perspective of expressive power, this work compares multi-layer...
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Kernel-Based Smoothness Analysis of Residual Networks
A major factor in the success of deep neural networks is the use of soph...
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A Dynamical Central Limit Theorem for Shallow Neural Networks
Recent theoretical work has characterized the dynamics of wide shallow n...
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A Functional Perspective on Learning Symmetric Functions with Neural Networks
Symmetric functions, which take as input an unordered, fixed-size set, a...
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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...
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In-Distribution Interpretability for Challenging Modalities
It is widely recognized that the predictions of deep neural networks are...
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Overfitting and Optimization in Offline Policy Learning
We consider the task of policy learning from an offline dataset generate...
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Neural Splines: Fitting 3D Surfaces with Infinitely-Wide Neural Networks
We present Neural Splines, a technique for 3D surface reconstruction tha...
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On Sparsity in Overparametrised Shallow ReLU Networks
The analysis of neural network training beyond their linearization regim...
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IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method
We introduce a framework for designing primal methods under the decentra...
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Continuous LWE
We introduce a continuous analogue of the Learning with Errors (LWE) pro...
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A Permutation-Equivariant Neural Network Architecture For Auction Design
Designing an incentive compatible auction that maximizes expected revenu...
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Provably Efficient Third-Person Imitation from Offline Observation
Domain adaptation in imitation learning represents an essential step tow...
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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...
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Can graph neural networks count substructures?
The ability to detect and count certain substructures in graphs is impor...
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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...
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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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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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Gradient Dynamics of Shallow Univariate ReLU Networks
We present a theoretical and empirical study of the gradient dynamics of...
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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 ...
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Stability of Graph Scattering Transforms
Scattering transforms are non-trainable deep convolutional architectures...
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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...
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Extra-gradient with player sampling for provable fast convergence in n-player games
Data-driven model training is increasingly relying on finding Nash equil...
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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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On the Expected Dynamics of Nonlinear TD Learning
While there are convergence guarantees for temporal difference (TD) lear...
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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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Advancing GraphSAGE with A Data-Driven Node Sampling
As an efficient and scalable graph neural network, GraphSAGE has enabled...
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Global convergence of neuron birth-death dynamics
Neural networks with a large number of parameters admit a mean-field des...
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Kymatio: Scattering Transforms in Python
The wavelet scattering transform is an invariant signal representation s...
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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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Pommerman: A Multi-Agent Playground
We present Pommerman, a multi-agent environment based on the classic con...
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Graph Neural Networks for IceCube Signal Classification
Tasks involving the analysis of geometric (graph- and manifold-structure...
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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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Backplay: "Man muss immer umkehren"
A long-standing problem in model free reinforcement learning (RL) is tha...
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Diffusion Scattering Transforms on Graphs
Stability is a key aspect of data analysis. In many applications, the na...
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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...
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Multiscale Sparse Microcanonical Models
We study density estimation of stationary processes defined over an infi...
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Mathematics of Deep Learning
Recently there has been a dramatic increase in the performance of recogn...
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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...
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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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Surface Networks
We study data-driven representations for three-dimensional triangle mesh...
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Community Detection with Graph Neural Networks
We study data-driven methods for community detection in graphs. This est...
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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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Divide and Conquer Networks
We consider the learning of algorithmic tasks by mere observation of inp...
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Topology and Geometry of Half-Rectified 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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Super-Resolution with Deep Convolutional Sufficient Statistics
Inverse problems in image and audio, and super-resolution in particular,...
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