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07/18/2023
Batched Predictors Generalize within Distribution
We study the generalization properties of batched predictors, i.e., mode...
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06/05/2023
Infusing Lattice Symmetry Priors in Attention Mechanisms for Sample-Efficient Abstract Geometric Reasoning
The Abstraction and Reasoning Corpus (ARC) (Chollet, 2019) and its most ...
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05/11/2023
Towards Understanding and Improving GFlowNet Training
Generative flow networks (GFlowNets) are a family of algorithms that lea...
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10/21/2022
Conditional Diffusion with Less Explicit Guidance via Model Predictive Control
How much explicit guidance is necessary for conditional diffusion? We co...
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10/19/2022
A Pareto-optimal compositional energy-based model for sampling and optimization of protein sequences
Deep generative models have emerged as a popular machine learning-based ...
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08/16/2022
On the generalization of learning algorithms that do not converge
Generalization analyses of deep learning typically assume that the train...
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08/08/2022
Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions
Integrating functions on discrete domains into neural networks is key to...
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04/04/2022
SPECTRE : Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators
We approach the graph generation problem from a spectral perspective by ...
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10/27/2021
SQALER: Scaling Question Answering by Decoupling Multi-Hop and Logical Reasoning
State-of-the-art approaches to reasoning and question answering over kno...
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07/05/2021
Partition and Code: learning how to compress graphs
Can we use machine learning to compress graph data? The absence of order...
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06/08/2021
What training reveals about neural network complexity
This work explores the hypothesis that the complexity of the function a ...
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03/05/2021
Attention is Not All You Need: Pure Attention Loses Rank Doubly Exponentially with Depth
Attention-based architectures have become ubiquitous in machine learning...
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06/29/2020
Multi-Head Attention: Collaborate Instead of Concatenate
Attention layers are widely used in natural language processing (NLP) an...
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06/26/2020
Building powerful and equivariant graph neural networks with message-passing
Message-passing has proved to be an effective way to design graph neural...
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06/18/2020
Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs
Combinatorial optimization problems are notoriously challenging for neur...
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05/13/2020
How hard is graph isomorphism for graph neural networks?
A hallmark of graph neural networks is their ability to distinguish the ...
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11/08/2019
On the Relationship between Self-Attention and Convolutional Layers
Recent trends of incorporating attention mechanisms in vision have led r...
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07/06/2019
What graph neural networks cannot learn: depth vs width
This paper studies the capacity limits of graph neural networks (GNN). R...
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05/31/2019
Discriminative structural graph classification
This paper focuses on the discrimination capacity of aggregation functio...
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05/23/2019
Some limitations of norm based generalization bounds in deep neural networks
Deep convolutional neural networks have been shown to be able to fit a l...
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03/18/2019
Extrapolating paths with graph neural networks
We consider the problem of path inference: given a path prefix, i.e., a ...
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01/29/2019
Approximating Spectral Clustering via Sampling: a Review
Spectral clustering refers to a family of unsupervised learning algorith...
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08/31/2018
Graph reduction by local variation
How can we reduce the size of a graph without significantly altering its...
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02/21/2018
Spectrally approximating large graphs with smaller graphs
How does coarsening affect the spectrum of a general graph? We provide c...
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06/12/2017
Fast Approximate Spectral Clustering for Dynamic Networks
Spectral clustering is a widely studied problem, yet its complexity is p...
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02/17/2017
How close are the eigenvectors and eigenvalues of the sample and actual covariance matrices?
How many samples are sufficient to guarantee that the eigenvectors and e...
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11/01/2016
Stationary time-vertex signal processing
The goal of this paper is to improve learning for multivariate processes...
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07/12/2016
Predicting the evolution of stationary graph signals
An emerging way of tackling the dimensionality issues arising in the mod...
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06/22/2016
Towards stationary time-vertex signal processing
Graph-based methods for signal processing have shown promise for the ana...
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02/14/2016
Autoregressive Moving Average Graph Filtering
One of the cornerstones of the field of signal processing on graphs are ...
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02/14/2016