arXiv AI
Which Algorithms Can Graph Neural Networks Learn?
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Learning to Execute Graph Algorithms Exactly with Graph Neural Networks
arXiv:2601. 23207v2 Announce Type: replace-cross Abstract: Understanding what graph neural networks can learn, especially their ability to learn to execute algorithms, remains a central theoretical challenge.
arXiv Machine Learning
Jul 30
On the Rademacher Complexity of Graph Neural Networks: Unifying Expressivity and Geometry
arXiv:2510. 10101v4 Announce Type: replace Abstract: Understanding the interplay between generalization, expressivity, and the geometry of the input space is a central challenge in graph learning.
arXiv Machine Learning
Jul 24
New Complexity-Theoretic Frontiers of Tractability for Neural Network Training
arXiv:2607. 20811v1 Announce Type: new Abstract: In spite of the fundamental role of neural networks in contemporary machine learning research, our understanding of the computational complexity of optimally training neural networks remains incomplete even when dealing with the simplest kinds of activation functions.
arXiv AI
Jul 15
Efficiently Learning Branching Networks for Multitask Algorithmic Reasoning
arXiv:2512. 01113v2 Announce Type: replace-cross Abstract: Algorithmic reasoning -- the ability to perform step-by-step logical inference -- is a synthetic benchmark for evaluating multi-step reasoning abilities, designed for graph neural networks and also for transformer models.
arXiv Machine Learning
Aug 4
Optimized Piecewise Affine Abstractions of Neural Networks with Learnable Activation Functions
arXiv:2602. 06737v2 Announce Type: replace Abstract: We present a generalized framework for the range verification of neural networks featuring non-linear activation functions.
arXiv Machine Learning
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A Dynamical Systems Perspective on the Analysis of Neural Networks
arXiv:2507. 05164v2 Announce Type: replace-cross Abstract: In this chapter, we utilize dynamical systems to analyze several aspects of machine learning algorithms.