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Workshop
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Learning Set Functions with Implicit Differentiation
Gözde Özcan · Chengzhi Shi · Stratis Ioannidis
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Workshop
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Combining Neural Networks and Symbolic Regression for Analytical Lyapunov Function Discovery
Jie Feng · Haohan Zou · Yuanyuan Shi
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Poster
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Wed 4:30
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Graph As Point Set
Xiyuan Wang · Pan Li · Muhan Zhang
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Poster
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Thu 2:30
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Exploring the Complexity of Deep Neural Networks through Functional Equivalence
Guohao Shen
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Workshop
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BiPer: Binary Neural Networks using a Periodic Function
Edwin Vargas · Claudia Correa · Carlos Hinojosa · Henry Arguello
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Workshop
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Message-Passing Monte Carlo: Generating low-discrepancy point sets via Graph Neural Networks
T. Konstantin Rusch · Nathan Kirk · Michael Bronstein · Christiane Lemieux · Daniela Rus
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Workshop
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Accelerating Electron Dynamics Simulations through Machine Learned Time Propagators
Karan Shah · Attila Cangi
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Workshop
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Regularized KL-Divergence for Well-Defined Function-Space Variational Inference in Bayesian neural networks
Tristan Cinquin · Robert Bamler
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Poster
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Thu 4:30
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Physics-Informed Neural Network Policy Iteration: Algorithms, Convergence, and Verification
Yiming Meng · Ruikun Zhou · Amartya Mukherjee · Maxwell Fitzsimmons · Christopher Song · Jun Liu
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Poster
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Thu 4:30
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Learning from Integral Losses in Physics Informed Neural Networks
Ehsan Saleh · Saba Ghaffari · Timothy Bretl · Luke Olson · Matthew West
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Workshop
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Asynchrony Invariance Loss Functions for Graph Neural Networks
Pablo Monteagudo-Lago · Arielle Rosinski · Andrew Dudzik · Petar Veličković
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Poster
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Wed 4:30
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Universal Consistency of Wide and Deep ReLU Neural Networks and Minimax Optimal Convergence Rates for Kolmogorov-Donoho Optimal Function Classes
Hyunouk Ko · Xiaoming Huo
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