Tabular foundation models suffer from test-time adversarial vulnerabilities that degrade accuracy and enable transferable attacks, but incremental adversarial in-context learning improves robustness on multiple benchmarks.
Homological neural networks: A sparse architecture for multivariate complex- ity
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HNNs recover known sparse hierarchies on synthetic tasks and match or exceed dense DNNs on real datasets while using orders of magnitude fewer parameters and showing lower hyperparameter sensitivity.
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On the Robustness of Tabular Foundation Models: Test-Time Attacks and In-Context Defenses
Tabular foundation models suffer from test-time adversarial vulnerabilities that degrade accuracy and enable transferable attacks, but incremental adversarial in-context learning improves robustness on multiple benchmarks.
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Compositional Sparsity as an Inductive Bias for Neural Architecture Design
HNNs recover known sparse hierarchies on synthetic tasks and match or exceed dense DNNs on real datasets while using orders of magnitude fewer parameters and showing lower hyperparameter sensitivity.