1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.LG2026
Rethinking Evaluation Paradigms in IBP-based Certified Training
Konstantin Kaulen, Hadar Shavit, Holger H. Hoos
Deep neural networks achieve strong performance on many supervised learning tasks but remain vulnerable to adversarial perturbations. Neural network verification provides mathemati…
cs.LG2026★ 1 cited
GraphBench: Next-generation graph learning benchmarking
Timo Stoll, Chendi Qian, Ben Finkelshtein +16
Machine learning on graphs has made substantial progress across domains such as molecular property prediction and chip design. Yet benchmarking practices remain fragmented, often r…