2 papers
cs.LG2023
Expressivity of ReLU-Networks under Convex Relaxations
Maximilian Baader, Mark Niklas Müller, Yuhao Mao +1
Convex relaxations are a key component of training and certifying provably safe neural networks. However, despite substantial progress, a wide and poorly understood accuracy gap to…
cs.LG2023
SMAP: A Novel Heterogeneous Information Framework for Scenario-based Optimal Model Assignment
Zekun Qiu, Zhipu Xie, Zehua Ji +2
The increasing maturity of big data applications has led to a proliferation of models targeting the same objectives within the same scenarios and datasets. However, selecting the m…