Summary of Approximating Two-layer Relu Networks For Hidden State Analysis in Differential Privacy, by Antti Koskela
Approximating Two-Layer ReLU Networks for Hidden State Analysis in Differential Privacyby Antti KoskelaFirst submitted to…
Approximating Two-Layer ReLU Networks for Hidden State Analysis in Differential Privacyby Antti KoskelaFirst submitted to…
NeuroSteiner: A Graph Transformer for Wirelength Estimationby Sahil Manchanda, Dana Kianfar, Markus Peschl, Romain Lepert,…
Convergence of Implicit Gradient Descent for Training Two-Layer Physics-Informed Neural Networksby Xianliang Xu, Ting Du,…
Improving the performance of Stein variational inference through extreme sparsification of physically-constrained neural network modelsby…
Graph Neural Networks Gone Hogwildby Olga Solodova, Nick Richardson, Deniz Oktay, Ryan P. AdamsFirst submitted…
On the Trade-off between Flatness and Optimization in Distributed Learningby Ying Cao, Zhaoxian Wu, Kun…
Semi-adaptive Synergetic Two-way Pseudoinverse Learning Systemby Binghong Liu, Ziqi Zhao, Shupan Li, Ke WangFirst submitted…
Why Line Search when you can Plane Search? SO-Friendly Neural Networks allow Per-Iteration Optimization of…
Complex fractal trainability boundary can arise from trivial non-convexityby Yizhou LiuFirst submitted to arxiv on:…
Evaluating the design space of diffusion-based generative modelsby Yuqing Wang, Ye He, Molei TaoFirst submitted…