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Thu 14:00 Diffusion Curvature for Estimating Local Curvature in High Dimensional Data
Dhananjay Bhaskar · Kincaid MacDonald · Oluwadamilola Fasina · Dawson Thomas · Bastian Rieck · Ian Adelstein · Smita Krishnaswamy
Wed 9:00 A Quantitative Geometric Approach to Neural-Network Smoothness
Zi Wang · Gautam Prakriya · Somesh Jha
On the performance of Direct Loss Minimization for Bayesian Neural Networks
Yadi Wei · Roni Khardon
Thu 14:00 Is $L^2$ Physics Informed Loss Always Suitable for Training Physics Informed Neural Network?
Chuwei Wang · Shanda Li · Di He · Liwei Wang
Tue 9:00 Gradient flow dynamics of shallow ReLU networks for square loss and orthogonal inputs
Etienne Boursier · Loucas PILLAUD-VIVIEN · Nicolas Flammarion
Tue 9:00 Biologically plausible solutions for spiking networks with efficient coding
Veronika Koren · Stefano Panzeri
NeuroSchedule: A Novel Effective GNN-based Scheduling Method for High-level Synthesis
Jun Zeng · Mingyang Kou · Hailong Yao
Affinity Workshop
Determination of Neural Network Parameters for Path Loss Prediction in Very High Frequency Wireless Channel
Abigail Jefia · Segun Popoola · Aderemi A. Atayero
Tue 14:00 Understanding Square Loss in Training Overparametrized Neural Network Classifiers
Tianyang Hu · Jun WANG · Wenjia Wang · Zhenguo Li
IM-Loss: Information Maximization Loss for Spiking Neural Networks
Yufei Guo · Yuanpei Chen · Liwen Zhang · Xiaode Liu · Yinglei Wang · Xuhui Huang · Zhe Ma
Physically-primed deep-neural-networks for generalized undersampled MRI reconstruction
Nitzan Avidan · Moti Freiman
Thu 9:00 Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors
Ravid Shwartz-Ziv · Micah Goldblum · Hossein Souri · Sanyam Kapoor · Chen Zhu · Yann LeCun · Andrew Wilson