Deep Learning
Deep linear networks for regression are implicitly regularized towards flat minima
The largest eigenvalue of the Hessian, or sharpness, of neural networks is a key quantity to understand their optimization dynamics.
PINNacle: A Comprehensive Benchmark of Physics-Informed Neural Networks for Solving PDEs
Partial Differential Equations (PDEs) is still lacking. This study introduces PIN-Nacle, a benchmarking tool designed to fill this gap.
Expanding Sparse Tuning for Low Memory Usage
Parameter-efficient fine-tuning (PEFT) is an effective method for adapting pre-trained vision models to downstream tasks by tuning a small subset of parameters.
Few-shot Algorithms for Consistent Neural Decoding (FALCON) Benchmark Brianna M. Karpowicz 1,2 Joel Y e
However, progress is limited by a lack of standardized datasets and comparison metrics, causing methods to be compared in an ad hoc manner.
Set-based Neural Network Encoding Without Weight Tying Bruno Andreis
Furthermore, our S et-based N eural network E ncoder (SNE) takes into consideration the hierarchical computational structure of neural networks.
