3 papers
cond-mat.dis-nn2026
Phase Transitions in Neural Networks Pruning
Diego Pesce, Yang-Hui He, Guido Caldarelli
Deep neural networks are strongly over-parameterized, often containing far more weights than required for their task. Although such redundancy can aid optimization, it leads to ine…
cs.CV2025
Unifying Dataset Pruning and Distillation for Efficient Large-scale Compression
Lingao Xiao, Songhua Liu, Yang He +1
Dataset pruning (DP) and dataset distillation (DD) fundamentally differ in their outputs: DP selects original image subsets, while DD generates synthetic images. Recently, DD's inc…
cs.CE2023
Incremental Neural Controlled Differential Equations for Modeling of Path-dependent Material Behavior
Yangzi He, Shabnam J. Semnani
Data-driven surrogate modeling has emerged as a promising approach for reducing computational expenses of multiscale simulations. Recurrent Neural Network (RNN) is a common choice…