137 citations · 351 across the 41 of their papers we have counts for
4 papers · 1 filter
Neuroevolution of Physics-Informed Neural Nets: Benchmark Problems and Comparative Results
Nicholas Sung Wei Yong, Jian Cheng Wong, Pao-Hsiung Chiu +3
The potential of learned models for fundamental scientific research and discovery is drawing increasing attention worldwide. Physics-informed neural networks (PINNs), where the los…
A Survey of Learning on Small Data: Generalization, Optimization, and Challenge
Xiaofeng Cao, Weixin Bu, Shengjun Huang +4
Learning on big data brings success for artificial intelligence (AI), but the annotation and training costs are expensive. In future, learning on small data that approximates the g…
Masked Frequency Modeling for Self-Supervised Visual Pre-Training
Jiahao Xie, Wei Li, Xiaohang Zhan +3
We present Masked Frequency Modeling (MFM), a unified frequency-domain-based approach for self-supervised pre-training of visual models. Instead of randomly inserting mask tokens t…
Jack and Masters of all Trades: One-Pass Learning Sets of Model Sets From Large Pre-Trained Models
Han Xiang Choong, Yew-Soon Ong, Abhishek Gupta +2
For deep learning, size is power. Massive neural nets trained on broad data for a spectrum of tasks are at the forefront of artificial intelligence. These large pre-trained models…