10 citations · 18 across the 9 of their papers we have counts for
3 papers · 1 filter
InRank: Incremental Low-Rank Learning
Jiawei Zhao, Yifei Zhang, Beidi Chen +2
The theory of greedy low-rank learning (GLRL) aims to explain the impressive generalization capabilities of deep learning. It proves that stochastic gradient-based training implici…
Competitive Physics Informed Networks
Qi Zeng, Yash Kothari, Spencer H. Bryngelson +1
Neural networks can be trained to solve partial differential equations (PDEs) by using the PDE residual as the loss function. This strategy is called "physics-informed neural netwo…
Polymatrix Competitive Gradient Descent
Jeffrey Ma, Alistair Letcher, Florian Schäfer +2
Many economic games and machine learning approaches can be cast as competitive optimization problems where multiple agents are minimizing their respective objective function, which…