Haizhao Yang

AutoSR not only recovers complex equations but also retains the entire scientific rationale behind them, revolutionizing how we approach symbolic regression.

Achieving an unprecedented reduction in storage costs for high-dimensional function optimization, this method allows for efficient training of deep neural networks in dimensions previously deemed impractical.

Sparsification in attention mechanisms can drastically alter content influence, with higher compression ratios leading to surprising shifts in model output that standard accuracy metrics overlook.

Renormalization can redefine how we understand and address the sim-to-real gap in robotics by leveraging effective parameters that capture omitted dynamics.