activity
20242026
most citedUnderstanding the Difficulty of Solving Cauchy Problems with PINNs

1 citations · 1 across the 3 of their papers we have counts for

collaborators

5 papers

cs.LG2026

A Survey of Weight Space Learning: Understanding, Representation, and Generation

Xiaolong Han, Zehong Wang, Bo Zhao +8

Neural network weights are typically viewed as the end product of training, while most deep learning research focuses on data, features, and architectures. However, recent advances…

cs.LG2026

Demystifying Mergeability: Interpretable Properties to Predict Model Merging Success

Luca Zhou, Bo Zhao, Rose Yu +1

Model merging combines knowledge from separately fine-tuned models, yet the factors driving its success remain poorly understood. While recent work treats mergeability as an intrin…

cs.CL2025

Emergence of Hierarchical Emotion Organization in Large Language Models

Maya Okawa, Bo Zhao, Eric J. Bigelow +4

As large language models (LLMs) increasingly power conversational agents, understanding how they model users' emotional states is critical for ethical deployment. Inspired by emoti…

cs.LG2025

Improving Learning to Optimize Using Parameter Symmetries

Guy Zamir, Aryan Dokania, Bo Zhao +1

We analyze a learning-to-optimize (L2O) algorithm that exploits parameter space symmetry to enhance optimization efficiency. Prior work has shown that jointly learning symmetry tra…

cs.LG20241 cited

Understanding the Difficulty of Solving Cauchy Problems with PINNs

Tao Wang, Bo Zhao, Sicun Gao +1

Physics-Informed Neural Networks (PINNs) have gained popularity in scientific computing in recent years. However, they often fail to achieve the same level of accuracy as classical…