9 citations · 15 across the 5 of their papers we have counts for
5 papers
Neural Langevin Machine: a local asymmetric learning rule can be creative
Zhendong Yu, Weizhong Huang, Haiping Huang
Fixed points of recurrent neural networks can be leveraged to store and generate information. These fixed points are captured by the Boltzmann-Gibbs measure, which leads to neural…
Network reconstruction may not mean dynamics prediction
Zhendong Yu, Haiping Huang
With an increasing amount of observations on the dynamics of many complex systems, it is required to reveal the underlying mechanisms behind these complex dynamics, which is fundam…
Spin glass model of in-context learning
Yuhao Li, Ruoran Bai, Haiping Huang
Large language models show a surprising in-context learning ability -- being able to use a prompt to form a prediction for a query, yet without additional training, in stark contra…
Nonequilbrium physics of generative diffusion models
Zhendong Yu, Haiping Huang
Generative diffusion models apply the concept of Langevin dynamics in physics to machine leaning, attracting a lot of interests from engineering, statistics and physics, but a comp…
Spiking mode-based neural networks
Zhanghan Lin, Haiping Huang
Spiking neural networks play an important role in brain-like neuromorphic computations and in studying working mechanisms of neural circuits. One drawback of training a large scale…