35 citations · 54 across the 9 of their papers we have counts for
9 papers
Non-equilibrium physics: from spin glasses to machine and neural learning
Weishun Zhong
Disordered many-body systems exhibit a wide range of emergent phenomena across different scales. These complex behaviors can be utilized for various information processing tasks su…
Many-body localized hidden generative models
Weishun Zhong, Xun Gao, Susanne F. Yelin +1
Born machines are quantum-inspired generative models that leverage the probabilistic nature of quantum states. Here, we present a new architecture called many-body localized (MBL)…
A theory of learning with constrained weight-distribution
Weishun Zhong, Ben Sorscher, Daniel D Lee +1
A central question in computational neuroscience is how structure determines function in neural networks. The emerging high-quality large-scale connectomic datasets raise the quest…
Learning about learning by many-body systems
Weishun Zhong, Jacob M. Gold, Sarah Marzen +2
Diverse many-body systems, from soap bubbles to suspensions to polymers, learn and remember patterns in the drives that push them far from equilibrium. This learning may be leverag…
Quantifying many-body learning far from equilibrium with representation learning
Weishun Zhong, Jacob M. Gold, Sarah Marzen +2
Far-from-equilibrium many-body systems, from soap bubbles to suspensions to polymers, learn the drives that push them. This learning has been observed via thermodynamic properties,…
A Closer Look at Disentangling in -VAE
Harshvardhan Sikka, Weishun Zhong, Jun Yin +1
In many data analysis tasks, it is beneficial to learn representations where each dimension is statistically independent and thus disentangled from the others. If data generating f…
Non-equilibrium statistical mechanics of continuous attractors
Weishun Zhong, Zhiyue Lu, David J Schwab +1
Continuous attractors have been used to understand recent neuroscience experiments where persistent activity patterns encode internal representations of external attributes like he…
Associative pattern recognition through macro-molecular self-assembly
Weishun Zhong, David J. Schwab, Arvind Murugan
We show that macro-molecular self-assembly can recognize and classify high-dimensional patterns in the concentrations of distinct molecular species. Similar to associative neur…
A Holographic c-Theorem for Schrodinger Spacetimes
James T. Liu, Weishun Zhong
We prove a c-theorem for holographic renormalization group flows in a Schrodinger spacetime that demonstrates that the effective radius L(r) monotonically decreases from the UV to…