7 citations · 7 across the 3 of their papers we have counts for
5 papers
Approximate Message Passing for Multi-Layer Estimation in Rotationally Invariant Models
Yizhou Xu, TianQi Hou, ShanSuo Liang +1
We consider the problem of reconstructing the signal and the hidden variables from observations coming from a multi-layer network with rotationally invariant weight matrices. The m…
The price of ignorance: how much does it cost to forget noise structure in low-rank matrix estimation?
Jean Barbier, TianQi Hou, Marco Mondelli +1
We consider the problem of estimating a rank-1 signal corrupted by structured rotationally invariant noise, and address the following question: how well do inference algorithms per…
Sparse superposition codes under VAMP decoding with generic rotational invariant coding matrices
TianQi Hou, YuHao Liu, Teng Fu +1
Sparse superposition codes were originally proposed as a capacity-achieving communication scheme over the gaussian channel, whose coding matrices were made of i.i.d. gaussian entri…
Statistical physics of unsupervised learning with prior knowledge in neural networks
Tianqi Hou, Haiping Huang
Integrating sensory inputs with prior beliefs from past experiences in unsupervised learning is a common and fundamental characteristic of brain or artificial neural computation. H…
Minimal model of permutation symmetry in unsupervised learning
Tianqi Hou, K. Y. Michael Wong, Haiping Huang
Permutation of any two hidden units yields invariant properties in typical deep generative neural networks. This permutation symmetry plays an important role in understanding the c…