18 citations · 66 across the 11 of their papers we have counts for
11 papers
Capacity-Achieving Sparse Regression Codes via Vector Approximate Message Passing
Yizhou Xu, YuHao Liu, ShanSuo Liang +4
Sparse regression codes (SPARCs) are a promising coding scheme that can approach the Shannon limit over Additive White Gaussian Noise (AWGN) channels. Previous works have proven th…
Mismatched estimation of non-symmetric rank-one matrices corrupted by structured noise
Teng Fu, YuHao Liu, Jean Barbier +3
We study the performance of a Bayesian statistician who estimates a rank-one signal corrupted by non-symmetric rotationally invariant noise with a generic distribution of singular…
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…
Spectrum of non-Hermitian deep-Hebbian neural networks
Zijian Jiang, Ziming Chen, Tianqi Hou +1
Neural networks with recurrent asymmetric couplings are important to understand how episodic memories are encoded in the brain. Here, we integrate the experimental observation of w…
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 with rotational invariant coding matrices for memoryless channels
YuHao Liu, Teng Fu, Jean Barbier +1
We recently showed in [1] the superiority of certain structured coding matrices ensembles (such as partial row-orthogonal) for sparse superposition codes when compared with purely…
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…
Eigenvalue spectrum of neural networks with arbitrary Hebbian length
Jianwen Zhou, Zijian Jiang, Tianqi Hou +3
Associative memory is a fundamental function in the brain. Here, we generalize the standard associative memory model to include long-range Hebbian interactions at the learning stag…
Associative memory model with arbitrary Hebbian length
Zijian Jiang, Jianwen Zhou, Tianqi Hou +2
Conversion of temporal to spatial correlations in the cortex is one of the most intriguing functions in the brain. The learning at synapses triggering the correlation conversion ca…
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…