no code implementations • 28 Mar 2024 • Xinyu Bian, Yuhao Liu, Yizhou Xu, Tianqi Hou, Wenjie Wang, Yuyi Mao, Jun Zhang
Simulation results demonstrate the effectiveness of our proposed decentralized precoding scheme, which achieves performance similar to the optimal centralized precoding scheme.
no code implementations • 15 Mar 2024 • Yuhao Liu, Xinyu Bian, Yizhou Xu, Tianqi Hou, Wenjie Wang, Yuyi Mao, Jun Zhang
In order to control the inter-cell interference for a multi-cell multi-user multiple-input multiple-output network, we consider the precoder design for coordinated multi-point with downlink coherent joint transmission.
no code implementations • 7 Feb 2023 • Teng Fu, Yuhao Liu, Jean Barbier, Marco Mondelli, Shansuo Liang, Tianqi Hou
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 values.
1 code implementation • 3 Dec 2022 • Yizhou Xu, Tianqi Hou, Shansuo Liang, Marco Mondelli
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.
no code implementations • 24 Aug 2022 • Zijian Jiang, Ziming Chen, Tianqi Hou, Haiping Huang
Neural networks with recurrent asymmetric couplings are important to understand how episodic memories are encoded in the brain.
no code implementations • 20 May 2022 • Jean Barbier, Tianqi Hou, Marco Mondelli, Manuel Sáenz
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 perform when the noise statistics is unknown and hence Gaussian noise is assumed?
no code implementations • 6 Nov 2019 • Tianqi Hou, Haiping Huang
Here, we propose a statistical physics model of unsupervised learning with prior knowledge, revealing that the sensory inputs drive a series of continuous phase transitions related to spontaneous intrinsic-symmetry breaking.
no code implementations • 30 Apr 2019 • Tianqi Hou, K. Y. Michael Wong, Haiping Huang
Remarkably, we find that the embedded correlation between two receptive fields of hidden units reduces the critical data size.