11 papers
Learning to Adapt: In-Context Learning Beyond Stationarity
Zhen Qin, Jiachen Jiang, Zhihui Zhu
Transformer models have become foundational across a wide range of scientific and engineering domains due to their strong empirical performance. A key capability underlying their s…
In-Context Learning for Non-Stationary MIMO Equalization
Jiachen Jiang, Zhen Qin, Zhihui Zhu
Channel equalization is fundamental for mitigating distortions such as frequency-selective fading and inter-symbol interference. Unlike standard supervised learning approaches that…
Quantum State Tomography for Tensor Networks in Two Dimensions
Zhen Qin, Zhihui Zhu
Recent work has shown that for one-dimensional quantum states that can be effectively approximated by matrix product operators (MPOs), a polynomial number of copies of the state su…
Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery
Xiao Liang, Zhen Qin, Zhihui Zhu +1
This paper presents a geometric analysis of the simultaneous blind deconvolution and phase retrieval (BDPR) problem via a structured low-rank tensor recovery framework. Due to the…
A Scalable Factorization Approach for High-Order Structured Tensor Recovery
Zhen Qin, Michael B. Wakin, Zhihui Zhu
Tensor decompositions, which represent an -order tensor using approximately factors of much smaller dimensions, can significantly reduce the number of parameters. This is pa…
On the Convergence of Gradient Descent on Learning Transformers with Residual Connections
Zhen Qin, Jinxin Zhou, Jiachen Jiang +1
Transformer models have emerged as fundamental tools across various scientific and engineering disciplines, owing to their outstanding performance in diverse applications. Despite…