1.4k citations · 3k across the 30 of their papers we have counts for
30 papers
ViBE: Visual-to-M/EEG Brain Encoding via Spatio-Temporal VAE and Distribution-Aligned Projection
Ganxi Xu, Zhao-Rong Lai, Yuting Tang +6
Brain encoding models not only serve to decipher how visual stimuli are transformed into neural responses, but also represent a critical step toward visual prostheses that restore…
Efficient Low Rank Attention for Long-Context Inference in Large Language Models
Tenghui Li, Guoxu Zhou, Xuyang Zhao +2
As the length of input text increases, the key-value (KV) cache in LLMs imposes prohibitive GPU memory costs and limits long-context inference on resource constrained devices. Exis…
Scaling Capability in Token Space: An Analysis of Large Vision Language Model
Tenghui Li, Guoxu Zhou, Xuyang Zhao +1
Large language models have demonstrated predictable scaling behaviors with respect to model parameters and training data. This study investigates whether a similar scaling relation…
tnGPS: Discovering Unknown Tensor Network Structure Search Algorithms via Large Language Models (LLMs)
Junhua Zeng, Chao Li, Zhun Sun +2
Tensor networks are efficient for extremely high-dimensional representation, but their model selection, known as tensor network structure search (TN-SS), is a challenging problem.…
Semi-supervised multi-view concept decomposition
Qi Jiang, Guoxu Zhou, Qibin Zhao
Concept Factorization (CF), as a novel paradigm of representation learning, has demonstrated superior performance in multi-view clustering tasks. It overcomes limitations such as t…
Transformed Low-Rank Parameterization Can Help Robust Generalization for Tensor Neural Networks
Andong Wang, Chao Li, Mingyuan Bai +3
Achieving efficient and robust multi-channel data learning is a challenging task in data science. By exploiting low-rankness in the transformed domain, i.e., transformed low-rankne…
An adaptive weighted self-representation method for incomplete multi-view clustering
Lishan Feng, Guoxu Zhou, Jingya Chang
For multi-view data in reality, part of its elements may be missing because of human or machine error. Incomplete multi-view clustering (IMC) clusters the incomplete multi-view dat…
Towards Efficient and Accurate Approximation: Tensor Decomposition Based on Randomized Block Krylov Iteration
Yichun Qiu, Weijun Sun, Guoxu Zhou +1
Efficient and accurate low-rank approximation (LRA) methods are of great significance for large-scale data analysis. Randomized tensor decompositions have emerged as powerful tools…
Latent Matrices for Tensor Network Decomposition and to Tensor Completion
Peilin Yang, Weijun Sun, Qibin Zhao +1
The prevalent fully-connected tensor network (FCTN) has achieved excellent success to compress data. However, the FCTN decomposition suffers from slow computational speed when faci…
A high-order tensor completion algorithm based on Fully-Connected Tensor Network weighted optimization
Peilin Yang, Yonghui Huang, Yuning Qiu +2
Tensor completion aimes at recovering missing data, and it is one of the popular concerns in deep learning and signal processing. Among the higher-order tensor decomposition algori…
Noisy Tensor Completion via Low-rank Tensor Ring
Yuning Qiu, Guoxu Zhou, Qibin Zhao +1
Tensor completion is a fundamental tool for incomplete data analysis, where the goal is to predict missing entries from partial observations. However, existing methods often make t…
Bayesian Robust Tensor Ring Model for Incomplete Multiway Data
Zhenhao Huang, Yuning Qiu, Xinqi Chen +2
Robust tensor completion (RTC) aims to recover a low-rank tensor from its incomplete observation with outlier corruption. The recently proposed tensor ring (TR) model has demonstra…
Multi-view Data Classification with a Label-driven Auto-weighted Strategy
Yuyuan Yu, Guoxu Zhou, Haonan Huang +2
Distinguishing the importance of views has proven to be quite helpful for semi-supervised multi-view learning models. However, existing strategies cannot take advantage of semi-sup…
Efficient Tensor Robust PCA under Hybrid Model of Tucker and Tensor Train
Yuning Qiu, Guoxu Zhou, Zhenhao Huang +2
Tensor robust principal component analysis (TRPCA) is a fundamental model in machine learning and computer vision. Recently, tensor train (TT) decomposition has been verified effec…
Toward Understanding Convolutional Neural Networks from Volterra Convolution Perspective
Tenghui Li, Guoxu Zhou, Yuning Qiu +1
We make an attempt to understanding convolutional neural network by exploring the relationship between (deep) convolutional neural networks and Volterra convolutions. We propose a…
Fast Hypergraph Regularized Nonnegative Tensor Ring Factorization Based on Low-Rank Approximation
Xinhai Zhao, Yuyuan Yu, Guoxu Zhou +2
For the high dimensional data representation, nonnegative tensor ring (NTR) decomposition equipped with manifold learning has become a promising model to exploit the multi-dimensio…
Improving EEG Decoding via Clustering-based Multi-task Feature Learning
Yu Zhang, Tao Zhou, Wei Wu +4
Accurate electroencephalogram (EEG) pattern decoding for specific mental tasks is one of the key steps for the development of brain-computer interface (BCI), which is quite challen…
Graph Regularized Nonnegative Tensor Ring Decomposition for Multiway Representation Learning
Yuyuan Yu, Guoxu Zhou, Ning Zheng +2
Tensor ring (TR) decomposition is a powerful tool for exploiting the low-rank nature of multiway data and has demonstrated great potential in a variety of important applications. I…
Tensor-Ring Nuclear Norm Minimization and Application for Visual Data Completion
Jinshi Yu, Chao Li, Qibin Zhao +1
Tensor ring (TR) decomposition has been successfully used to obtain the state-of-the-art performance in the visual data completion problem. However, the existing TR-based completio…
Learning the Hierarchical Parts of Objects by Deep Non-Smooth Nonnegative Matrix Factorization
Jinshi Yu, Guoxu Zhou, Andrzej Cichocki +1
Nonsmooth Nonnegative Matrix Factorization (nsNMF) is capable of producing more localized, less overlapped feature representations than other variants of NMF while keeping satisfac…
Deep Approximately Orthogonal Nonnegative Matrix Factorization for Clustering
Yuning Qiu, Guoxu Zhou, Kan Xie
Nonnegative Matrix Factorization (NMF) is a widely used technique for data representation. Inspired by the expressive power of deep learning, several NMF variants equipped with dee…
Tensor Ring Decomposition
Qibin Zhao, Guoxu Zhou, Shengli Xie +2
Tensor networks have in recent years emerged as the powerful tools for solving the large-scale optimization problems. One of the most popular tensor network is tensor train (TT) de…
Linked Component Analysis from Matrices to High Order Tensors: Applications to Biomedical Data
Guoxu Zhou, Qibin Zhao, Yu Zhang +3
With the increasing availability of various sensor technologies, we now have access to large amounts of multi-block (also called multi-set, multi-relational, or multi-view) data th…
Decomposition of Big Tensors With Low Multilinear Rank
Guoxu Zhou, Andrzej Cichocki, Shengli Xie
Tensor decompositions are promising tools for big data analytics as they bring multiple modes and aspects of data to a unified framework, which allows us to discover complex intern…