3 citations · 4 across the 11 of their papers we have counts for
11 papers
A Novel Block-Alternating Iterative Algorithm for Retrieving Top- Elements from Factorized Tensors
Chuanfu Xiao, Jiaxin Zeng
Tensors, especially higher-order tensors, are typically represented in low-rank formats to preserve the main information of the high-dimensional data while saving memory space. In…
AnTKV: Anchor Token-Aware Sub-Bit Vector Quantization for KV Cache in Large Language Models
Zeyu Li, Chuanfu Xiao, Yang Wang +6
Quantization has emerged as an effective and lightweight solution to reduce the memory footprint of the KV cache in Large Language Models. Nevertheless, minimizing the accuracy deg…
Provable Low-Rank Tensor-Train Approximations in the Inverse of Large-Scale Structured Matrices
Chuanfu Xiao, Kejun Tang, Zhitao Zhu
This paper studies the low-rank property of the inverse of a class of large-scale structured matrices in the tensor-train (TT) format, which is typically discretized from different…
HaTT: Hadamard avoiding TT recompression
Zhonghao Sun, Jizu Huang, Chuanfu Xiao +1
The Hadamard product of tensor train (TT) tensors is a fundamental nonlinear operation in scientific computing and data analysis. However, due to its tendency to significantly incr…
SampleAttention: Near-Lossless Acceleration of Long Context LLM Inference with Adaptive Structured Sparse Attention
Qianchao Zhu, Jiangfei Duan, Chang Chen +6
Large language models (LLMs) now support extremely long context windows, but the quadratic complexity of vanilla attention results in significantly long Time-to-First-Token (TTFT)…
APTT: An accuracy-preserved tensor-train method for the Boltzmann-BGK equation
Zhitao Zhu, Chuanfu Xiao, Kejun Tang +2
Solving the Boltzmann-BGK equation with traditional numerical methods suffers from high computational and memory costs due to the curse of dimensionality. In this paper, we propose…
HOSCF: Efficient decoupling algorithms for finding the best rank-one approximation of higher-order tensors
Chuanfu Xiao, Zeyu Li, Chao Yang
Best rank-one approximation is one of the most fundamental tasks in tensor computation. In order to fully exploit modern multi-core parallel computers, it is necessary to develop d…
Tensor-Based Sketching Method for the Low-Rank Approximation of Data Streams
Cuiyu Liu, Chuanfu Xiao, Mingshuo Ding +1
Low-rank approximation in data streams is a fundamental and significant task in computing science, machine learning and statistics. Multiple streaming algorithms have emerged over…
A rank-adaptive higher-order orthogonal iteration algorithm for truncated Tucker decomposition
Chuanfu Xiao, Chao Yang
We propose a novel rank-adaptive higher-order orthogonal iteration (HOOI) algorithm to compute the truncated Tucker decomposition of higher-order tensors with a given error toleran…
a-Tucker: Input-Adaptive and Matricization-Free Tucker Decomposition for Dense Tensors on CPUs and GPUs
Min Li, Chuanfu Xiao, Chao Yang
Tucker decomposition is one of the most popular models for analyzing and compressing large-scale tensorial data. Existing Tucker decomposition algorithms usually rely on a single s…
Efficient Alternating Least Squares Algorithms for Low Multilinear Rank Approximation of Tensors
Chuanfu Xiao, Chao Yang, Min Li
The low multilinear rank approximation, also known as the truncated Tucker decomposition, has been extensively utilized in many applications that involve higher-order tensors. Popu…