57 citations · 67 across the 8 of their papers we have counts for
8 papers
A Simple Efficiency Incremental Learning Framework via Vision-Language Model with Nonlinear Multi-Adapters
Haihua Luo, Xuming Ran, Jiangrong Shen +4
Incremental Learning (IL) aims to learn new tasks while preserving previously acquired knowledge. Integrating the zero-shot learning capabilities of pre-trained vision-language mod…
Representation Finetuning for Continual Learning
Haihua Luo, Xuming Ran, Tommi Kärkkäinen +4
The world is inherently dynamic, and continual learning aims to enable models to adapt to ever-evolving data streams. While pre-trained models have shown powerful performance in co…
FCNCP: A Coupled Nonnegative CANDECOMP/PARAFAC Decomposition Based on Federated Learning
Yukai Cai, Hang Liu, Xiulin Wang +4
In the field of brain science, data sharing across servers is becoming increasingly challenging due to issues such as industry competition, privacy security, and administrative pro…
Fast Learnings of Coupled Nonnegative Tensor Decomposition Using Optimal Gradient and Low-rank Approximation
Xiulin Wang, Jing Liu, Fengyu Cong
Tensor decomposition is a fundamental technique widely applied in signal processing, machine learning, and various other fields. However, traditional tensor decomposition methods e…
Optimal Number of Clusters by Measuring Similarity among Topographies for Spatio-temporal ERP Analysis
Reza Mahini, Peng Xu, Guoliang Chen +6
Averaging amplitudes over consecutive time samples within a time-window is widely used to calculate the amplitude of an event-related potential (ERP) for cognitive neuroscience. Ob…
Sparse Nonnegative CANDECOMP/PARAFAC Decomposition in Block Coordinate Descent Framework: A Comparison Study
Deqing Wang, Fengyu Cong, Tapani Ristaniemi
Nonnegative CANDECOMP/PARAFAC (NCP) decomposition is an important tool to process nonnegative tensor. Sometimes, additional sparse regularization is needed to extract meaningful no…
Double Coupled Canonical Polyadic Decomposition for Joint Blind Source Separation
Xiao-Feng Gong, Qiu-Hua Lin, Feng-Yu Cong +1
Joint blind source separation (J-BSS) is an emerging data-driven technique for multi-set data-fusion. In this paper, J-BSS is addressed from a tensorial perspective. We show how, b…
Diffusion map for clustering fMRI spatial maps extracted by independent component analysis
Tuomo Sipola, Fengyu Cong, Tapani Ristaniemi +4
Functional magnetic resonance imaging (fMRI) produces data about activity inside the brain, from which spatial maps can be extracted by independent component analysis (ICA). In dat…