5 papers
BrainDistill: Implantable Motor Decoding with Task-Specific Knowledge Distillation
Yuhan Xie, Jinhan Liu, Xiaoyong Ni +11
Transformer-based neural decoders with large parameter counts, pre-trained on large-scale datasets, have recently outperformed classical machine learning models and small neural ne…
LoFT-LLM: Low-Frequency Time-Series Forecasting with Large Language Models
Jiacheng You, Jingcheng Yang, Yuhang Xie +7
Time-series forecasting in real-world applications such as finance and energy often faces challenges due to limited training data and complex, noisy temporal dynamics. Existing dee…
Multimodal Representation Learning and Fusion
Qihang Jin, Enze Ge, Yuhang Xie +8
Multi-modal learning is a fast growing area in artificial intelligence. It tries to help machines understand complex things by combining information from different sources, like im…
rETF-semiSL: Semi-Supervised Learning for Neural Collapse in Temporal Data
Yuhan Xie, William Cappelletti, Mahsa Shoaran +1
Deep neural networks for time series must capture complex temporal patterns, to effectively represent dynamic data. Self- and semi-supervised learning methods show promising result…
PHM-Bench: A Domain-Specific Benchmarking Framework for Systematic Evaluation of Large Models in Prognostics and Health Management
Puyu Yang, Laifa Tao, Zijian Huang +11
With the rapid advancement of generative artificial intelligence, large language models (LLMs) are increasingly adopted in industrial domains, offering new opportunities for Progno…