3 citations · 3 across the 8 of their papers we have counts for
4 papers · 1 filter
SiameseNorm: Breaking the Barrier to Reconciling Pre/Post-Norm
Tianyu Li, Dongchen Han, Zixuan Cao +7
The long-standing tension between Pre- and Post-Norm remains an open problem in Transformer architecture, reflecting a fundamental trade-off between training stability and represen…
Lightweight Time Series Data Valuation on Time Series Foundation Models via In-Context Finetuning
Shunyu Wu, Tianyue Li, Yixuan Leng +4
Time series foundation models (TSFMs) have demonstrated increasing capabilities due to their extensive pretraining on large volumes of diverse time series data. Consequently, the q…
Secure Transfer Learning: Training Clean Models Against Backdoor in (Both) Pre-trained Encoders and Downstream Datasets
Yechao Zhang, Yuxuan Zhou, Tianyu Li +4
Transfer learning from pre-trained encoders has become essential in modern machine learning, enabling efficient model adaptation across diverse tasks. However, this combination of…
Disentangled Parameter-Efficient Linear Model for Long-Term Time Series Forecasting
Yuang Zhao, Tianyu Li, Jiadong Chen +3
Long-term Time Series Forecasting (LTSF) is crucial across various domains, but complex deep models like Transformers are often prone to overfitting on extended sequences. Linear F…