Paper Review
1.[Review] Accurate Parameter-Efficient Test-Time Adaptation for Time Series Forecasting (PETSA)
Medeiros, Heitor R., et al. "Accurate Parameter-Efficient Test-Time Adaptation for Time Series Forecasting." ICML 2025.
2025년 9월 20일
2.[Review] Shift-Aware Test-Time Adaptation and Benchmarking for Time-Series Forecasting (DynaTTA)
Grover, Shivam, and Ali Etemad. "Shift-Aware Test Time Adaptation and Benchmarking for Time-Series Forecasting." ICML 2025.https://github.com/shi
2025년 9월 21일
3.[Review] Federated Foundation Models on Heterogeneous Time Series (FFTS)
Chen, Shengchao, et al. "Federated foundation models on heterogeneous time series." Proceedings of the AAAI Conference on Artificial Intelligence. Vol
2025년 9월 24일
4.[Review] Communication-Efficient Learning of Deep Networks from Decentralized Data (FedAvg)
McMahan, Brendan, et al. "Communication-efficient learning of deep networks from decentralized data." Artificial intelligence and statistics. PMLR, 20
2025년 10월 4일
5.[Review] Less is More: Recursive Reasoning with Tiny Networks (TRM)
Jolicoeur-Martineau, Alexia. "Less is More: Recursive Reasoning with Tiny Networks.", arXiv, 2025. 리뷰에 앞서 2025년 10월, 삼성 AI 연구팀, SAIT에서 새로운 AI 모델을 제안했
2025년 11월 2일