Summary of Fine-tuning a Time Series Foundation Model with Wasserstein Loss, by Andrei Chernov
Fine-Tuning a Time Series Foundation Model with Wasserstein Lossby Andrei ChernovFirst submitted to arxiv on:…
Fine-Tuning a Time Series Foundation Model with Wasserstein Lossby Andrei ChernovFirst submitted to arxiv on:…
ReFine: Boosting Time Series Prediction of Extreme Events by Reweighting and Fine-tuningby Jimeng Shi, Azam…
Flat-LoRA: Low-Rank Adaption over a Flat Loss Landscapeby Tao Li, Zhengbao He, Yujun Li, Yasheng…
ChronoGAN: Supervised and Embedded Generative Adversarial Networks for Time Series Generationby MohammadReza EskandariNasab, Shah Muhammad…
Logically Consistent Language Models via Neuro-Symbolic Integrationby Diego Calanzone, Stefano Teso, Antonio VergariFirst submitted to…
Non-overlapping, Schwarz-type Domain Decomposition Method for Physics and Equality Constrained Artificial Neural Networksby Qifeng Hu,…
A Margin-Maximizing Fine-Grained Ensemble Methodby Jinghui Yuan, Hao Chen, Renwei Luo, Feiping NieFirst submitted to…
Unraveling the Hessian: A Key to Smooth Convergence in Loss Function Landscapesby Nikita Kiselev, Andrey…
Continual Learning of Conjugated Visual Representations through Higher-order Motion Flowsby Simone Marullo, Matteo Tiezzi, Marco…
FedNE: Surrogate-Assisted Federated Neighbor Embedding for Dimensionality Reductionby Ziwei Li, Xiaoqi Wang, Hong-You Chen, Han-Wei…