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pro vyhledávání: '"Bakman, Yavuz Faruk"'
Autor:
Mushtaq, Erum, Yaldiz, Duygu Nur, Bakman, Yavuz Faruk, Ding, Jie, Tao, Chenyang, Dimitriadis, Dimitrios, Avestimehr, Salman
Continual self-supervised learning (CSSL) learns a series of tasks sequentially on the unlabeled data. Two main challenges of continual learning are catastrophic forgetting and task confusion. While CSSL problem has been studied to address the catast
Externí odkaz:
http://arxiv.org/abs/2407.12188
Autor:
Yaldiz, Duygu Nur, Bakman, Yavuz Faruk, Buyukates, Baturalp, Tao, Chenyang, Ramakrishna, Anil, Dimitriadis, Dimitrios, Avestimehr, Salman
In this work, we introduce the Learnable Response Scoring Function (LARS) for Uncertainty Estimation (UE) in generative Large Language Models (LLMs). Current scoring functions for probability-based UE, such as length-normalized scoring and semantic c
Externí odkaz:
http://arxiv.org/abs/2406.11278
Large language models (LLMs) have shown impressive capabilities in tasks such as machine translation, text summarization, question answering, and solving complex mathematical problems. However, their primary training on data-rich languages like Engli
Externí odkaz:
http://arxiv.org/abs/2406.05569
Autor:
Bakman, Yavuz Faruk, Yaldiz, Duygu Nur, Buyukates, Baturalp, Tao, Chenyang, Dimitriadis, Dimitrios, Avestimehr, Salman
Generative Large Language Models (LLMs) are widely utilized for their excellence in various tasks. However, their tendency to produce inaccurate or misleading outputs poses a potential risk, particularly in high-stakes environments. Therefore, estima
Externí odkaz:
http://arxiv.org/abs/2402.11756
Federated Learning (FL) has gained significant attraction due to its ability to enable privacy-preserving training over decentralized data. Current literature in FL mostly focuses on single-task learning. However, over time, new tasks may appear in t
Externí odkaz:
http://arxiv.org/abs/2309.01289
Federated Learning (FL) aims to train a machine learning (ML) model in a distributed fashion to strengthen data privacy with limited data migration costs. It is a distributed learning framework naturally suitable for privacy-sensitive medical imaging
Externí odkaz:
http://arxiv.org/abs/2304.09327