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pro vyhledávání: '"Tack A"'
Deep learning has shown tremendous progress in a wide range of digital pathology and medical image classification tasks. Its integration into safe clinical decision-making support requires robust and reliable models. However, real-world data comes wi
Externí odkaz:
http://arxiv.org/abs/2408.08432
Autor:
Nam, Jaehyun, Song, Woomin, Park, Seong Hyeon, Tack, Jihoon, Yun, Sukmin, Kim, Jaehyung, Oh, Kyu Hwan, Shin, Jinwoo
Learning with a limited number of labeled data is a central problem in real-world applications of machine learning, as it is often expensive to obtain annotations. To deal with the scarcity of labeled data, transfer learning is a conventional approac
Externí odkaz:
http://arxiv.org/abs/2408.11063
Learning effective representations from raw data is crucial for the success of deep learning methods. However, in the tabular domain, practitioners often prefer augmenting raw column features over using learned representations, as conventional tree-b
Externí odkaz:
http://arxiv.org/abs/2406.08527
The remarkable capabilities and easy accessibility of large language models (LLMs) have significantly increased societal risks (e.g., fake news generation), necessitating the development of LLM-generated text (LGT) detection methods for safe usage. H
Externí odkaz:
http://arxiv.org/abs/2405.17382
Copepods participate in large-scale diel vertical migrations (DVM) as primary consumers in marine ecosystems. Given that they are negatively buoyant, gravity facilitates their downward cruising but impedes their upward relocation. In principle, overc
Externí odkaz:
http://arxiv.org/abs/2404.04413
Autor:
Chen, Shengzhuang, Tack, Jihoon, Yang, Yunqiao, Teh, Yee Whye, Schwarz, Jonathan Richard, Wei, Ying
Recent successes suggest that parameter-efficient fine-tuning of foundation models as the state-of-the-art method for transfer learning in vision, replacing the rich literature of alternatives such as meta-learning. In trying to harness the best of b
Externí odkaz:
http://arxiv.org/abs/2403.08477
Autor:
Tack, Jihoon, Kim, Jaehyung, Mitchell, Eric, Shin, Jinwoo, Teh, Yee Whye, Schwarz, Jonathan Richard
Due to the rapid generation and dissemination of information, large language models (LLMs) quickly run out of date despite enormous development costs. To address the crucial need to keep models updated, online learning has emerged as a critical tool
Externí odkaz:
http://arxiv.org/abs/2403.04317
Despite its practical importance across a wide range of modalities, recent advances in self-supervised learning (SSL) have been primarily focused on a few well-curated domains, e.g., vision and language, often relying on their domain-specific knowled
Externí odkaz:
http://arxiv.org/abs/2310.16318
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-13 (2024)
Abstract The primary component of the lipid barrier on human hair, which is essential for defense against aging and environmental stresses, is 18-methyleicosanoic acid (18-MEA), which provides hydrophobic properties and protective benefits. Since 18-
Externí odkaz:
https://doaj.org/article/b268187b9eff4cc1a92f249369863256
This paper describes the results of the first shared task on the generation of teacher responses in educational dialogues. The goal of the task was to benchmark the ability of generative language models to act as AI teachers, replying to a student in
Externí odkaz:
http://arxiv.org/abs/2306.06941