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pro vyhledávání: '"Burapacheep, Jirayu"'
Autor:
Burapacheep, Jirayu, Li, Yixuan
The ability to detect out-of-distribution (OOD) inputs is critical to guarantee the reliability of classification models deployed in an open environment. A fundamental challenge in OOD detection is that a discriminative classifier is typically traine
Externí odkaz:
http://arxiv.org/abs/2408.04851
This paper introduces the ColorSwap dataset, designed to assess and improve the proficiency of multimodal models in matching objects with their colors. The dataset is comprised of 2,000 unique image-caption pairs, grouped into 1,000 examples. Each ex
Externí odkaz:
http://arxiv.org/abs/2402.04492
Aligning large language models with human objectives is paramount, yet common approaches including RLHF suffer from unstable and resource-intensive training. In response to this challenge, we introduce ARGS, Alignment as Reward-Guided Search, a novel
Externí odkaz:
http://arxiv.org/abs/2402.01694
Autoregressive language models, which use deep learning to produce human-like texts, have become increasingly widespread. Such models are powering popular virtual assistants in areas like smart health, finance, and autonomous driving. While the param
Externí odkaz:
http://arxiv.org/abs/2209.13627
Akademický článek
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Publikováno v:
International Dental Journal; 20240101, Issue: Preprints