Zobrazeno 1 - 10
of 102 827
pro vyhledávání: '"Ok, A."'
Large language models (LLMs) have shown remarkable versatility across tasks, but aligning them with individual human preferences remains challenging due to the complexity and diversity of these preferences. Existing methods often overlook the fact th
Externí odkaz:
http://arxiv.org/abs/2411.00524
Inverse Optimization (IO) is a framework for learning the unknown objective function of an expert decision-maker from a past dataset. In this paper, we extend the hypothesis class of IO objective functions to a reproducing kernel Hilbert space (RKHS)
Externí odkaz:
http://arxiv.org/abs/2410.23952
Retrieval-augmented generation (RAG) addresses key limitations of large language models (LLMs), such as hallucinations and outdated knowledge, by incorporating external databases. These databases typically consult multiple sources to encompass up-to-
Externí odkaz:
http://arxiv.org/abs/2410.22954
Federated fine-tuning for Large Language Models (LLMs) has recently gained attention due to the heavy communication overhead of transmitting large model updates. Low Rank Adaptation (LoRA) has been proposed as a solution, yet its application in feder
Externí odkaz:
http://arxiv.org/abs/2410.22815
Deep learning-based expert models have reached superhuman performance in decision-making domains such as chess and Go. However, it is under-explored to explain or comment on given decisions although it is important for human education and model expla
Externí odkaz:
http://arxiv.org/abs/2410.20811
We study partial regularity for degenerate elliptic systems of double-phase type, where the growth function is given by $H(x,t)=t^p+a(x)t^q$ with $1
Externí odkaz:
http://arxiv.org/abs/2410.14350
Autor:
Jang, Oh-Tae, Song, Hae-Kang, Kim, Min-Jun, Lee, Kyung-Hwan, Lee, Geon, Kim, Sung-Ho, Shin, Hee-Sub, Ok, Jae-Woo, Back, Min-Young, Yoon, Jae-Hyuk, Kim, Kyung-Tae
Recently, computer-aided design models and electromagnetic simulations have been used to augment synthetic aperture radar (SAR) data for deep learning. However, an automatic target recognition (ATR) model struggles with domain shift when using synthe
Externí odkaz:
http://arxiv.org/abs/2409.16845
Anomaly detection is a dynamic field, in which the evaluation of models plays a critical role in understanding their effectiveness. The selection and interpretation of the evaluation metrics are pivotal, particularly in scenarios with varying amounts
Externí odkaz:
http://arxiv.org/abs/2409.15986
Autor:
Kim, Minjun, Jang, Ohtae, Song, Haekang, Shin, Heesub, Ok, Jaewoo, Back, Minyoung, Youn, Jaehyuk, Kim, Sungho
Synthetic aperture radar technology is crucial for high-resolution imaging under various conditions; however, the acquisition of real-world synthetic aperture radar data for deep learning-based automatic target recognition remains challenging due to
Externí odkaz:
http://arxiv.org/abs/2409.14060
We introduce fractional weighted Sobolev spaces with degenerate weights. For these spaces we provide embeddings and Poincar\'e inequalities. When the order of fractional differentiability goes to $0$ or $1$, we recover the weighted Lebesgue and Sobol
Externí odkaz:
http://arxiv.org/abs/2409.11829