Zobrazeno 1 - 10
of 33 270
pro vyhledávání: '"Johnson Mark"'
The Middle Kingdom under the Big Sky. A History of the Chinese Experience in Montana Johnson Mark T.
Autor:
Gow, William
Publikováno v:
The Pacific Northwest Quarterly, 2023 Jan 01. 114(1), 41-42.
Externí odkaz:
https://www.jstor.org/stable/27274087
Autor:
Fitzmorris, Terrence W.
Publikováno v:
Louisiana History: The Journal of the Louisiana Historical Association, 2022 Jan 01. 63(1), 119-122.
Externí odkaz:
https://www.jstor.org/stable/27190839
The COVID-19 pandemic has underscored the need for low-cost, scalable approaches to measuring contactless vital signs, either during initial triage at a healthcare facility or virtual telemedicine visits. Remote photoplethysmography (rPPG) can accura
Externí odkaz:
http://arxiv.org/abs/2410.15851
Autor:
Chen, Meng, Arthur, Philip, Feng, Qianyu, Hoang, Cong Duy Vu, Hong, Yu-Heng, Moghaddam, Mahdi Kazemi, Nezami, Omid, Nguyen, Thien, Tangari, Gioacchino, Vu, Duy, Vu, Thanh, Johnson, Mark, Kenthapadi, Krishnaram, Dharmasiri, Don, Duong, Long, Li, Yuan-Fang
Large language models (LLMs) have shown impressive performance in \emph{code} understanding and generation, making coding tasks a key focus for researchers due to their practical applications and value as a testbed for LLM evaluation. Data synthesis
Externí odkaz:
http://arxiv.org/abs/2411.00005
Intracranial aneurysms (IAs) that rupture result in significant morbidity and mortality. While traditional risk models such as the PHASES score are useful in clinical decision making, machine learning (ML) models offer the potential to provide more a
Externí odkaz:
http://arxiv.org/abs/2410.00121
Publikováno v:
Proceedings of Interspeech 2024
This paper enhances dysarthric and dysphonic speech recognition by fine-tuning pretrained automatic speech recognition (ASR) models on the 2023-10-05 data package of the Speech Accessibility Project (SAP), which contains the speech of 253 people with
Externí odkaz:
http://arxiv.org/abs/2409.19818
Autor:
Wu, Junkai, Fan, Xulin, Lu, Bo-Ru, Jiang, Xilin, Mesgarani, Nima, Hasegawa-Johnson, Mark, Ostendorf, Mari
In recent years, we have observed a rapid advancement in speech language models (SpeechLLMs), catching up with humans' listening and reasoning abilities. SpeechLLMs have demonstrated impressive spoken dialog question-answering (SQA) performance in be
Externí odkaz:
http://arxiv.org/abs/2409.04927
This work reimplements a recent semantic bootstrapping child-language acquisition model, which was originally designed for English, and trains it to learn a new language: Hebrew. The model learns from pairs of utterances and logical forms as meaning
Externí odkaz:
http://arxiv.org/abs/2408.12254