Zobrazeno 1 - 10
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pro vyhledávání: '"Glass A"'
Autor:
Goldstein Emily, Martinez-García Laura, Obermeier Martin, Glass Allison, Krügel Maria, Maree Leana, Gunson Rory, Onelia Francesco, Pacenti Monia, Nelson Kevin S., Joseph Ajith M., Palm Michael J., Lucic Danijela, Marlowe Natalia, Dhein Jens, Reinhardt Birgit, Pfeifer Karin, Galan Juan-C., Azzato Francesca
Publikováno v:
Journal of Laboratory Medicine, Vol 45, Iss 4-5, Pp 213-223 (2021)
Accurate and rapid diagnosis of sexually transmitted infections (STIs) is essential for timely administration of appropriate treatment and reducing the spread of the disease. We examined the performance of the new Alinity m STI assay, a qualitative r
Externí odkaz:
https://doaj.org/article/7b9098939b374a679640390b95736a68
Autor:
Bhati, Saurabhchand, Gong, Yuan, Karlinsky, Leonid, Kuehne, Hilde, Feris, Rogerio, Glass, James
State-space models (SSMs) have emerged as an alternative to Transformers for audio modeling due to their high computational efficiency with long inputs. While recent efforts on Audio SSMs have reported encouraging results, two main limitations remain
Externí odkaz:
http://arxiv.org/abs/2407.04082
Autor:
Chen, Jintai, Hu, Yaojun, Wang, Yue, Lu, Yingzhou, Cao, Xu, Lin, Miao, Xu, Hongxia, Wu, Jian, Xiao, Cao, Sun, Jimeng, Glass, Lucas, Huang, Kexin, Zitnik, Marinka, Fu, Tianfan
Clinical trials are pivotal for developing new medical treatments, yet they typically pose some risks such as patient mortality, adverse events, and enrollment failure that waste immense efforts spanning over a decade. Applying artificial intelligenc
Externí odkaz:
http://arxiv.org/abs/2407.00631
Autor:
Wang, Liming, Gong, Yuan, Dawalatabad, Nauman, Vilela, Marco, Placek, Katerina, Tracey, Brian, Gong, Yishu, Premasiri, Alan, Vieira, Fernando, Glass, James
Automatic prediction of amyotrophic lateral sclerosis (ALS) disease progression provides a more efficient and objective alternative than manual approaches. We propose ALS longitudinal speech transformer (ALST), a neural network-based automatic predic
Externí odkaz:
http://arxiv.org/abs/2406.18625
Autor:
Glass, Cheyne, Vidaurre, Elizabeth
Topological Data Analysis has grown in popularity in recent years as a way to apply tools from algebraic topology to large data sets. One of the main tools in topological data analysis is persistent homology. This paper uses undergraduate linear alge
Externí odkaz:
http://arxiv.org/abs/2406.17045
Autor:
Hsieh, Cheng-Yu, Chuang, Yung-Sung, Li, Chun-Liang, Wang, Zifeng, Le, Long T., Kumar, Abhishek, Glass, James, Ratner, Alexander, Lee, Chen-Yu, Krishna, Ranjay, Pfister, Tomas
Large language models (LLMs), even when specifically trained to process long input contexts, struggle to capture relevant information located in the middle of their input. This phenomenon has been known as the lost-in-the-middle problem. In this work
Externí odkaz:
http://arxiv.org/abs/2406.16008
Autor:
Kang, Junmo, Karlinsky, Leonid, Luo, Hongyin, Wang, Zhen, Hansen, Jacob, Glass, James, Cox, David, Panda, Rameswar, Feris, Rogerio, Ritter, Alan
We present Self-MoE, an approach that transforms a monolithic LLM into a compositional, modular system of self-specialized experts, named MiXSE (MiXture of Self-specialized Experts). Our approach leverages self-specialization, which constructs expert
Externí odkaz:
http://arxiv.org/abs/2406.12034
Query rewriting is a crucial technique for passage retrieval in open-domain conversational question answering (CQA). It decontexualizes conversational queries into self-contained questions suitable for off-the-shelf retrievers. Existing methods attem
Externí odkaz:
http://arxiv.org/abs/2406.10991
Autor:
Rouditchenko, Andrew, Gong, Yuan, Thomas, Samuel, Karlinsky, Leonid, Kuehne, Hilde, Feris, Rogerio, Glass, James
Audio-Visual Speech Recognition (AVSR) uses lip-based video to improve performance in noise. Since videos are harder to obtain than audio, the video training data of AVSR models is usually limited to a few thousand hours. In contrast, speech models s
Externí odkaz:
http://arxiv.org/abs/2406.10082
Large language models (LLMs) have shown impressive capabilities across diverse settings, but still struggle as the length and complexity of the context increases. To address this challenge, we propose Thinking Recursively and Dynamically (ThReaD). TH
Externí odkaz:
http://arxiv.org/abs/2405.17402