Zobrazeno 1 - 10
of 10 008
pro vyhledávání: '"Lee, In suk"'
This paper introduces an explainable DNN-based beamformer with a postfilter (ExNet-BF+PF) for multichannel signal processing. Our approach combines the U-Net network with a beamformer structure to address this problem. The method involves a two-stage
Externí odkaz:
http://arxiv.org/abs/2411.10854
Autor:
Jeong, Suyeong, Jung, Dae-Han, Han, Hee-Sung, Kim, Ganghwi, Kang, Myeonghwan, Im, Mi-Young, Park, Younggun, Lee, Ki-Suk
Skyrmions, topologically stable magnetic solitons characterized by whirling magnetization in nanoscale magnetic elements, show promise information carriers in spintronics and spin-based quantum computing due to their unique properties: small size, st
Externí odkaz:
http://arxiv.org/abs/2411.04367
Autor:
Lee, Young-Suk, Gunasekara, Chulaka, Contractor, Danish, Astudillo, Ramón Fernandez, Florian, Radu
We introduce a technique for multi-document grounded multi-turn synthetic dialog generation that incorporates three main ideas. First, we control the overall dialog flow using taxonomy-driven user queries that are generated with Chain-of-Thought (CoT
Externí odkaz:
http://arxiv.org/abs/2409.11500
Autor:
Xu, Zhongweiyang, Aroudi, Ali, Tan, Ke, Pandey, Ashutosh, Lee, Jung-Suk, Xu, Buye, Nesta, Francesco
This paper presents a novel multi-channel speech enhancement approach, FoVNet, that enables highly efficient speech enhancement within a configurable field of view (FoV) of a smart-glasses user without needing specific target-talker(s) directions. It
Externí odkaz:
http://arxiv.org/abs/2408.06468
The strawberry industry yields significant economic benefits for Florida, yet the process of monitoring strawberry growth and yield is labor-intensive and costly. The development of machine learning-based detection and tracking methodologies has been
Externí odkaz:
http://arxiv.org/abs/2407.12614
Autor:
Eiras, Francisco, Petrov, Aleksandar, Vidgen, Bertie, de Witt, Christian Schroeder, Pizzati, Fabio, Elkins, Katherine, Mukhopadhyay, Supratik, Bibi, Adel, Csaba, Botos, Steibel, Fabro, Barez, Fazl, Smith, Genevieve, Guadagni, Gianluca, Chun, Jon, Cabot, Jordi, Imperial, Joseph Marvin, Nolazco-Flores, Juan A., Landay, Lori, Jackson, Matthew, Röttger, Paul, Torr, Philip H. S., Darrell, Trevor, Lee, Yong Suk, Foerster, Jakob
In the next few years, applications of Generative AI are expected to revolutionize a number of different areas, ranging from science & medicine to education. The potential for these seismic changes has triggered a lively debate about potential risks
Externí odkaz:
http://arxiv.org/abs/2404.17047
Autor:
Ramji, Keshav, Lee, Young-Suk, Astudillo, Ramón Fernandez, Sultan, Md Arafat, Naseem, Tahira, Munawar, Asim, Florian, Radu, Roukos, Salim
It is often desirable for Large Language Models (LLMs) to capture multiple objectives when providing a response. In document-grounded response generation, for example, agent responses are expected to be relevant to a user's query while also being gro
Externí odkaz:
http://arxiv.org/abs/2403.00827
This paper presents a groundbreaking self-improving interference management framework tailored for wireless communications, integrating deep learning with uncertainty quantification to enhance overall system performance. Our approach addresses the co
Externí odkaz:
http://arxiv.org/abs/2401.13206
The surge in real-time data collection across various industries has underscored the need for advanced anomaly detection in both univariate and multivariate time series data. This paper introduces TransNAS-TSAD, a framework that synergizes the transf
Externí odkaz:
http://arxiv.org/abs/2311.18061
Lipid droplets (LDs), once considered mere storage depots for lipids, have gained recognition for their intricate roles in cellular processes, including metabolism, membrane trafficking, and disease states like obesity and cancer. This review explore
Externí odkaz:
http://arxiv.org/abs/2310.16465