Zobrazeno 1 - 10
of 49
pro vyhledávání: '"Wang, I."'
Autor:
Hsu, Chia-Yu, Wang, I-Hsiang
Reliability of sequential hypothesis testing can be greatly improved when decision maker is given the freedom to adaptively take an action that determines the distribution of the current collected sample. Such advantage of sampling adaptivity has bee
Externí odkaz:
http://arxiv.org/abs/2405.06554
Autor:
Li, Ching-Fang, Wang, I-Hsiang
In hypothesis testing problems, taking samples sequentially and stopping opportunistically to make the inference greatly enhances the reliability. The design of the stopping and inference policy, however, critically relies on the knowledge of the und
Externí odkaz:
http://arxiv.org/abs/2401.16213
Autor:
Johnson, Erik C., Robinson, Brian S., Vallabha, Gautam K., Joyce, Justin, Matelsky, Jordan K., Norman-Tenazas, Raphael, Western, Isaac, Villafañe-Delgado, Marisel, Cervantes, Martha, Robinette, Michael S., Reddy, Arun V., Kitchell, Lindsey, Rivlin, Patricia K., Reilly, Elizabeth P., Drenkow, Nathan, Roos, Matthew J., Wang, I-Jeng, Wester, Brock A., Gray-Roncal, William R., Hoffmann, Joan A.
Despite the progress in deep learning networks, efficient learning at the edge (enabling adaptable, low-complexity machine learning solutions) remains a critical need for defense and commercial applications. We envision a pipeline to utilize large ne
Externí odkaz:
http://arxiv.org/abs/2305.17300
Autor:
Finocchio, Giovanni, Incorvia, Jean Anne C., Friedman, Joseph S., Yang, Qu, Giordano, Anna, Grollier, Julie, Yang, Hyunsoo, Ciubotaru, Florin, Chumak, Andrii, Naeemi, Azad J., Cotofana, Sorin D., Tomasello, Riccardo, Panagopoulos, Christos, Carpentieri, Mario, Lin, Peng, Pan, Gang, Yang, J. Joshua, Todri-Sanial, Aida, Boschetto, Gabriele, Makasheva, Kremena, Sangwan, Vinod K., Trivedi, Amit Ranjan, Hersam, Mark C., Camsari, Kerem Y., McMahon, Peter L., Datta, Supriyo, Koiller, Belita, Aguilar, Gabriel H., Temporão, Guilherme P., Rodrigues, Davi R., Sunada, Satoshi, Everschor-Sitte, Karin, Tatsumura, Kosuke, Goto, Hayato, Puliafito, Vito, Åkerman, Johan, Takesue, Hiroki, Di Ventra, Massimiliano, Pershin, Yuriy V., Mukhopadhyay, Saibal, Roy, Kaushik, Wang, I-Ting, Kang, Wang, Zhu, Yao, Kaushik, Brajesh Kumar, Hasler, Jennifer, Ganguly, Samiran, Ghosh, Avik W., Levy, William, Roychowdhury, Vwani, Bandyopadhyay, Supriyo
Publikováno v:
Nano Futures (2024)
In the "Beyond Moore's Law" era, with increasing edge intelligence, domain-specific computing embracing unconventional approaches will become increasingly prevalent. At the same time, adopting a variety of nanotechnologies will offer benefits in ener
Externí odkaz:
http://arxiv.org/abs/2301.06727
Standard deep reinforcement learning (DRL) aims to maximize expected reward, considering collected experiences equally in formulating a policy. This differs from human decision-making, where gains and losses are valued differently and outlying outcom
Externí odkaz:
http://arxiv.org/abs/2208.09106
Publikováno v:
IEEE Journal on Selected Areas in Information Theory 2021
We study the content delivery problem between a transmitter and two receivers through erasure links, when each receiver has access to some random side-information about the files requested by the other user. The random side-information is cached at t
Externí odkaz:
http://arxiv.org/abs/2105.00323
New results on vacuum fluctuations: Accelerated detector versus inertial detector in a quantum field
Autor:
Wang, I-Chin
Publikováno v:
Phys. Rev. D 104, 045014 (2021)
We investigate the interaction between a moving detector and a quantum field, especially about how the trajectory of the detector would affect the vacuum fluctuations when the detector is moves in a quantum field (the Unruh effect). We focus on two m
Externí odkaz:
http://arxiv.org/abs/2104.04142
Accurate state and uncertainty estimation is imperative for mobile robots and self driving vehicles to achieve safe navigation in pedestrian rich environments. A critical component of state and uncertainty estimation for robot navigation is to perfor
Externí odkaz:
http://arxiv.org/abs/2104.02799
Autor:
Li, Yun-Han, Wang, I-Hsiang
In this paper, combinatorial quantitative group testing (QGT) with noisy measurements is studied. The goal of QGT is to detect defective items from a data set of size $n$ with counting measurements, each of which counts the number of defects in a sel
Externí odkaz:
http://arxiv.org/abs/2101.12653
Autor:
Katyal, Kapil, Gao, Yuxiang, Markowitz, Jared, Pohland, Sara, Rivera, Corban, Wang, I-Jeng, Huang, Chien-Ming
Human-aware robot navigation promises a range of applications in which mobile robots bring versatile assistance to people in common human environments. While prior research has mostly focused on modeling pedestrians as independent, intentional indivi
Externí odkaz:
http://arxiv.org/abs/2012.12291