Zobrazeno 1 - 10
of 284 954
pro vyhledávání: '"To Chiu"'
Autor:
Gupta, V., Araujo, G. R., Babicz, M., Baudis, L., Chiu, P. -J., Choudhary, S., Goldbrunner, M., Hamer, A., Kuźniak, M., Kuźwa, M., Leonhardt, A., Montagna, E., Nieradka, G., Parkinson, H. B., Pietropaolo, F., Pollmann, T. R., Resnati, F., Schönert, S., Szelc, A. M., Thieme, K., Walczak, M.
Liquid argon detectors rely on wavelength shifters for efficient detection of scintillation light. The current standard is tetraphenyl butadiene (TPB), but it is challenging to instrument on a large scale. Poly(ethylene 2,6-naphthalate) (PEN), a poly
Externí odkaz:
http://arxiv.org/abs/2411.17934
Text-to-image (T2I) models have shown remarkable progress, but their potential to generate harmful content remains a critical concern in the ML community. While various safety mechanisms have been developed, the field lacks systematic tools for evalu
Externí odkaz:
http://arxiv.org/abs/2411.16769
Autor:
Song, Yuhang, Gianni, Mario, Yang, Chenguang, Lin, Kunyang, Chiu, Te-Chuan, Nguyen, Anh, Lee, Chun-Yi
This paper addresses the challenge of fine-grained alignment in Vision-and-Language Navigation (VLN) tasks, where robots navigate realistic 3D environments based on natural language instructions. Current approaches use contrastive learning to align l
Externí odkaz:
http://arxiv.org/abs/2411.14811
Autor:
Zimmermann, Yoel, Bazgir, Adib, Afzal, Zartashia, Agbere, Fariha, Ai, Qianxiang, Alampara, Nawaf, Al-Feghali, Alexander, Ansari, Mehrad, Antypov, Dmytro, Aswad, Amro, Bai, Jiaru, Baibakova, Viktoriia, Biswajeet, Devi Dutta, Bitzek, Erik, Bocarsly, Joshua D., Borisova, Anna, Bran, Andres M, Brinson, L. Catherine, Calderon, Marcel Moran, Canalicchio, Alessandro, Chen, Victor, Chiang, Yuan, Circi, Defne, Charmes, Benjamin, Chaudhary, Vikrant, Chen, Zizhang, Chiu, Min-Hsueh, Clymo, Judith, Dabhadkar, Kedar, Daelman, Nathan, Datar, Archit, Evans, Matthew L., Fard, Maryam Ghazizade, Fisicaro, Giuseppe, Gangan, Abhijeet Sadashiv, George, Janine, Gonzalez, Jose D. Cojal, Götte, Michael, Gupta, Ankur K., Harb, Hassan, Hong, Pengyu, Ibrahim, Abdelrahman, Ilyas, Ahmed, Imran, Alishba, Ishimwe, Kevin, Issa, Ramsey, Jablonka, Kevin Maik, Jones, Colin, Josephson, Tyler R., Juhasz, Greg, Kapoor, Sarthak, Kang, Rongda, Khalighinejad, Ghazal, Khan, Sartaaj, Klawohn, Sascha, Kuman, Suneel, Ladines, Alvin Noe, Leang, Sarom, Lederbauer, Magdalena, Liao, Sheng-Lun Mark, Liu, Hao, Liu, Xuefeng, Lo, Stanley, Madireddy, Sandeep, Maharana, Piyush Ranjan, Maheshwari, Shagun, Mahjoubi, Soroush, Márquez, José A., Mills, Rob, Mohanty, Trupti, Mohr, Bernadette, Moosavi, Seyed Mohamad, Moßhammer, Alexander, Naghdi, Amirhossein D., Naik, Aakash, Narykov, Oleksandr, Näsström, Hampus, Nguyen, Xuan Vu, Ni, Xinyi, O'Connor, Dana, Olayiwola, Teslim, Ottomano, Federico, Ozhan, Aleyna Beste, Pagel, Sebastian, Parida, Chiku, Park, Jaehee, Patel, Vraj, Patyukova, Elena, Petersen, Martin Hoffmann, Pinto, Luis, Pizarro, José M., Plessers, Dieter, Pradhan, Tapashree, Pratiush, Utkarsh, Puli, Charishma, Qin, Andrew, Rajabi, Mahyar, Ricci, Francesco, Risch, Elliot, Ríos-García, Martiño, Roy, Aritra, Rug, Tehseen, Sayeed, Hasan M, Scheidgen, Markus, Schilling-Wilhelmi, Mara, Schloz, Marcel, Schöppach, Fabian, Schumann, Julia, Schwaller, Philippe, Schwarting, Marcus, Sharlin, Samiha, Shen, Kevin, Shi, Jiale, Si, Pradip, D'Souza, Jennifer, Sparks, Taylor, Sudhakar, Suraj, Talirz, Leopold, Tang, Dandan, Taran, Olga, Terboven, Carla, Tropin, Mark, Tsymbal, Anastasiia, Ueltzen, Katharina, Unzueta, Pablo Andres, Vasan, Archit, Vinchurkar, Tirtha, Vo, Trung, Vogel, Gabriel, Völker, Christoph, Weinreich, Jan, Yang, Faradawn, Zaki, Mohd, Zhang, Chi, Zhang, Sylvester, Zhang, Weijie, Zhu, Ruijie, Zhu, Shang, Janssen, Jan, Foster, Ian, Blaiszik, Ben
Here, we present the outcomes from the second Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry, which engaged participants across global hybrid locations, resulting in 34 team submissions. The submissions spann
Externí odkaz:
http://arxiv.org/abs/2411.15221
Language mismatch is among the most common and challenging domain mismatches in deploying speaker verification (SV) systems. Adversarial reprogramming has shown promising results in cross-language adaptation for SV. The reprogramming is implemented b
Externí odkaz:
http://arxiv.org/abs/2411.11353
Autor:
Chiu, Ting-Wai
The spatial $z$-correlators of meson operators in $N_f=2+1+1+1$ lattice QCD with optimal domain-wall quarks are studied for eight temperatures in the range of 325-3250 MeV. The meson operators include a complete set of Dirac bilinears (scalar, pseudo
Externí odkaz:
http://arxiv.org/abs/2411.16705
Autor:
Nasri, Mahsa, Narayan, Uttkarsh, Sonbudak, Mustafa Feyyaz, Simonson, Aubrey, Chiu, Maria, Donati, Jason, Sivak, Mark, Kosa, Mehmet, Harteveld, Casper
Apprenticeship and training programs in advanced manufacturing frequently encounter safety and accessibility concerns due to using heavy machinery. Virtual Reality (VR) training addresses such constraints while maintaining the spatial and procedural
Externí odkaz:
http://arxiv.org/abs/2411.08859
Autor:
Yang, Chih-Kai, Fu, Yu-Kuan, Li, Chen-An, Lin, Yi-Cheng, Lin, Yu-Xiang, Chen, Wei-Chih, Chung, Ho Lam, Kuan, Chun-Yi, Huang, Wei-Ping, Lu, Ke-Han, Lin, Tzu-Quan, Wang, Hsiu-Hsuan, Hu, En-Pei, Hsu, Chan-Jan, Tseng, Liang-Hsuan, Chiu, I-Hsiang, Sanga, Ulin, Chen, Xuanjun, Hsu, Po-chun, Yang, Shu-wen, Lee, Hung-yi
This technical report presents our initial attempt to build a spoken large language model (LLM) for Taiwanese Mandarin, specifically tailored to enable real-time, speech-to-speech interaction in multi-turn conversations. Our end-to-end model incorpor
Externí odkaz:
http://arxiv.org/abs/2411.07111
NeKo: Toward Post Recognition Generative Correction Large Language Models with Task-Oriented Experts
Autor:
Lin, Yen-Ting, Yang, Chao-Han Huck, Chen, Zhehuai, Zelasko, Piotr, Yang, Xuesong, Chen, Zih-Ching, Puvvada, Krishna C, Fu, Szu-Wei, Hu, Ke, Chiu, Jun Wei, Balam, Jagadeesh, Ginsburg, Boris, Wang, Yu-Chiang Frank
Construction of a general-purpose post-recognition error corrector poses a crucial question: how can we most effectively train a model on a large mixture of domain datasets? The answer would lie in learning dataset-specific features and digesting the
Externí odkaz:
http://arxiv.org/abs/2411.05945
Autor:
Chiu, Henry
We present a non-probabilistic, path-by-path framework for studying path-dependent (i.e., where weight is a functional of time and historical time-series), long-only portfolio allocation in continuous-time based on [Chiu & Cont '23], where the fundam
Externí odkaz:
http://arxiv.org/abs/2411.05470