Zobrazeno 1 - 10
of 17 408
pro vyhledávání: '"Imani, A."'
Autor:
MicroBooNE collaboration, Abratenko, P., Alterkait, O., Aldana, D. Andrade, Arellano, L., Asaadi, J., Ashkenazi, A., Balasubramanian, S., Baller, B., Barnard, A., Barr, G., Barrow, D., Barrow, J., Basque, V., Bateman, J., Rodrigues, O. Benevides, Berkman, S., Bhanderi, A., Bhat, A., Bhattacharya, M., Bishai, M., Blake, A., Bogart, B., Bolton, T., Brunetti, M. B., Camilleri, L., Cao, Y., Caratelli, D., Cavanna, F., Cerati, G., Chappell, A., Chen, Y., Conrad, J. M., Convery, M., Cooper-Troendle, L., Crespo-Anadon, J. I., Cross, R., Del Tutto, M., Dennis, S. R., Detje, P., Diurba, R., Djurcic, Z., Duffy, K., Dytman, S., Eberly, B., Englezos, P., Ereditato, A., Evans, J. J., Fang, C., Fleming, B. T., Foreman, W., Franco, D., Furmanski, A. P., Gao, F., Garcia-Gamez, D., Gardiner, S., Ge, G., Gollapinni, S., Gramellini, E., Green, P., Greenlee, H., Gu, L., Gu, W., Guenette, R., Guzowski, P., Hagaman, L., Handley, M. D., Hen, O., Hilgenberg, C., Horton-Smith, G. A., Imani, Z., Irwin, B., Ismail, M. S., James, C., Ji, X., Jo, J. H., Johnson, R. A., Jwa, Y. J., Kalra, D., Karagiorgi, G., Ketchum, W., Kirby, M., Kobilarcik, T., Lane, N., Li, J. -Y., Li, Y., Lin, K., Littlejohn, B. R., Liu, L., Louis, W. C., Luo, X., Mahmud, T., Mariani, C., Marsden, D., Marshall, J., Martinez, N., Caicedo, D. A. Martinez, Martynenko, S., Mastbaum, A., Mawby, I., McConkey, N., Meddage, V., Mellet, L., Mendez, J., Micallef, J., Miller, K., Mistry, K., Mohayai, T., Mogan, A., Mooney, M., Moor, A. F., Moore, C. D., Lepin, L. Mora, Moudgalya, M. M., Babu, S. Mulleria, Naples, D., Navrer-Agasson, A., Nayak, N., Nebot-Guinot, M., Nguyen, C., Nowak, J., Oza, N., Palamara, O., Pallat, N., Paolone, V., Papadopoulou, A., Papavassiliou, V., Parkinson, H., Pate, S. F., Patel, N., Pavlovic, Z., Piasetzky, E., Pletcher, K., Pophale, I., Qian, X., Raaf, J. L., Radeka, V., Rafique, A., Reggiani-Guzzo, M., Ren, L., Rochester, L., Rondon, J. Rodriguez, Rosenberg, M., Ross-Lonergan, M., Safa, I., Schmitz, D. W., Schukraft, A., Seligman, W., Shaevitz, M. H., Sharankova, R., Shi, J., Snider, E. L., Soderberg, M., Soldner-Rembold, S., Spitz, J., Stancari, M., John, J. St., Strauss, T., Szelc, A. M., Taniuchi, N., Terao, K., Thorpe, C., Torbunov, D., Totani, D., Toups, M., Trettin, A., Tsai, Y. -T., Tyler, J., Uchida, M. A., Usher, T., Viren, B., Wang, J., Weber, M., Wei, H., White, A. J., Wolbers, S., Wongjirad, T., Wospakrik, M., Wresilo, K., Wu, W., Yandel, E., Yang, T., Yates, L. E., Yu, H. W., Zeller, G. P., Zennamo, J., Zhang, C.
Large neutrino liquid argon time projection chamber (LArTPC) experiments can broaden their physics reach by reconstructing and interpreting MeV-scale energy depositions, or blips, present in their data. We demonstrate new calorimetric and particle di
Externí odkaz:
http://arxiv.org/abs/2410.18419
Autor:
Rizvi, Naba, Strickland, Harper, Gitelman, Daniel, Cooper, Tristan, Morales-Flores, Alexis, Golden, Michael, Kallepalli, Aekta, Alurkar, Akshat, Owens, Haaset, Ahmedi, Saleha, Khirwadkar, Isha, Munyaka, Imani, Ousidhoum, Nedjma
As our understanding of autism and ableism continues to increase, so does our understanding of ableist language towards autistic people. Such language poses a significant challenge in NLP research due to its subtle and context-dependent nature. Yet,
Externí odkaz:
http://arxiv.org/abs/2410.16520
Autor:
Yin, Xunzhao, Barkam, Hamza Errahmouni, Müller, Franz, Jiang, Yuxiao, Imani, Mohsen, Abdulazhanov, Sukhrob, Vardar, Alptekin, Laleni, Nellie, Zhao, Zijian, Duan, Jiahui, Shi, Zhiguo, Joshi, Siddharth, Niemier, Michael, Hu, Xiaobo Sharon, Zhuo, Cheng, Kämpfe, Thomas, Ni, Kai
Neuro-symbolic artificial intelligence (AI) excels at learning from noisy and generalized patterns, conducting logical inferences, and providing interpretable reasoning. Comprising a 'neuro' component for feature extraction and a 'symbolic' component
Externí odkaz:
http://arxiv.org/abs/2410.15296
Autor:
Liu, Yihong, Wang, Mingyang, Kargaran, Amir Hossein, Imani, Ayyoob, Xhelili, Orgest, Ye, Haotian, Ma, Chunlan, Yvon, François, Schütze, Hinrich
Recent studies have shown that post-aligning multilingual pretrained language models (mPLMs) using alignment objectives on both original and transliterated data can improve crosslingual alignment. This improvement further leads to better crosslingual
Externí odkaz:
http://arxiv.org/abs/2409.17326
Autor:
Köksal, Abdullatif, Thaler, Marion, Imani, Ayyoob, Üstün, Ahmet, Korhonen, Anna, Schütze, Hinrich
Instruction tuning enhances large language models (LLMs) by aligning them with human preferences across diverse tasks. Traditional approaches to create instruction tuning datasets face serious challenges for low-resource languages due to their depend
Externí odkaz:
http://arxiv.org/abs/2409.12958
Autor:
Bacheva, Vesna, Madison, Imani, Baldwin, Mathew, Beilstein, Mark, Call, Douglas F., Deaver, Jessica A., Efimenko, Kirill, Genzer, Jan, Grieger, Khara, Gu, April Z., Ilman, Mehmet Mert, Liu, Jen, Li, Sijin, Mayer, Brooke K., Mishra, Anand Kumar, Nino, Juan Claudio, Rubambiza, Gloire, Sengers, Phoebe, Shepherd, Robert, Woodson, Jesse, Weatherspoon, Hakim, Frank, Margaret, Jones, Jacob, Sozzani, Rosangela, Stroock, Abraham
Feeding the growing human population sustainably amidst climate change is one of the most important challenges in the 21st century. Current practices often lead to the overuse of agronomic inputs, such as synthetic fertilizers and water, resulting in
Externí odkaz:
http://arxiv.org/abs/2409.12337
Code comments are essential for clarifying code functionality, improving readability, and facilitating collaboration among developers. Despite their importance, comments often become outdated, leading to inconsistencies with the corresponding code. T
Externí odkaz:
http://arxiv.org/abs/2409.10781
Link prediction is a crucial task in network analysis, but it has been shown to be prone to biased predictions, particularly when links are unfairly predicted between nodes from different sensitive groups. In this paper, we study the fair link predic
Externí odkaz:
http://arxiv.org/abs/2409.08658
Autor:
Chen, Hanning, Ni, Yang, Huang, Wenjun, Liu, Yezi, Jeong, SungHeon, Wen, Fei, Bastian, Nathaniel, Latapie, Hugo, Imani, Mohsen
Vision Transformers (ViTs) have emerged as the backbone of many segmentation models, consistently achieving state-of-the-art (SOTA) performance. However, their success comes at a significant computational cost. Image token pruning is one of the most
Externí odkaz:
http://arxiv.org/abs/2409.08464
Autor:
Huang, Wenjun, Ni, Yang, Rezvani, Arghavan, Jeong, SungHeon, Chen, Hanning, Liu, Yezi, Wen, Fei, Imani, Mohsen
Human pose estimation (HPE) is crucial for various applications. However, deploying HPE algorithms in surveillance contexts raises significant privacy concerns due to the potential leakage of sensitive personal information (SPI) such as facial featur
Externí odkaz:
http://arxiv.org/abs/2409.02715