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pro vyhledávání: '"Huang, William"'
Existing pose estimation models perform poorly on wheelchair users due to a lack of representation in training data. We present a data synthesis pipeline to address this disparity in data collection and subsequently improve pose estimation performanc
Externí odkaz:
http://arxiv.org/abs/2404.17063
More capable language models increasingly saturate existing task benchmarks, in some cases outperforming humans. This has left little headroom with which to measure further progress. Adversarial dataset creation has been proposed as a strategy to con
Externí odkaz:
http://arxiv.org/abs/2111.08181
Autor:
Kamal, Kanika, Xia, Eric, Li, Sara J., Alavi, Afsaneh, Cogen, Anna L., Firooz, Alireza, Marzano, Angelo V., Kaffenberger, Benjamin H., Sibbald, Cathryn, Fernandez, Anthony P., Callen, Jeffrey P., Dissemond, Joachim, Gontijo, João Renato V., Shams, Kave, Gerbens, Louise A., French, Lars E., Gould, Lisa J., Bissonnette, Robert, Shaigany, Sheila, Tolkachjov, Stanislav, Yamamoto, Toshiyuki, Wei-Ting Huang, William, Ortega-Loayza, Alex G., Mostaghimi, Arash
Publikováno v:
In Journal of Investigative Dermatology June 2024 144(6):1295-1300
Despite agreement on the importance of detecting out-of-distribution (OOD) examples, there is little consensus on the formal definition of OOD examples and how to best detect them. We categorize these examples by whether they exhibit a background shi
Externí odkaz:
http://arxiv.org/abs/2109.06827
Autor:
Vania, Clara, Htut, Phu Mon, Huang, William, Mungra, Dhara, Pang, Richard Yuanzhe, Phang, Jason, Liu, Haokun, Cho, Kyunghyun, Bowman, Samuel R.
Recent years have seen numerous NLP datasets introduced to evaluate the performance of fine-tuned models on natural language understanding tasks. Recent results from large pretrained models, though, show that many of these datasets are largely satura
Externí odkaz:
http://arxiv.org/abs/2106.00840
Autor:
Hrin, Matthew L., Huang, William W.
Publikováno v:
In Dermatologic Clinics April 2024 42(2):193-207
Autor:
Huang, I-Shen, Fantus, Richard J., Halpern, Joshua A., Wren, James, Bennett, Nelson E., Pham, Minh Nguyen, Stanisic, Alexander, Huang, William J., Brannigan, Robert E.
Publikováno v:
In F&S Reports March 2024 5(1):95-101
Autor:
Parrish, Alicia, Huang, William, Agha, Omar, Lee, Soo-Hwan, Nangia, Nikita, Warstadt, Alex, Aggarwal, Karmanya, Allaway, Emily, Linzen, Tal, Bowman, Samuel R.
Many crowdsourced NLP datasets contain systematic gaps and biases that are identified only after data collection is complete. Identifying these issues from early data samples during crowdsourcing should make mitigation more efficient, especially when
Externí odkaz:
http://arxiv.org/abs/2104.07179
Autor:
Pan, Li-Ling Hope *, *, Chen, Shih-Pin *, Ling, Yu-Hsiang, Wang, Yen-Feng, Lai, Kuan-Lin, Liu, Hung-Yu, Chen, Wei-Ta *, Huang, William J., Coppola, Gianluca, Treede, Rolf-Detlef, Wang, Shuu-Jiun *
Publikováno v:
In The Journal of Pain May 2024