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of 5
pro vyhledávání: '"Cromp, Sonia"'
Autor:
Roberts, Nicholas, Guo, Samuel, Gao, Zhiqi, GNVV, Satya Sai Srinath Namburi, Cromp, Sonia, Wu, Chengjun, Duan, Chengyu, Sala, Frederic
While Transformers underpin modern large language models (LMs), there is a growing list of alternative architectures with new capabilities, promises, and tradeoffs. This makes choosing the right LM architecture challenging. Recently-proposed $\textit
Externí odkaz:
http://arxiv.org/abs/2406.00894
Popular zero-shot models suffer due to artifacts inherited from pretraining. A particularly detrimental artifact, caused by unbalanced web-scale pretraining data, is mismatched label distribution. Existing approaches that seek to repair the label dis
Externí odkaz:
http://arxiv.org/abs/2404.08461
Autor:
Roberts, Nicholas, Li, Xintong, Adila, Dyah, Cromp, Sonia, Huang, Tzu-Heng, Zhao, Jitian, Sala, Frederic
Machine learning models -- including prominent zero-shot models -- are often trained on datasets whose labels are only a small proportion of a larger label space. Such spaces are commonly equipped with a metric that relates the labels via distances b
Externí odkaz:
http://arxiv.org/abs/2307.12226
Publikováno v:
NeurIPS 2023
Weak supervision enables efficient development of training sets by reducing the need for ground truth labels. However, the techniques that make weak supervision attractive -- such as integrating any source of signal to estimate unknown labels -- also
Externí odkaz:
http://arxiv.org/abs/2303.17713
Many tasks use data housed in relational databases to train boosted regression tree models. In this paper, we give a relational adaptation of the greedy algorithm for training boosted regression trees. For the subproblem of calculating the sum of squ
Externí odkaz:
http://arxiv.org/abs/2107.12373