Zobrazeno 1 - 10
of 98
pro vyhledávání: '"Zhang Jifan"'
Autor:
Zhang, Jifan, Jain, Lalit, Guo, Yang, Chen, Jiayi, Zhou, Kuan Lok, Suresh, Siddharth, Wagenmaker, Andrew, Sievert, Scott, Rogers, Timothy, Jamieson, Kevin, Mankoff, Robert, Nowak, Robert
We present a novel multimodal preference dataset for creative tasks, consisting of over 250 million human ratings on more than 2.2 million captions, collected through crowdsourcing rating data for The New Yorker's weekly cartoon caption contest over
Externí odkaz:
http://arxiv.org/abs/2406.10522
The application of modified thermodynamic system correction method to secondary reheat steam turbine
Publikováno v:
MATEC Web of Conferences, Vol 355, p 02058 (2022)
In order to calculation the enthalpy of wet steam in the secondary reheat turbine thermal system the thermodynamic system of the secondary reheat steam turbine based on the isentropic ideal expansion process line was corrected, This method simplifies
Externí odkaz:
https://doaj.org/article/70b0b8521bfa41c2af5b07d00127157f
Autor:
Soltani, Nasim, Zhang, Jifan, Salehi, Batool, Roy, Debashri, Nowak, Robert, Chowdhury, Kaushik
Collecting an over-the-air wireless communications training dataset for deep learning-based communication tasks is relatively simple. However, labeling the dataset requires expert involvement and domain knowledge, may involve private intellectual pro
Externí odkaz:
http://arxiv.org/abs/2402.04896
Autor:
Bhatt, Gantavya, Chen, Yifang, Das, Arnav M., Zhang, Jifan, Truong, Sang T., Mussmann, Stephen, Zhu, Yinglun, Bilmes, Jeffrey, Du, Simon S., Jamieson, Kevin, Ash, Jordan T., Nowak, Robert D.
Supervised finetuning (SFT) on instruction datasets has played a crucial role in achieving the remarkable zero-shot generalization capabilities observed in modern large language models (LLMs). However, the annotation efforts required to produce high
Externí odkaz:
http://arxiv.org/abs/2401.06692
Class imbalance is a prevalent issue in real world machine learning applications, often leading to poor performance in rare and minority classes. With an abundance of wild unlabeled data, active learning is perhaps the most effective technique in sol
Externí odkaz:
http://arxiv.org/abs/2312.09196
Autor:
Zhang, Jifan, Chen, Yifang, Canal, Gregory, Mussmann, Stephen, Das, Arnav M., Bhatt, Gantavya, Zhu, Yinglun, Bilmes, Jeffrey, Du, Simon Shaolei, Jamieson, Kevin, Nowak, Robert D
Labeled data are critical to modern machine learning applications, but obtaining labels can be expensive. To mitigate this cost, machine learning methods, such as transfer learning, semi-supervised learning and active learning, aim to be label-effici
Externí odkaz:
http://arxiv.org/abs/2306.09910
Label efficiency has become an increasingly important objective in deep learning applications. Active learning aims to reduce the number of labeled examples needed to train deep networks, but the empirical performance of active learning algorithms ca
Externí odkaz:
http://arxiv.org/abs/2302.07317
Autor:
Yang, Liu, Zhang, Jifan, Shenouda, Joseph, Papailiopoulos, Dimitris, Lee, Kangwook, Nowak, Robert D.
Weight decay is one of the most widely used forms of regularization in deep learning, and has been shown to improve generalization and robustness. The optimization objective driving weight decay is a sum of losses plus a term proportional to the sum
Externí odkaz:
http://arxiv.org/abs/2210.03069
Active learning is a label-efficient approach to train highly effective models while interactively selecting only small subsets of unlabelled data for labelling and training. In "open world" settings, the classes of interest can make up a small fract
Externí odkaz:
http://arxiv.org/abs/2202.01402
Publikováno v:
In Journal of Alloys and Compounds 25 October 2024 1003