Results 231 to 240 of about 5,805,357 (277)

Domain-Adversarial Training of Neural Networks

Journal of machine learning research, 2015
We introduce a new representation learning approach for domain adaptation, in which data at training and test time come from similar but different distributions.
Yaroslav Ganin, E. Ustinova, Hana Ajakan, Pascal Germain, H. Larochelle, François Laviolette, M. Marchand, V. Lempitsky   +7 more
semanticscholar   +1 more source

Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks

European Conference on Computer Vision, 2020
Large-scale pre-training methods of learning cross-modal representations on image-text pairs are becoming popular for vision-language tasks. While existing methods simply concatenate image region features and text features as input to the model to be pre-
Xiujun Li, Xi Yin, Chunyuan Li, Xiaowei Hu, Pengchuan Zhang, Lei Zhang, Lijuan Wang, Houdong Hu, Li Dong, Furu Wei, Yejin Choi, Jianfeng Gao   +11 more
semanticscholar   +1 more source

“Training, Training, Training”

2017
This chapter deals with the Training system of the Iraqi armed forces. It describes the Training Division of the General Staff, its missions and responsibilities and its development, especially during the Iran-Iraq war, the Iraqi Military Doctrine and training methods, and the staff directorates subordinated to it.
Pesach Malovany, Amatzia Baram, Kevin M. Woods, Ronna Englesberg   +3 more
openaire   +1 more source

Training

Emergency Nurse, 2008
The National Patient Safety Agency (NPSA) has launched a Foresight Training Resource Pack to improve the safety of patients treated in the NHS.
openaire   +2 more sources

InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models

arXiv.org
We introduce InternVL3, a significant advancement in the InternVL series featuring a native multimodal pre-training paradigm. Rather than adapting a text-only large language model (LLM) into a multimodal large language model (MLLM) that supports visual ...
Jinguo Zhu, Weiyun Wang, Zhe Chen, Zhaoyang Liu, Shenglong Ye, Lixin Gu, Yuchen Duan, Hao Tian, Weijie Su, Jie Shao, Zhangwei Gao, Erfei Cui, Yue Cao, Yangzhou Liu, Haomin Wang, Weiye Xu, Hao Li, Jiahao Wang, Han Lv, De-Hua Chen, Songze Li, Yinan He, Tan Jiang, Jiapeng Luo, Yi Wang, Conghui He, Botian Shi, Xingcheng Zhang, Wenqi Shao, Junjun He, Ying Xiong, Wenwen Qu, Peng Sun, Penglong Jiao, Lijun Wu, Kai Zhang, Hui Deng, Jiaye Ge, Kaiming Chen, Limin Wang, Min Dou, Lewei Lu, Xizhou Zhu, Tong Lu, Dahua Lin, Yu Qiao, Jifeng Dai, Wenhai Wang   +47 more
semanticscholar   +1 more source

Understanding R1-Zero-Like Training: A Critical Perspective

arXiv.org
DeepSeek-R1-Zero has shown that reinforcement learning (RL) at scale can directly enhance the reasoning capabilities of LLMs without supervised fine-tuning.
Zi-Yan Liu, Changyu Chen, Wenjun Li, Penghui Qi, Tianyu Pang, Chao Du, Wee Sun Lee, Min Lin   +7 more
semanticscholar   +1 more source

Training

Nursing Older People, 2009
The Royal Society for Public Health is holding a one-day course providing vital guidance on the nutritional needs of older people. The course includes advice on nutritional assessment, catering, practical cooking and menu planning.
openaire   +2 more sources

TÜLU 3: Pushing Frontiers in Open Language Model Post-Training

arXiv.org
Language model post-training is applied to refine behaviors and unlock new skills across a wide range of recent language models, but open recipes for applying these techniques lag behind proprietary ones. The underlying training data and recipes for post-
Nathan Lambert, Jacob Daniel Morrison, Valentina Pyatkin, Shengyi Huang, Hamish Ivison, Faeze Brahman, Lester James Validad Miranda, Alisa Liu, Nouha Dziri, Xinxi Lyu, Yuling Gu, Saumya Malik, Victoria Graf, Jena D. Hwang, Jiangjiang Yang, Ronan Le Bras, Oyvind Tafjord, Christopher Wilhelm, Luca Soldaini, Noah A. Smith, Yizhong Wang, Pradeep Dasigi, Hanna Hajishirzi   +22 more
semanticscholar   +1 more source

Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training

arXiv.org
Humans are capable of strategically deceptive behavior: behaving helpfully in most situations, but then behaving very differently in order to pursue alternative objectives when given the opportunity.
Evan Hubinger, Carson E. Denison, Jesse Mu, Mike Lambert, Meg Tong, M. MacDiarmid, Tamera Lanham, Daniel M. Ziegler, Tim Maxwell, Newton Cheng, Adam Jermyn, Amanda Askell, Ansh Radhakrishnan, Cem Anil, D. Duvenaud, Deep Ganguli, Fazl Barez, J. Clark, Kamal Ndousse, Kshitij Sachan, M. Sellitto, Mrinank Sharma, Nova Dassarma, Roger Grosse, Shauna Kravec, Yuntao Bai, Zachary Witten, Marina Favaro, J. Brauner, Holden Karnofsky, P. Christiano, Samuel R. Bowman, Logan Graham, Jared Kaplan, S. Mindermann, R. Greenblatt, Buck Shlegeris, Nicholas Schiefer, Ethan Perez   +38 more
semanticscholar   +1 more source

Training benefits

Nursing Standard, 1987
Training district nurses in health education benefits the nurses themselves, users of the service and management, according to a health promotion officer from Nottinghamshire.
openaire   +2 more sources