Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Shen, Junhong"'
We present Unified PDE Solvers (UPS), a data- and compute-efficient approach to developing unified neural operators for diverse families of spatiotemporal PDEs from various domains, dimensions, and resolutions. UPS embeds different PDEs into a shared
Externí odkaz:
http://arxiv.org/abs/2403.07187
Large Language Models (LLMs) have demonstrated remarkable proficiency in understanding and generating natural language. However, their capabilities wane in highly specialized domains underrepresented in the pretraining corpus, such as physical and bi
Externí odkaz:
http://arxiv.org/abs/2402.05140
Autor:
Shen, Junhong, Li, Liam, Dery, Lucio M., Staten, Corey, Khodak, Mikhail, Neubig, Graham, Talwalkar, Ameet
Fine-tuning large-scale pretrained models has led to tremendous progress in well-studied modalities such as vision and NLP. However, similar gains have not been observed in many other modalities due to a lack of relevant pretrained models. In this wo
Externí odkaz:
http://arxiv.org/abs/2302.05738
While neural architecture search (NAS) has enabled automated machine learning (AutoML) for well-researched areas, its application to tasks beyond computer vision is still under-explored. As less-studied domains are precisely those where we expect Aut
Externí odkaz:
http://arxiv.org/abs/2204.07554
Autor:
Tu, Renbo, Roberts, Nicholas, Khodak, Mikhail, Shen, Junhong, Sala, Frederic, Talwalkar, Ameet
Most existing neural architecture search (NAS) benchmarks and algorithms prioritize well-studied tasks, e.g. image classification on CIFAR or ImageNet. This makes the performance of NAS approaches in more diverse areas poorly understood. In this pape
Externí odkaz:
http://arxiv.org/abs/2110.05668
Autor:
Shen, Junhong, Yang, Lin F.
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence, 35(11), 9558-9566 (2021)
Recently, deep reinforcement learning (RL) has achieved remarkable empirical success by integrating deep neural networks into RL frameworks. However, these algorithms often require a large number of training samples and admit little theoretical under
Externí odkaz:
http://arxiv.org/abs/2110.04422
Autor:
Yuan, Luyao, Zhou, Dongruo, Shen, Junhong, Gao, Jingdong, Chen, Jeffrey L., Gu, Quanquan, Wu, Ying Nian, Zhu, Song-Chun
Publikováno v:
Advances in Neural Information Processing Systems (2021)
In human pedagogy, teachers and students can interact adaptively to maximize communication efficiency. The teacher adjusts her teaching method for different students, and the student, after getting familiar with the teacher's instruction mechanism, c
Externí odkaz:
http://arxiv.org/abs/2110.00137
Publikováno v:
Emergent Communication Workshop, 33rd Conference on Neural Information Processing Systems (NeurIPS 2019)
Pragmatics studies how context can contribute to language meanings. In human communication, language is never interpreted out of context, and sentences can usually convey more information than their literal meanings. However, this mechanism is missin
Externí odkaz:
http://arxiv.org/abs/2001.07752
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Wang, Xiaoli, Shen, Junhong, Xu, Changyan, Wan, Chen, Yang, Haoyu, Qiu, Yu, Xu, Mengmeng, Duo, Wenjuan, Sun, Tongjun, Cui, Jie, Chu, Liang, Yang, Xiaodi
Publikováno v:
In Comparative Immunology, Microbiology and Infectious Diseases June 2023 97