Zobrazeno 1 - 10
of 65
pro vyhledávání: '"Wang, Kangrui"'
Autor:
Li, Manling, Zhao, Shiyu, Wang, Qineng, Wang, Kangrui, Zhou, Yu, Srivastava, Sanjana, Gokmen, Cem, Lee, Tony, Li, Li Erran, Zhang, Ruohan, Liu, Weiyu, Liang, Percy, Fei-Fei, Li, Mao, Jiayuan, Wu, Jiajun
We aim to evaluate Large Language Models (LLMs) for embodied decision making. While a significant body of work has been leveraging LLMs for decision making in embodied environments, we still lack a systematic understanding of their performance becaus
Externí odkaz:
http://arxiv.org/abs/2410.07166
Autor:
Ding, Peng, Fang, Jiading, Li, Peng, Wang, Kangrui, Zhou, Xiaochen, Yu, Mo, Li, Jing, Walter, Matthew R., Mei, Hongyuan
Large language models such as ChatGPT and GPT-4 have recently achieved astonishing performance on a variety of natural language processing tasks. In this paper, we propose MANGO, a benchmark to evaluate their capabilities to perform text-based mappin
Externí odkaz:
http://arxiv.org/abs/2403.19913
Autor:
Shi, Xiaoming, Xue, Siqiao, Wang, Kangrui, Zhou, Fan, Zhang, James Y., Zhou, Jun, Tan, Chenhao, Mei, Hongyuan
Large language models have shown astonishing performance on a wide range of reasoning tasks. In this paper, we investigate whether they could reason about real-world events and help improve the prediction performance of event sequence models. We desi
Externí odkaz:
http://arxiv.org/abs/2305.16646
Language models have been shown to perform remarkably well on a wide range of natural language processing tasks. In this paper, we propose LEAP, a novel system that uses language models to perform multi-step logical reasoning and incorporates explici
Externí odkaz:
http://arxiv.org/abs/2303.15714
In this paper, we focus on a typical two-phase phenomenon in the learning of multi-layer perceptrons (MLPs), and we aim to explain the reason for the decrease of feature diversity in the first phase. Specifically, people find that, in the training of
Externí odkaz:
http://arxiv.org/abs/2112.00980
Autor:
Wang, Kangrui, Chakrabarty, Dalia
We present a new method for learning Soft Random Geometric Graphs (SRGGs), drawn in probabilistic metric spaces, with the connection function of the graph defined as the marginal posterior probability of an edge random variable, given the correlation
Externí odkaz:
http://arxiv.org/abs/2002.01339
We consider evidence integration from potentially dependent observation processes under varying spatio-temporal sampling resolutions and noise levels. We develop a multi-resolution multi-task (MRGP) framework while allowing for both inter-task and in
Externí odkaz:
http://arxiv.org/abs/1906.08344
Akademický článek
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Autor:
Wang, Kangrui, Chakrabarty, Dalia
We undertake Bayesian learning of the high-dimensional functional relationship between a system parameter vector and an observable, that is in general tensor-valued. The ultimate aim is Bayesian inverse prediction of the system parameters, at which t
Externí odkaz:
http://arxiv.org/abs/1803.04582
Autor:
Chakrabarty, Dalia1 (AUTHOR) dalia.chakrabarty@brunel.ac.uk, Wang, Kangrui2 (AUTHOR), Roy, Gargi1 (AUTHOR), Bhojgaria, Akash3 (AUTHOR), Zhang, Chuqiao1 (AUTHOR), Pavlu, Jiri4 (AUTHOR), Chakrabartty, Joydeep3 (AUTHOR)
Publikováno v:
PLoS ONE. 10/19/2023, Vol. 18 Issue 10, p1-28. 28p.