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pro vyhledávání: '"Liu, Anthony"'
Autor:
Liu, Anthony Z., Wang, Xinhe, Sansom, Jacob, Fu, Yao, Choi, Jongwook, Sohn, Sungryull, Kim, Jaekyeom, Lee, Honglak
Large Language Models (LLMs) demonstrate strong abilities in common-sense reasoning and interactive decision-making, but often struggle with complex, long-horizon planning tasks. Recent techniques have sought to structure LLM outputs using control fl
Externí odkaz:
http://arxiv.org/abs/2411.13826
Autor:
Sohn, Sungryull, Lyu, Yiwei, Liu, Anthony, Logeswaran, Lajanugen, Kim, Dong-Ki, Shim, Dongsub, Lee, Honglak
Task-Oriented Dialogue (TOD) systems have become crucial components in interactive artificial intelligence applications. While recent advances have capitalized on pre-trained language models (PLMs), they exhibit limitations regarding transparency and
Externí odkaz:
http://arxiv.org/abs/2312.04668
Autor:
Logeswaran, Lajanugen, Sohn, Sungryull, Lyu, Yiwei, Liu, Anthony Zhe, Kim, Dong-Ki, Shim, Dongsub, Lee, Moontae, Lee, Honglak
One of the fundamental skills required for an agent acting in an environment to complete tasks is the ability to understand what actions are plausible at any given point. This work explores a novel use of code representations to reason about action p
Externí odkaz:
http://arxiv.org/abs/2311.09601
Planning is an important capability of artificial agents that perform long-horizon tasks in real-world environments. In this work, we explore the use of pre-trained language models (PLMs) to reason about plan sequences from text instructions in embod
Externí odkaz:
http://arxiv.org/abs/2303.09031
Real world tasks are hierarchical and compositional. Tasks can be composed of multiple subtasks (or sub-goals) that are dependent on each other. These subtasks are defined in terms of entities (e.g., "apple", "pear") that can be recombined to form ne
Externí odkaz:
http://arxiv.org/abs/2203.15034
Publikováno v:
In Modern Pathology January 2025 38(1)
Autor:
Lee, Kuang-Huei, Fischer, Ian, Liu, Anthony, Guo, Yijie, Lee, Honglak, Canny, John, Guadarrama, Sergio
The Predictive Information is the mutual information between the past and the future, I(X_past; X_future). We hypothesize that capturing the predictive information is useful in RL, since the ability to model what will happen next is necessary for suc
Externí odkaz:
http://arxiv.org/abs/2007.12401
Autor:
El Helali, Aya, Lam, Tai-Chung, Ko, Elaine Yee-Ling, Shih, David J.H., Chan, Chun Kau, Wong, Charlene H.L., Wong, Jason W.H., Cheung, Lydia W.T., Lau, Johnny K.S., Liu, Anthony P.Y., Chan, Ann S.Y., Loong, Herbert H., Lam, Stephen Tak Sum, Chan, Godfrey Chi-Fung, Lee, Victor H.F., Yuen, Kwok Keung, Ng, Wai-Tong, Lee, Anne W.M., Ma, Edmond S.K.
Publikováno v:
In The Lancet Regional Health - Western Pacific July 2023 36
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