no code implementations • 26 Jun 2024 • Tian Tian, Lin F. Yang, Csaba Szepesvári
The constrained Markov decision process (CMDP) framework emerges as an important reinforcement learning approach for imposing safety or other critical objectives while maximizing cumulative reward.
1 code implementation • 20 May 2024 • Runou Yang, Tian Tian, Jinwen Tian
Addressing the challenge of domain shift between datasets is vital in maintaining model performance.
no code implementations • 7 May 2024 • Tian Tian, Liu Ze hui, Huang Zichen, Yubing Tang
This paper explores the application of AI and NLP techniques for user feedback analysis in the context of heavy machine crane products.
no code implementations • 3 May 2024 • Tian Tian, Ricky Cooper, Jiahao Deng, Qingquan Zhang
In essence, this research marks a pivotal moment in economic discourse, unveiling novel methodologies poised to redefine decision-making paradigms and elevate performance benchmarks for both investment entities and individual enterprises navigating the intricate tapestry of financial realms.
no code implementations • 3 May 2024 • Tian Tian, Jiahao Deng
This pioneering research introduces a novel approach for decision-makers in the heavy machinery industry, specifically focusing on production management.
no code implementations • 23 Aug 2023 • Tian Tian, Agastya Raj, Bruno Missi Xavier, Ying Zhang, Feiyun Wu, Kunde Yang
Accurate estimation of the Underwater acoustic (UWA) is a key part of underwater communications, especially for coherent systems.
no code implementations • 4 Jul 2022 • Tian Tian, Kenny Young, Richard S. Sutton
However, Asynchronous VI still requires a maximization over the entire action space, making it impractical for domains with large action space.
1 code implementation • 8 Jun 2022 • Yueqing Liang, Canyu Chen, Tian Tian, Kai Shu
Though we lack the sensitive attribute for training a fair model in the target domain, there might exist a similar domain that has sensitive attributes.
no code implementations • 2 May 2022 • Joseph Musielewicz, Xiaoxiao Wang, Tian Tian, Zachary Ulissi
Finally, we demonstrate a technique for leveraging the interactive functionality built in to VASP to efficiently compute single point calculations within our online active learning framework without the significant startup costs.
1 code implementation • 12 Feb 2022 • Hangwei Qian, Tian Tian, Chunyan Miao
Self-supervised learning establishes a new paradigm of learning representations with much fewer or even no label annotations.
no code implementations • 24 Jun 2021 • Xin Jin, Ji-Eun Lee, Charley Schaefer, Xinwei Luo, Adam J. M. Wollman, Alex L. Payne-Dwyer, Tian Tian, Xiaowei Zhang, Xiao Chen, Yingxing Li, Tom C. B. McLeish, Mark C. Leake, Fan Bai
Liquid-liquid phase separation is emerging as a crucial phenomenon in several fundamental cell processes.
1 code implementation • 2 Jun 2021 • Yingtao Luo, Qiang Liu, Yuntian Chen, WenBo Hu, Tian Tian, Jun Zhu
Especially, the discovery of PDEs with highly nonlinear coefficients from low-quality data remains largely under-addressed.
no code implementations • 14 Jun 2020 • Zhiheng Zhang, Wen-Bo Hu, Tian Tian, Jun Zhu
In this paper, we present the dynamic window-level Granger causality method (DWGC) for multi-channel time series data.
1 code implementation • ICML 2020 • Jianfei Chen, Cheng Lu, Biqi Chenli, Jun Zhu, Tian Tian
Generative flows are promising tractable models for density modeling that define probabilistic distributions with invertible transformations.
3 code implementations • 7 Mar 2019 • Kenny Young, Tian Tian
With the representation learning problem simplified, we can perform experiments with significantly less computational expense.
1 code implementation • NeurIPS 2018 • Yucen Luo, Tian Tian, Jiaxin Shi, Jun Zhu, Bo Zhang
We propose a new approach that includes a deep generative model (DGM) to characterize low-level features of the data, and a statistical relational model for noisy pairwise annotations on its subset.
1 code implementation • ICML 2018 • Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, Le Song
Deep learning on graph structures has shown exciting results in various applications.
no code implementations • JEPTALNRECITAL 2017 • Tian Tian, Isabelle Tellier, Marco Dinarelli, Pedro Cardoso
Dans cet article, nous proposons un mod{\`e}le pour d{\'e}tecter dans les textes g{\'e}n{\'e}r{\'e}s par des utilisateurs (en particulier les tweets), les mots non-standards {\`a} corriger.
no code implementations • COLING 2016 • Yuezhang Li, Ronghuo Zheng, Tian Tian, Zhiting Hu, Rahul Iyer, Katia Sycara
Due to the lack of structured knowledge applied in learning distributed representation of cate- gories, existing work cannot incorporate category hierarchies into entity information.
no code implementations • 12 May 2016 • Yuezhang Li, Ronghuo Zheng, Tian Tian, Zhiting Hu, Rahul Iyer, Katia Sycara
Due to the lack of structured knowledge applied in learning distributed representation of categories, existing work cannot incorporate category hierarchies into entity information.~We propose a framework that embeds entities and categories into a semantic space by integrating structured knowledge and taxonomy hierarchy from large knowledge bases.
no code implementations • 11 May 2016 • Tian Tian, Yuezhang Li
Machine comprehension plays an essential role in NLP and has been widely explored with dataset like MCTest.
no code implementations • LREC 2016 • Tian Tian, Marco Dinarelli, Isabelle Tellier, Pedro Dias Cardoso
We explain the specificities of this corpus with examples and describe some baseline experiments.
no code implementations • 7 Dec 2015 • Bei Chen, Jun Zhu, Nan Yang, Tian Tian, Ming Zhou, Bo Zhang
Modeling document structure is of great importance for discourse analysis and related applications.
no code implementations • NeurIPS 2015 • Tian Tian, Jun Zhu
Learning-from-crowds aims to design proper aggregation strategies to infer the unknown true labels from the noisy labels provided by ordinary web workers.
no code implementations • JEPTALNRECITAL 2015 • Tian Tian, Dinarelli Marco, Tellier Isabelle, Cardoso Pedro
Nous nous int{\'e}ressons dans cet article {\`a} l{'}apprentissage automatique d{'}un {\'e}tiqueteur mopho-syntaxique pour les tweets en anglais.