no code implementations • 30 Apr 2024 • Jiading Fang, Xiangshan Tan, Shengjie Lin, Igor Vasiljevic, Vitor Guizilini, Hongyuan Mei, Rares Ambrus, Gregory Shakhnarovich, Matthew R Walter
We introduce Transcrib3D, an approach that brings together 3D detection methods and the emergent reasoning capabilities of large language models (LLMs).
1 code implementation • 5 Apr 2024 • Yangqiaoyu Zhou, Haokun Liu, Tejes Srivastava, Hongyuan Mei, Chenhao Tan
We focus on hypothesis generation based on data (i. e., labeled examples).
1 code implementation • 29 Mar 2024 • Peng Ding, Jiading Fang, Peng Li, Kangrui Wang, Xiaochen Zhou, Mo Yu, Jing Li, Matthew R. Walter, Hongyuan Mei
The task is question-answering: for each maze, a large language model reads the walkthrough and answers hundreds of mapping and navigation questions such as "How should you go to Attic from West of House?"
1 code implementation • 10 Aug 2023 • Siqiao Xue, Fan Zhou, Yi Xu, Ming Jin, Qingsong Wen, Hongyan Hao, Qingyang Dai, Caigao Jiang, Hongyu Zhao, Shuo Xie, Jianshan He, James Zhang, Hongyuan Mei
We present WeaverBird, an intelligent dialogue system designed specifically for the finance domain.
1 code implementation • 16 Jul 2023 • Siqiao Xue, Xiaoming Shi, Zhixuan Chu, Yan Wang, Hongyan Hao, Fan Zhou, Caigao Jiang, Chen Pan, James Y. Zhang, Qingsong Wen, Jun Zhou, Hongyuan Mei
In this paper, we present EasyTPP, the first central repository of research assets (e. g., data, models, evaluation programs, documentations) in the area of event sequence modeling.
no code implementations • 30 Jun 2023 • Takuma Yoneda, Jiading Fang, Peng Li, Huanyu Zhang, Tianchong Jiang, Shengjie Lin, Ben Picker, David Yunis, Hongyuan Mei, Matthew R. Walter
In this paper, we explore a new dimension in which large language models may benefit robotics planning.
2 code implementations • NeurIPS 2023 • Xiaoming Shi, Siqiao Xue, Kangrui Wang, Fan Zhou, James Y. Zhang, Jun Zhou, Chenhao Tan, Hongyuan Mei
Large language models have shown astonishing performance on a wide range of reasoning tasks.
no code implementations • 20 May 2023 • Li Du, Hongyuan Mei, Jason Eisner
To predict the next token, autoregressive models ordinarily examine the past.
no code implementations • 6 Apr 2023 • Chen Feng Tsai, Xiaochen Zhou, Sierra S. Liu, Jing Li, Mo Yu, Hongyuan Mei
Large language models (LLMs) such as ChatGPT and GPT-4 have recently demonstrated their remarkable abilities of communicating with human users.
2 code implementations • 28 Mar 2023 • Hongyu Zhao, Kangrui Wang, Mo Yu, Hongyuan Mei
In this paper, we propose LEAP, a novel system that uses language models to perform multi-step logical reasoning and incorporates explicit planning into the inference procedure.
1 code implementation • 7 Dec 2022 • Jiasheng Gu, Hongyu Zhao, Hanzi Xu, Liangyu Nie, Hongyuan Mei, Wenpeng Yin
To our knowledge, this is the first work that systematically studies how robust a PLM is when it is supervised by instructions with different factors of variability.
no code implementations • 18 Oct 2022 • Hongyu Zhao, Hao Tan, Hongyuan Mei
Our tiny-attention adapter learns to modify the hidden states at each position directly conditioned on the hidden states at all the other positions, which is missed by the previously proposed adapters.
no code implementations • 18 Oct 2022 • Shuo Xie, Jiahao Qiu, Ankita Pasad, Li Du, Qing Qu, Hongyuan Mei
We propose to select layers based on the variability of their hidden states given a task-specific corpus.
3 code implementations • 4 Oct 2022 • Siqiao Xue, Xiaoming Shi, james Y zhang, Hongyuan Mei
In this paper, we tackle the important yet under-investigated problem of making long-horizon prediction of event sequences.
1 code implementation • 29 Jan 2022 • Chao Qu, Xiaoyu Tan, Siqiao Xue, Xiaoming Shi, James Zhang, Hongyuan Mei
We consider a sequential decision making problem where the agent faces the environment characterized by the stochastic discrete events and seeks an optimal intervention policy such that its long-term reward is maximized.
1 code implementation • ICLR 2022 • Chenghao Yang, Hongyuan Mei, Jason Eisner
The neural Hawkes process (Mei & Eisner, 2017) is a generative model of irregularly spaced sequences of discrete events.
2 code implementations • NeurIPS 2020 • Hongyuan Mei, Tom Wan, Jason Eisner
The log-likelihood of a generative model often involves both positive and negative terms.
no code implementations • 8 Jul 2020 • William Hua, Hongyuan Mei, Sarah Zohar, Magali Giral, Yanxun Xu
In the second step, we propose a policy gradient method to learn the personalized optimal clinical decision that maximizes the patient survival by interacting the MTPP with the model on clinical observations while accounting for uncertainties in clinical observations learned from the posterior inference of the Bayesian joint model in the first step.
Methodology
1 code implementation • ICML 2020 • Hongyuan Mei, Guanghui Qin, Minjie Xu, Jason Eisner
Learning how to predict future events from patterns of past events is difficult when the set of possible event types is large.
no code implementations • 25 Sep 2019 • Hongyuan Mei, Guanghui Qin, Minjie Xu, Jason Eisner
Consider a world in which events occur that involve various entities.
2 code implementations • 14 May 2019 • Hongyuan Mei, Guanghui Qin, Jason Eisner
On held-out incomplete sequences, our method is effective at inferring the ground-truth unobserved events, with particle smoothing consistently improving upon particle filtering.
no code implementations • NAACL 2019 • Shijia Liu, Hongyuan Mei, Adina Williams, Ryan Cotterell
While idiosyncrasies of the Chinese classifier system have been a richly studied topic among linguists (Adams and Conklin, 1973; Erbaugh, 1986; Lakoff, 1986), not much work has been done to quantify them with statistical methods.
no code implementations • 27 Sep 2018 • Hongyuan Mei, Guanghui Qin, Jason Eisner
Particle smoothing is an extension of particle filtering in which proposed events are conditioned on the future as well as the past.
no code implementations • SEMEVAL 2018 • Hongyuan Mei, Sheng Zhang, Kevin Duh, Benjamin Van Durme
Cross-lingual information extraction (CLIE) is an important and challenging task, especially in low resource scenarios.
8 code implementations • NeurIPS 2017 • Hongyuan Mei, Jason Eisner
Many events occur in the world.
no code implementations • 21 Nov 2016 • Hongyuan Mei, Mohit Bansal, Matthew R. Walter
We model coherent conversation continuation via RNN-based dialogue models equipped with a dynamic attention mechanism.
no code implementations • 30 Oct 2015 • Hang Chu, Hongyuan Mei, Mohit Bansal, Matthew R. Walter
We propose a method for accurately localizing ground vehicles with the aid of satellite imagery.
1 code implementation • NAACL 2016 • Hongyuan Mei, Mohit Bansal, Matthew R. Walter
We propose an end-to-end, domain-independent neural encoder-aligner-decoder model for selective generation, i. e., the joint task of content selection and surface realization.
1 code implementation • 12 Jun 2015 • Hongyuan Mei, Mohit Bansal, Matthew R. Walter
We propose a neural sequence-to-sequence model for direction following, a task that is essential to realizing effective autonomous agents.