no code implementations • CCL 2020 • Wangda Luo, YuHan Liu, Bin Liang, Ruifeng Xu
针对问答立场任务中, 现有方法难以提取问答文本间的依赖关系问题, 本文提出一种基于循环交互注意力(Recurrent Interactive Attention, RIA)网络的问答立场分析方法。该方法通过模仿人类阅读理解时的思维方式, 基于交互注意力机制和循环迭代方法, 有效地从问题和答案的相互联系中挖掘问答文本的立场信息。此外, 该方法将问题进行陈述化表示, 有效地解决疑问句表述下问题文本无法明确表达自身立场的问题。实验结果表明, 本文方法取得了比现有模型方法更好的效果, 同时证明该方法能有效拟合问答立场分析任务中的问答对依赖关系。
no code implementations • Findings (EMNLP) 2021 • Jun Gao, YuHan Liu, Haolin Deng, Wei Wang, Yu Cao, Jiachen Du, Ruifeng Xu
The emotion cause is a stimulus for human emotions.
no code implementations • 20 Mar 2023 • YuHan Liu, Anna Fang, Glen Moriarty, Robert Kraut, Haiyi Zhu
Online mental health communities (OMHCs) are an effective and accessible channel to give and receive social support for individuals with mental and emotional issues.
no code implementations • 7 Nov 2022 • Jayadev Acharya, YuHan Liu, Ziteng Sun
Perhaps surprisingly, we show that in suitable parameter regimes, having $m$ samples per user is equivalent to having $m$ times more users, each with only one sample.
no code implementations • 7 Nov 2022 • YuHan Liu, Pengyu Wang, Roland Tóth
Gaussian process (GP) based estimation of system models is an effective tool to learn unknown dynamics directly from input/output data.
no code implementations • 26 Sep 2022 • Pengzhi Yang, YuHan Liu, Shumon Koga, Arash Asgharivaskasi, Nikolay Atanasov
This paper proposes a method for learning continuous control policies for active landmark localization and exploration using an information-theoretic cost.
1 code implementation • 9 Jun 2022 • Shangbin Feng, Zhaoxuan Tan, Herun Wan, Ningnan Wang, Zilong Chen, Binchi Zhang, Qinghua Zheng, Wenqian Zhang, Zhenyu Lei, Shujie Yang, Xinshun Feng, Qingyue Zhang, Hongrui Wang, YuHan Liu, Yuyang Bai, Heng Wang, Zijian Cai, Yanbo Wang, Lijing Zheng, Zihan Ma, Jundong Li, Minnan Luo
Twitter bot detection has become an increasingly important task to combat misinformation, facilitate social media moderation, and preserve the integrity of the online discourse.
no code implementations • 7 Jun 2022 • YuHan Liu, Ananda Theertha Suresh, Wennan Zhu, Peter Kairouz, Marco Gruteser
We consider the heterogeneous scenario where both the quantity and distribution of data can be different for each user.
no code implementations • 6 Jun 2022 • Zhaolin Zhang, Mingqi Song, Wugang Meng, YuHan Liu, Fengcong Li, Xiang Feng, Yinan Zhao
First, this method decomposes and reconstructs the radar signal according to the difference in the reflected echo frequency between the limbs and the trunk of the human body.
1 code implementation • 7 May 2022 • YuHan Liu, Jun Gao, Jiachen Du, Lanjun Zhou, Ruifeng Xu
The emotion-aware dialogue management contains two parts: (1) Emotion state tracking maintains the current emotion state of the user and (2) Empathetic dialogue policy selection predicts a target emotion and a user's intent based on the results of the emotion state tracking.
no code implementations • 22 Dec 2021 • YuHan Liu, Roland Tóth
In this paper, we present a novel Dual Gaussian Process (DGP) based model predictive control strategy that improves the performance of a quadcopter during trajectory tracking.
no code implementations • NeurIPS 2021 • Jayadev Acharya, Clement Canonne, YuHan Liu, Ziteng Sun, Himanshu Tyagi
We obtain tight minimax rates for the problem of distributed estimation of discrete distributions under communication constraints, where $n$ users observing $m $ samples each can broadcast only $\ell$ bits.
no code implementations • 15 Oct 2021 • Ryan Jacobs, Mingren Shen, YuHan Liu, Wei Hao, Xiaoshan Li, Ruoyu He, Jacob RC Greaves, Donglin Wang, Zeming Xie, Zitong Huang, Chao Wang, Kevin G. Field, Dane Morgan
In this work, we perform semantic segmentation of multiple defect types in electron microscopy images of irradiated FeCrAl alloys using a deep learning Mask Regional Convolutional Neural Network (Mask R-CNN) model.
no code implementations • 19 Aug 2021 • Mingren Shen, Guanzhao Li, Dongxia Wu, YuHan Liu, Jacob Greaves, Wei Hao, Nathaniel J. Krakauer, Leah Krudy, Jacob Perez, Varun Sreenivasan, Bryan Sanchez, Oigimer Torres, Wei Li, Kevin Field, Dane Morgan
Electron microscopy is widely used to explore defects in crystal structures, but human detecting of defects is often time-consuming, error-prone, and unreliable, and is not scalable to large numbers of images or real-time analysis.
no code implementations • CVPR 2021 • Zhengqin Li, Ting-Wei Yu, Shen Sang, Sarah Wang, Meng Song, YuHan Liu, Yu-Ying Yeh, Rui Zhu, Nitesh Gundavarapu, Jia Shi, Sai Bi, Hong-Xing Yu, Zexiang Xu, Kalyan Sunkavalli, Milos Hasan, Ravi Ramamoorthi, Manmohan Chandraker
Finally, we demonstrate that our framework may also be integrated with physics engines, to create virtual robotics environments with unique ground truth such as friction coefficients and correspondence to real scenes.
no code implementations • 3 May 2021 • YuHan Liu, Yuhan Gao, Zhifan Nan, Long Chen
During the COVID-19 pandemic, people started to discuss about pandemic-related topics on social media.
1 code implementation • 2 Feb 2021 • YuHan Liu, Saurabh Agarwal, Shivaram Venkataraman
With the rapid adoption of machine learning (ML), a number of domains now use the approach of fine tuning models which were pre-trained on a large corpus of data.
no code implementations • 18 Jan 2021 • Arjun Balasubramanian, Adarsh Kumar, YuHan Liu, Han Cao, Shivaram Venkataraman, Aditya Akella
We present the design of GATI, an end-to-end prediction serving system that incorporates learned caches for low-latency DNN inference.
no code implementations • 30 Oct 2020 • Jayadev Acharya, Peter Kairouz, YuHan Liu, Ziteng Sun
We consider the problem of estimating sparse discrete distributions under local differential privacy (LDP) and communication constraints.
no code implementations • 14 Oct 2020 • Yunhai Han, YuHan Liu, David Paz, Henrik Christensen
Calibration of sensors is fundamental to robust performance for intelligent vehicles.
no code implementations • NeurIPS 2020 • Yuhan Liu, Ananda Theertha Suresh, Felix Yu, Sanjiv Kumar, Michael Riley
If each user has $m$ samples, we show that straightforward applications of Laplace or Gaussian mechanisms require the number of users to be $\mathcal{O}(k/(m\alpha^2) + k/\epsilon\alpha)$ to achieve an $\ell_1$ distance of $\alpha$ between the true and estimated distributions, with the privacy-induced penalty $k/\epsilon\alpha$ independent of the number of samples per user $m$.
no code implementations • 25 Jul 2020 • Zhengqin Li, Ting-Wei Yu, Shen Sang, Sarah Wang, Meng Song, YuHan Liu, Yu-Ying Yeh, Rui Zhu, Nitesh Gundavarapu, Jia Shi, Sai Bi, Zexiang Xu, Hong-Xing Yu, Kalyan Sunkavalli, Miloš Hašan, Ravi Ramamoorthi, Manmohan Chandraker
Finally, we demonstrate that our framework may also be integrated with physics engines, to create virtual robotics environments with unique ground truth such as friction coefficients and correspondence to real scenes.
no code implementations • 3 Dec 2018 • Ching Hua Lee, Guangjie Li, YuHan Liu, Tommy Tai, Ronny Thomale, Xiao Zhang
Non-Hermitian nodal knot metals (NKMs) contains intricate complex-valued energy bands gives rise to knotted exceptional loops and new topological surface states.
Mesoscale and Nanoscale Physics Materials Science Other Condensed Matter Mathematical Physics Mathematical Physics Quantum Physics
1 code implementation • 24 Mar 2018 • Yuhan Liu, Xiao Zhang, Maciej Lewenstein, Shi-Ju Ran
In this work, we implement simple numerical experiments, related to pattern/images classification, in which we represent the classifiers by many-qubit quantum states written in the matrix product states (MPS).