no code implementations • 12 Nov 2016 • Chuyang Ke, Yan Jin, Heather Evans, Bill Lober, Xiaoning Qian, Ji Liu, Shuai Huang
Since existing prediction models of SSI have quite limited capacity to utilize the evolving clinical data, we develop the corresponding solution to equip these mHealth tools with decision-making capabilities for SSI prediction with a seamless assembly of several machine learning models to tackle the analytic challenges arising from the spatial-temporal data.
no code implementations • 29 Nov 2017 • Aven Samareh, Yan Jin, Zhangyang Wang, Xiangyu Chang, Shuai Huang
We present our preliminary work to determine if patient's vocal acoustic, linguistic, and facial patterns could predict clinical ratings of depression severity, namely Patient Health Questionnaire depression scale (PHQ-8).
no code implementations • 2 May 2018 • Hengyi Cai, Xingguang Ji, Yonghao Song, Yan Jin, Yang Zhang, Mairgup Mansur, Xiaofang Zhao
In contrast to previous work, KNPTC is able to integrate explicit knowledge into NMT for pinyin typo correction, and is able to learn to correct a variety of typos without the guidance of manually selected constraints or languagespecific features.
no code implementations • 13 Mar 2019 • Yan Jin, John H. Drake, Una Benlic, Kun He
The maximum k-plex problem is a computationally complex problem, which emerged from graph-theoretic social network studies.
no code implementations • 22 Jan 2020 • Kun He, Min Zhang, Jianrong Zhou, Yan Jin, Chu-min Li
Inspired by its success in deep learning, we apply the idea of SGD with batch selection of samples to a classic optimization problem in decision version.
1 code implementation • 8 Dec 2020 • Jiongzhi Zheng, Kun He, Jianrong Zhou, Yan Jin, Chu-min Li
We address the Traveling Salesman Problem (TSP), a famous NP-hard combinatorial optimization problem.
no code implementations • 14 Dec 2021 • Boxuan Zhang, Chao Wei, Yan Jin, Weiru Zhang
In detail, ACE-BERT leverages the patch features and pixel features as image representation.
no code implementations • 14 Jan 2022 • Jiongzhi Zheng, Kun He, Jianrong Zhou, Yan Jin, Chu-min Li, Felip Manya
We address Partial MaxSAT (PMS) and Weighted PMS (WPMS), two practical generalizations of the MaxSAT problem, and propose a local search algorithm for these problems, called BandMaxSAT, that applies a multi-armed bandit model to guide the search direction.
1 code implementation • 8 Jul 2022 • Jiongzhi Zheng, Kun He, Jianrong Zhou, Yan Jin, Chu-min Li
LKH-3 is a powerful extension of LKH that can solve many TSP variants.
no code implementations • 21 Nov 2022 • Nhat Nguyen Minh, Stephen McIlvanna, Yuzhu Sun, Yan Jin, Mien Van
We formulate the control synthesis problem as an optimal control problem that enforces control barrier function (CBF) constraints to achieve obstacle avoidance.
1 code implementation • 29 Nov 2022 • Jiongzhi Zheng, Kun He, Jianrong Zhou, Yan Jin, Chu-min Li, Felip Manyà
In this paper, we propose a local search algorithm for these problems, called BandHS, which applies two multi-armed bandits to guide the search directions when escaping local optima.
no code implementations • 15 Dec 2022 • Yuandong Ding, Mingxiao Feng, Guozi Liu, Wei Jiang, Chuheng Zhang, Li Zhao, Lei Song, Houqiang Li, Yan Jin, Jiang Bian
In this paper, we consider the inventory management (IM) problem where we need to make replenishment decisions for a large number of stock keeping units (SKUs) to balance their supply and demand.
1 code implementation • CVPR 2023 • Yan Jin, Mengke Li, Yang Lu, Yiu-ming Cheung, Hanzi Wang
To address this problem, state-of-the-art methods usually adopt a mixture of experts (MoE) to focus on different parts of the long-tailed distribution.
1 code implementation • 19 Apr 2023 • Yan Jin, Yuandong Ding, Xuanhao Pan, Kun He, Li Zhao, Tao Qin, Lei Song, Jiang Bian
Traveling Salesman Problem (TSP), as a classic routing optimization problem originally arising in the domain of transportation and logistics, has become a critical task in broader domains, such as manufacturing and biology.
1 code implementation • 19 Apr 2023 • Xuanhao Pan, Yan Jin, Yuandong Ding, Mingxiao Feng, Li Zhao, Lei Song, Jiang Bian
We propose an end-to-end learning framework based on hierarchical reinforcement learning, called H-TSP, for addressing the large-scale Travelling Salesman Problem (TSP).
no code implementations • 25 Mar 2024 • Xinrui Wang, Yan Jin
This study explores a learning-based tri-finger robotic arm manipulating task, which requires complex movements and coordination among the fingers.