Search Results for author: Yan Jin

Found 18 papers, 7 papers with code

Transitive Vision-Language Prompt Learning for Domain Generalization

no code implementations29 Apr 2024 Liyuan Wang, Yan Jin, Zhen Chen, Jinlin Wu, Mengke Li, Yang Lu, Hanzi Wang

The vision-language pre-training has enabled deep models to make a huge step forward in generalizing across unseen domains.

Domain Generalization

Dynamically Anchored Prompting for Task-Imbalanced Continual Learning

1 code implementation23 Apr 2024 Chenxing Hong, Yan Jin, Zhiqi Kang, Yizhou Chen, Mengke Li, Yang Lu, Hanzi Wang

We find that imbalanced tasks significantly challenge the capability of models to control the trade-off between stability and plasticity from the perspective of recent prompt-based continual learning methods.

Continual Learning

Exploring CausalWorld: Enhancing robotic manipulation via knowledge transfer and curriculum learning

no code implementations25 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.

Transfer Learning

Pointerformer: Deep Reinforced Multi-Pointer Transformer for the Traveling Salesman Problem

1 code implementation19 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.

Decoder Traveling Salesman Problem

H-TSP: Hierarchically Solving the Large-Scale Travelling Salesman Problem

1 code implementation19 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).

Hierarchical Reinforcement Learning reinforcement-learning

Long-Tailed Visual Recognition via Self-Heterogeneous Integration with Knowledge Excavation

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.

Transfer Learning

Multi-Agent Reinforcement Learning with Shared Resources for Inventory Management

no code implementations15 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.

Management Multi-agent Reinforcement Learning +2

Incorporating Multi-armed Bandit with Local Search for MaxSAT

1 code implementation29 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.

Multi-Armed Bandits

Enhancing Mobile Robot Navigation Safety and Efficiency through NMPC with Relaxed CBF in Dynamic Environments

no code implementations21 Nov 2022 Nhat Nguyen Minh, Stephen McIlvanna, Yuzhu Sun, Yan Jin, Mien Van

In this paper, a safety-critical control strategy for a nonholonomic robot is developed to generate control signals that result in optimal, obstacle-free paths through dynamic environments.

Computational Efficiency Model Predictive Control +1

BandMaxSAT: A Local Search MaxSAT Solver with Multi-armed Bandit

no code implementations14 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.

ACE-BERT: Adversarial Cross-modal Enhanced BERT for E-commerce Retrieval

no code implementations14 Dec 2021 Boxuan Zhang, Chao Wei, Yan Jin, Weiru Zhang

In detail, ACE-BERT leverages the patch features and pixel features as image representation.

Inductive Bias Retrieval

Stochastic Item Descent Method for Large Scale Equal Circle Packing Problem

no code implementations22 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.

Effective reinforcement learning based local search for the maximum k-plex problem

no code implementations13 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.

reinforcement-learning Reinforcement Learning (RL)

KNPTC: Knowledge and Neural Machine Translation Powered Chinese Pinyin Typo Correction

no code implementations2 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.

Machine Translation NMT +2

Predicting Depression Severity by Multi-Modal Feature Engineering and Fusion

no code implementations29 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).

Feature Engineering

Prognostics of Surgical Site Infections using Dynamic Health Data

no code implementations12 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.

Decision Making Imputation +1

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