Search Results for author: Yang Yue

Found 15 papers, 7 papers with code

Jointly spatial-temporal representation learning for individual trajectories

no code implementations7 Dec 2023 Fei Huang, Jianrong Lv, Yang Yue

The proposed ST-GraphRL consists of three compositions: (i) a weighted directed spatial-temporal graph to explicitly construct mobility interactions in both space and time dimensions; (ii) a two-stage jointly encoder (i. e., decoupling and fusion), to learn entangled spatial-temporal dependencies by independently decomposing and jointly aggregating space and time information; (iii) a decoder guides ST-GraphRL to learn explicit mobility regularities by simulating the spatial-temporal distributions of trajectories.

Representation Learning

Understanding, Predicting and Better Resolving Q-Value Divergence in Offline-RL

2 code implementations NeurIPS 2023 Yang Yue, Rui Lu, Bingyi Kang, Shiji Song, Gao Huang

We first identify a fundamental pattern, self-excitation, as the primary cause of Q-value estimation divergence in offline RL.

Attribute Offline RL

Improving and Benchmarking Offline Reinforcement Learning Algorithms

1 code implementation1 Jun 2023 Bingyi Kang, Xiao Ma, Yirui Wang, Yang Yue, Shuicheng Yan

Recently, Offline Reinforcement Learning (RL) has achieved remarkable progress with the emergence of various algorithms and datasets.

Attribute Benchmarking +4

Confidence-based Reliable Learning under Dual Noises

no code implementations10 Feb 2023 Peng Cui, Yang Yue, Zhijie Deng, Jun Zhu

Deep neural networks (DNNs) have achieved remarkable success in a variety of computer vision tasks, where massive labeled images are routinely required for model optimization.

Model Optimization

Boosting Offline Reinforcement Learning via Data Rebalancing

no code implementations17 Oct 2022 Yang Yue, Bingyi Kang, Xiao Ma, Zhongwen Xu, Gao Huang, Shuicheng Yan

Therefore, we propose a simple yet effective method to boost offline RL algorithms based on the observation that resampling a dataset keeps the distribution support unchanged.

D4RL Offline RL +2

AdaFocusV3: On Unified Spatial-temporal Dynamic Video Recognition

no code implementations27 Sep 2022 Yulin Wang, Yang Yue, Xinhong Xu, Ali Hassani, Victor Kulikov, Nikita Orlov, Shiji Song, Humphrey Shi, Gao Huang

Recent research has revealed that reducing the temporal and spatial redundancy are both effective approaches towards efficient video recognition, e. g., allocating the majority of computation to a task-relevant subset of frames or the most valuable image regions of each frame.

Video Recognition

Value-Consistent Representation Learning for Data-Efficient Reinforcement Learning

no code implementations25 Jun 2022 Yang Yue, Bingyi Kang, Zhongwen Xu, Gao Huang, Shuicheng Yan

Recently, visual representation learning has been shown to be effective and promising for boosting sample efficiency in RL.

Contrastive Learning Data Augmentation +5

Improving short-term bike sharing demand forecast through an irregular convolutional neural network

no code implementations9 Feb 2022 Xinyu Li, Yang Xu, Xiaohu Zhang, Wenzhong Shi, Yang Yue, Qingquan Li

As an important task for the management of bike sharing systems, accurate forecast of travel demand could facilitate dispatch and relocation of bicycles to improve user satisfaction.

Management

Zero-Shot Learning in Named-Entity Recognition with External Knowledge

1 code implementation15 Nov 2021 Nguyen Van Hoang, Soeren Hougaard Mulvad, Dexter Neo Yuan Rong, Yang Yue

We propose ZERO, a model that performs zero-shot and few-shot learning in NER to generalize to unseen domains by incorporating pre-existing knowledge in the form of semantic word embeddings.

Few-Shot Learning named-entity-recognition +4

Separating Content and Style for Unsupervised Image-to-Image Translation

1 code implementation27 Oct 2021 Yunfei Liu, Haofei Wang, Yang Yue, Feng Lu

Unsupervised image-to-image translation aims to learn the mapping between two visual domains with unpaired samples.

Translation Unsupervised Image-To-Image Translation

A SLAM Map Restoration Algorithm Based on Submaps and an Undirected Connected Graph

no code implementations29 Jul 2020 Zongqian Zhan, Wenjie Jian, Yi-Hui Li, Xin Wang, Yang Yue

To solve the missing map problem, which is an issue in many applications , after the tracking is lost, based on monocular visual SLAM, we present a method of reconstructing a complete global map of UAV datasets by sequentially merging the submaps via the corresponding undirected connected graph.

Simultaneous Localization and Mapping

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