Locality Preserving Multiview Graph Hashing for Large Scale Remote Sensing Image Search

no code implementations • 10 Apr 2023 • Wenyun Li, Guo Zhong, Xingyu Lu, Chi-Man Pun

This article proposes a multiview hashing with learnable parameters to retrieve the queried images for a large-scale remote sensing dataset.

Image Retrieval

EDUKG: a Heterogeneous Sustainable K-12 Educational Knowledge Graph

1 code implementation • 21 Oct 2022 • Bowen Zhao, Jiuding Sun, Bin Xu, Xingyu Lu, Yuchen Li, Jifan Yu, Minghui Liu, Tingjian Zhang, Qiuyang Chen, Hanming Li, Lei Hou, Juanzi Li

To tackle these issues, we propose EDUKG, a heterogeneous sustainable K-12 Educational Knowledge Graph.

Entity Linking

A framework for massive scale personalized promotion

no code implementations • 27 Aug 2021 • Yitao Shen, Yue Wang, Xingyu Lu, Feng Qi, Jia Yan, Yixiang Mu, Yao Yang, Yifan Peng, Jinjie Gu

In order to do effective optimization in the second stage, counterfactual prediction and noise-reduction are essential for the first stage.

Node Proximity Is All You Need: Unified Structural and Positional Node and Graph Embedding

1 code implementation • 26 Feb 2021 • Jing Zhu, Xingyu Lu, Mark Heimann, Danai Koutra

While most network embedding techniques model the relative positions of nodes in a network, recently there has been significant interest in structural embeddings that model node role equivalences, irrespective of their distances to any specific nodes.

Graph Embedding Network Embedding

Active Deep Learning on Entity Resolution by Risk Sampling

no code implementations • 23 Dec 2020 • Youcef Nafa, Qun Chen, Zhaoqiang Chen, Xingyu Lu, Haiyang He, Tianyi Duan, Zhanhuai Li

Building upon the recent advances in risk analysis for ER, which can provide a more refined estimate on label misprediction risk than the simpler classifier outputs, we propose a novel AL approach of risk sampling for ER.

Active Learning Entity Resolution

Dynamics Generalization via Information Bottleneck in Deep Reinforcement Learning

no code implementations • 3 Aug 2020 • Xingyu Lu, Kimin Lee, Pieter Abbeel, Stas Tiomkin

Despite the significant progress of deep reinforcement learning (RL) in solving sequential decision making problems, RL agents often overfit to training environments and struggle to adapt to new, unseen environments.

Decision Making reinforcement-learning +1

Predictive Coding for Boosting Deep Reinforcement Learning with Sparse Rewards

no code implementations • 21 Dec 2019 • Xingyu Lu, Stas Tiomkin, Pieter Abbeel

While recent progress in deep reinforcement learning has enabled robots to learn complex behaviors, tasks with long horizons and sparse rewards remain an ongoing challenge.

reinforcement-learning Reinforcement Learning (RL)

Why Does Hierarchy (Sometimes) Work So Well in Reinforcement Learning?

no code implementations • 23 Sep 2019 • Ofir Nachum, Haoran Tang, Xingyu Lu, Shixiang Gu, Honglak Lee, Sergey Levine

Hierarchical reinforcement learning has demonstrated significant success at solving difficult reinforcement learning (RL) tasks.

Hierarchical Reinforcement Learning reinforcement-learning +1