Search Results for author: Hanchen Wang

Found 12 papers, 6 papers with code

Reinforcement Learning Based Query Vertex Ordering Model for Subgraph Matching

no code implementations25 Jan 2022 Hanchen Wang, Ying Zhang, Lu Qin, Wei Wang, Wenjie Zhang, Xuemin Lin

In recent years, many advanced techniques for query vertex ordering (i. e., matching order generation) have been proposed to reduce the unpromising intermediate results according to the preset heuristic rules.

reinforcement-learning

Iterative Teaching by Label Synthesis

no code implementations NeurIPS 2021 Weiyang Liu, Zhen Liu, Hanchen Wang, Liam Paull, Bernhard Schölkopf, Adrian Weller

In this paper, we consider the problem of iterative machine teaching, where a teacher provides examples sequentially based on the current iterative learner.

MLReal: Bridging the gap between training on synthetic data and real data applications in machine learning

no code implementations11 Sep 2021 Tariq Alkhalifah, Hanchen Wang, Oleg Ovcharenko

This is accomplished by applying two operations on the input data to the NN model: 1) The crosscorrelation of the input data (i. e., shot gather, seismic image, etc.)

Domain Adaptation

Matching Point Sets with Quantum Circuit Learning

no code implementations12 Feb 2021 Mohammadreza Noormandipour, Hanchen Wang

In this work, we propose a parameterised quantum circuit learning approach to point set matching problem.

set matching

Unsupervised Point Cloud Pre-Training via Occlusion Completion

1 code implementation ICCV 2021 Hanchen Wang, Qi Liu, Xiangyu Yue, Joan Lasenby, Matthew J. Kusner

We find that even when we construct a single pre-training dataset (from ModelNet40), this pre-training method improves accuracy across different datasets and encoders, on a wide range of downstream tasks.

3D Point Cloud Linear Classification Point Cloud Pre-training +2

Pre-Training by Completing Point Clouds

no code implementations28 Sep 2020 Hanchen Wang, Qi Liu, Xiangyu Yue, Joan Lasenby, Matt Kusner

There has recently been a flurry of exciting advances in deep learning models on point clouds.

GoGNN: Graph of Graphs Neural Network for Predicting Structured Entity Interactions

1 code implementation12 May 2020 Hanchen Wang, Defu Lian, Ying Zhang, Lu Qin, Xuemin Lin

We observe that existing works on structured entity interaction prediction cannot properly exploit the unique graph of graphs model.

Binarized Graph Neural Network

no code implementations19 Apr 2020 Hanchen Wang, Defu Lian, Ying Zhang, Lu Qin, Xiangjian He, Yiguang Lin, Xuemin Lin

Our proposed method can be seamlessly integrated into the existing GNN-based embedding approaches to binarize the model parameters and learn the compact embedding.

Graph Embedding

Neural Random Subspace

1 code implementation18 Nov 2019 Yun-Hao Cao, Jianxin Wu, Hanchen Wang, Joan Lasenby

The random subspace method, known as the pillar of random forests, is good at making precise and robust predictions.

Representation Learning

An Empirical Study on Learning Fairness Metrics for COMPAS Data with Human Supervision

1 code implementation22 Oct 2019 Hanchen Wang, Nina Grgic-Hlaca, Preethi Lahoti, Krishna P. Gummadi, Adrian Weller

We do not provide a way to directly learn a similarity metric satisfying the individual fairness, but to provide an empirical study on how to derive the similarity metric from human supervisors, then future work can use this as a tool to understand human supervision.

Fairness Metric Learning

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