Search Results for author: Haonan Wang

Found 18 papers, 6 papers with code

Can Single-Pass Contrastive Learning Work for Both Homophilic and Heterophilic Graph?

1 code implementation20 Nov 2022 Haonan Wang, Jieyu Zhang, Qi Zhu, Wei Huang

To answer this, we analyze the concentration property of features obtained by neighborhood aggregation on both homophilic and heterophilic graphs, introduce the single-pass graph contrastive learning loss based on the property, and provide performance guarantees of the minimizer of the loss on downstream tasks.

Contrastive Learning

BEBERT: Efficient and robust binary ensemble BERT

no code implementations28 Oct 2022 Jiayi Tian, Chao Fang, Haonan Wang, Zhongfeng Wang

Pre-trained BERT models have achieved impressive accuracy on natural language processing (NLP) tasks.

Binarization Knowledge Distillation

A Neural Corpus Indexer for Document Retrieval

no code implementations6 Jun 2022 Yujing Wang, Yingyan Hou, Haonan Wang, Ziming Miao, Shibin Wu, Hao Sun, Qi Chen, Yuqing Xia, Chengmin Chi, Guoshuai Zhao, Zheng Liu, Xing Xie, Hao Allen Sun, Weiwei Deng, Qi Zhang, Mao Yang

To this end, we propose Neural Corpus Indexer (NCI), a sequence-to-sequence network that generates relevant document identifiers directly for a designated query.

Retrieval TriviaQA

Understanding Programmatic Weak Supervision via Source-aware Influence Function

no code implementations25 May 2022 Jieyu Zhang, Haonan Wang, Cheng-Yu Hsieh, Alexander Ratner

Programmatic Weak Supervision (PWS) aggregates the source votes of multiple weak supervision sources into probabilistic training labels, which are in turn used to train an end model.

Augmentation-Free Graph Contrastive Learning with Performance Guarantee

no code implementations11 Apr 2022 Haonan Wang, Jieyu Zhang, Qi Zhu, Wei Huang

Graph contrastive learning (GCL) is the most representative and prevalent self-supervised learning approach for graph-structured data.

Contrastive Learning Self-Supervised Learning

Training Fair Deep Neural Networks by Balancing Influence

no code implementations15 Jan 2022 Haonan Wang, Ziwei Wu, Jingrui He

Most fair machine learning methods either highly rely on the sensitive information of the training samples or require a large modification on the target models, which hinders their practical application.

Fairness

From Intrinsic to Counterfactual: On the Explainability of Contextualized Recommender Systems

no code implementations28 Oct 2021 Yao Zhou, Haonan Wang, Jingrui He, Haixun Wang

With the prevalence of deep learning based embedding approaches, recommender systems have become a proven and indispensable tool in various information filtering applications.

Explainable Models Explainable Recommendation +1

Deep Active Learning by Leveraging Training Dynamics

no code implementations16 Oct 2021 Haonan Wang, Wei Huang, Ziwei Wu, Andrew Margenot, Hanghang Tong, Jingrui He

Active learning theories and methods have been extensively studied in classical statistical learning settings.

Active Learning

UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformer

2 code implementations9 Sep 2021 Haonan Wang, Peng Cao, Jiaqi Wang, Osmar R. Zaiane

Specifically, the CTrans module is an alternate of the U-Net skip connections, which consists of a sub-module to conduct the multi-scale Channel Cross fusion with Transformer (named CCT) and a sub-module Channel-wise Cross-Attention (named CCA) to guide the fused multi-scale channel-wise information to effectively connect to the decoder features for eliminating the ambiguity.

 Ranked #1 on Medical Image Segmentation on GlaS (IoU metric)

Image Segmentation Medical Image Segmentation +1

Controllable Gradient Item Retrieval

1 code implementation31 May 2021 Haonan Wang, Chang Zhou, Carl Yang, Hongxia Yang, Jingrui He

A better way is to present a sequence of products with increasingly floral attributes based on the white dress, and allow the customer to select the most satisfactory one from the sequence.

Disentanglement Retrieval

Adversarial Data Generation of Multi-category Marked Temporal Point Processes with Sparse, Incomplete, and Small Training Samples

no code implementations1 Jan 2021 Shashika Ranga Muramudalige, Anura Jayasumana, Haonan Wang

The transformation of training data to the distribution facilitates the accurate capture of underlying process characteristics despite the sparseness and incompleteness of data.

Point Processes

Secure Network Release with Link Privacy

no code implementations28 Sep 2020 Carl Yang, Haonan Wang, Ke Zhang, Lichao Sun

Many data mining and analytical tasks rely on the abstraction of networks (graphs) to summarize relational structures among individuals (nodes).

Graph Generation

Transfer Learning of Graph Neural Networks with Ego-graph Information Maximization

1 code implementation NeurIPS 2021 Qi Zhu, Carl Yang, Yidan Xu, Haonan Wang, Chao Zhang, Jiawei Han

Graph neural networks (GNNs) have achieved superior performance in various applications, but training dedicated GNNs can be costly for large-scale graphs.

Knowledge Graphs Transfer Learning

Secure Deep Graph Generation with Link Differential Privacy

1 code implementation1 May 2020 Carl Yang, Haonan Wang, Ke Zhang, Liang Chen, Lichao Sun

Many data mining and analytical tasks rely on the abstraction of networks (graphs) to summarize relational structures among individuals (nodes).

Graph Generation Link Prediction

Relation Learning on Social Networks with Multi-Modal Graph Edge Variational Autoencoders

no code implementations4 Nov 2019 Carl Yang, Jieyu Zhang, Haonan Wang, Sha Li, Myungwan Kim, Matt Walker, Yiou Xiao, Jiawei Han

While node semantics have been extensively explored in social networks, little research attention has been paid to profile edge semantics, i. e., social relations.

Learning to Transfer via Modelling Multi-level Task Dependency

no code implementations25 Sep 2019 Haonan Wang, Zhenbang Wu, Ziniu Hu, Yizhou Sun

Besides, the understanding of relationships among tasks has been ignored by most of the current methods.

Multi-Task Learning

Design Light-weight 3D Convolutional Networks for Video Recognition Temporal Residual, Fully Separable Block, and Fast Algorithm

no code implementations31 May 2019 Haonan Wang, Jun Lin, Zhongfeng Wang

Deep 3-dimensional (3D) Convolutional Network (ConvNet) has shown promising performance on video recognition tasks because of its powerful spatio-temporal information fusion ability.

Video Recognition

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