Yuntao Du | DeepAI

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∙ 07/31/2023

LaplaceConfidence: a Graph-based Approach for Learning with Noisy Labels

In real-world applications, perfect labels are rarely available, making ...

0 Mingcai Chen, et al. ∙

∙ 04/28/2023

Knowledge-refined Denoising Network for Robust Recommendation

Knowledge graph (KG), which contains rich side information, becomes an e...

0 Xinjun Zhu, et al. ∙

∙ 04/27/2023

Towards Explainable Collaborative Filtering with Taste Clusters Learning

Collaborative Filtering (CF) is a widely used and effective technique fo...

0 Yuntao Du, et al. ∙

∙ 02/13/2023

LDPTrace: Locally Differentially Private Trajectory Synthesis

Trajectory data has the potential to greatly benefit a wide-range of rea...

0 Yuntao Du, et al. ∙

∙ 10/06/2022

Spatial-Temporal Graph Convolutional Gated Recurrent Network for Traffic Forecasting

As an important part of intelligent transportation systems, traffic fore...

0 Le Zhao, et al. ∙

∙ 04/14/2022

Self-Guided Learning to Denoise for Robust Recommendation

The ubiquity of implicit feedback makes them the default choice to build...

0 Yunjun Gao, et al. ∙

∙ 04/11/2022

HAKG: Hierarchy-Aware Knowledge Gated Network for Recommendation

Knowledge graph (KG) plays an increasingly important role to improve the...

0 Yuntao Du, et al. ∙

∙ 02/08/2022

MetaKG: Meta-learning on Knowledge Graph for Cold-start Recommendation

A knowledge graph (KG) consists of a set of interconnected typed entitie...

0 Yuntao Du, et al. ∙

∙ 12/17/2021

Deep Spatially and Temporally Aware Similarity Computation for Road Network Constrained Trajectories

Trajectory similarity computation has drawn massive attention, as it is ...

0 Ziquan Fang, et al. ∙

∙ 12/06/2021

Two Wrongs Don't Make a Right: Combating Confirmation Bias in Learning with Label Noise

Noisy labels damage the performance of deep networks. For robust learnin...

0 Mingcai Chen, et al. ∙

∙ 10/13/2021

An Efficient Source Model Selection Framework in Model Databases

With the explosive increase of big data, training a Machine Learning (ML...

5 Minjun Zhao, et al. ∙

∙ 09/09/2021

Generation, augmentation, and alignment: A pseudo-source domain based method for source-free domain adaptation

Conventional unsupervised domain adaptation (UDA) methods need to access...

0 Yuntao Du, et al. ∙

∙ 08/10/2021

AdaRNN: Adaptive Learning and Forecasting of Time Series

Time series has wide applications in the real world and is known to be d...

0 Yuntao Du, et al. ∙

∙ 07/10/2021

Semi-Supervised Learning with Multi-Head Co-Training

Co-training, extended from self-training, is one of the frameworks for s...

0 Mingcai Chen, et al. ∙

∙ 06/29/2021

Cross-domain error minimization for unsupervised domain adaptation

Unsupervised domain adaptation aims to transfer knowledge from a labeled...

0 Yuntao Du, et al. ∙

∙ 03/26/2020

Unsupervised domain adaptation with exploring more statistics and discriminative information

Unsupervised domain adaptation aims at transferring knowledge from the l...

0 Yuntao Du, et al. ∙

∙ 01/01/2020

Dual Adversarial Domain Adaptation

Unsupervised domain adaptation aims at transferring knowledge from the l...

0 Yuntao Du, et al. ∙

∙ 12/31/2019

Homogeneous Online Transfer Learning with Online Distribution Discrepancy Minimization

Transfer learning has been demonstrated to be successful and essential i...

0 Yuntao Du, et al. ∙

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