b'Jiangchao Yao'

research

∙ 09/13/2023

UniBrain: Universal Brain MRI Diagnosis with Hierarchical Knowledge-enhanced Pre-training

Magnetic resonance imaging (MRI) have played a crucial role in brain dis...

0 Jiayu Lei, et al. ∙

research

∙ 08/17/2023

Bag of Tricks for Long-Tailed Multi-Label Classification on Chest X-Rays

Clinical classification of chest radiography is particularly challenging...

0 Feng Hong, et al. ∙

research

∙ 08/03/2023

Balanced Destruction-Reconstruction Dynamics for Memory-replay Class Incremental Learning

Class incremental learning (CIL) aims to incrementally update a trained ...

0 Yuhang Zhou, et al. ∙

research

∙ 06/15/2023

On Strengthening and Defending Graph Reconstruction Attack with Markov Chain Approximation

Although powerful graph neural networks (GNNs) have boosted numerous rea...

0 Zhanke Zhou, et al. ∙

research

∙ 06/12/2023

Zero-shot Composed Text-Image Retrieval

In this paper, we consider the problem of composed image retrieval (CIR)...

2 Yikun Liu, et al. ∙

research

∙ 06/06/2023

Unleashing Mask: Explore the Intrinsic Out-of-Distribution Detection Capability

Out-of-distribution (OOD) detection is an indispensable aspect of secure...

0 Jianing Zhu, et al. ∙

research

∙ 04/30/2023

Class-Balancing Diffusion Models

Diffusion-based models have shown the merits of generating high-quality ...

0 Yiming Qin, et al. ∙

research

∙ 04/05/2023

Towards Efficient Task-Driven Model Reprogramming with Foundation Models

Vision foundation models exhibit impressive power, benefiting from the e...

0 Shoukai Xu, et al. ∙

research

∙ 03/01/2023

Combating Exacerbated Heterogeneity for Robust Models in Federated Learning

Privacy and security concerns in real-world applications have led to the...

0 Jianing Zhu, et al. ∙

research

∙ 02/19/2023

Latent Class-Conditional Noise Model

Learning with noisy labels has become imperative in the Big Data era, wh...

0 Jiangchao Yao, et al. ∙

research

∙ 02/10/2023

Long-Tailed Partial Label Learning via Dynamic Rebalancing

Real-world data usually couples the label ambiguity and heavy imbalance,...

0 Feng Hong, et al. ∙

research

∙ 12/14/2022

FedSkip: Combatting Statistical Heterogeneity with Federated Skip Aggregation

The statistical heterogeneity of the non-independent and identically dis...

0 Ziqing Fan, et al. ∙

research

∙ 11/23/2022

NAS-LID: Efficient Neural Architecture Search with Local Intrinsic Dimension

One-shot neural architecture search (NAS) substantially improves the sea...

0 Xin He, et al. ∙

research

∙ 07/07/2022

Device-Cloud Collaborative Recommendation via Meta Controller

On-device machine learning enables the lightweight deployment of recomme...

0 Jiangchao Yao, et al. ∙

research

∙ 05/25/2022

Contrastive Learning with Boosted Memorization

Self-supervised learning has achieved a great success in the representat...

0 Zhihan Zhou, et al. ∙

research

∙ 11/11/2021

Edge-Cloud Polarization and Collaboration: A Comprehensive Survey

Influenced by the great success of deep learning via cloud computing and...

0 Jiangchao Yao, et al. ∙

research

∙ 09/27/2021

Click-through Rate Prediction with Auto-Quantized Contrastive Learning

Click-through rate (CTR) prediction becomes indispensable in ubiquitous ...

0 Yujie Pan, et al. ∙

research

∙ 09/25/2021

MC^2-SF: Slow-Fast Learning for Mobile-Cloud Collaborative Recommendation

With the hardware development of mobile devices, it is possible to build...

6 Zeyuan Chen, et al. ∙

research

∙ 08/11/2021

Cooperative Learning for Noisy Supervision

Learning with noisy labels has gained the enormous interest in the robus...

0 Hao Wu, et al. ∙

research

∙ 06/09/2021

Reliable Adversarial Distillation with Unreliable Teachers

In ordinary distillation, student networks are trained with soft labels ...

1 Jianing Zhu, et al. ∙

research

∙ 04/14/2021

Device-Cloud Collaborative Learning for Recommendation

With the rapid development of storage and computing power on mobile devi...

0 Jiangchao Yao, et al. ∙

research

∙ 03/17/2021

Learning with Group Noise

Machine learning in the context of noise is a challenging but practical ...

0 Qizhou Wang, et al. ∙

research

∙ 02/18/2021

Sparse-Interest Network for Sequential Recommendation

Recent methods in sequential recommendation focus on learning an overall...

0 Qiaoyu Tan, et al. ∙

research

∙ 11/03/2020

Learning on Attribute-Missing Graphs

Graphs with complete node attributes have been widely explored recently....

3 Xu Chen, et al. ∙

research

∙ 08/28/2020

Decoupled Variational Embedding for Signed Directed Networks

Node representation learning for signed directed networks has received c...

7 Xu Chen, et al. ∙

research

∙ 08/28/2019

Bayes EMbedding (BEM): Refining Representation by Integrating Knowledge Graphs and Behavior-specific Networks

Low-dimensional embeddings of knowledge graphs and behavior graphs have ...

0 Yuting Ye, et al. ∙

research

∙ 07/23/2019

Node Attribute Generation on Graphs

Graph structured data provide two-fold information: graph structures and...

0 Xu Chen, et al. ∙

research

∙ 03/06/2019

Safeguarded Dynamic Label Regression for Generalized Noisy Supervision

Learning with noisy labels, which aims to reduce expensive labors on acc...

2 Jiangchao Yao, et al. ∙

research

∙ 01/14/2019

How Does Disagreement Benefit Co-teaching?

Learning with noisy labels is one of the most important question in weak...

0 Xingrui Yu, et al. ∙

research

∙ 09/28/2018

Pumpout: A Meta Approach for Robustly Training Deep Neural Networks with Noisy Labels

It is challenging to train deep neural networks robustly on the industri...

8 Bo Han, et al. ∙

research

∙ 09/22/2018

Variational Collaborative Learning for User Probabilistic Representation

Collaborative filtering (CF) has been successfully employed by many mode...

0 Kenan Cui, et al. ∙

research

∙ 07/10/2018

Understanding VAEs in Fisher-Shannon Plane

In information theory, Fisher information and Shannon information (entro...

0 Huangjie Zheng, et al. ∙

research

∙ 05/21/2018

Masking: A New Perspective of Noisy Supervision

It is important to learn classifiers under noisy labels due to their ubi...

0 Bo Han, et al. ∙

research

∙ 04/12/2018

Variational Composite Autoencoders

Learning in the latent variable model is challenging in the presence of ...

0 Jiangchao Yao, et al. ∙

research

∙ 02/19/2018

Degeneration in VAE: in the Light of Fisher Information Loss

Variational Autoencoder (VAE) is one of the most popular generative mode...

0 Huangjie Zheng, et al. ∙

research

∙ 02/10/2018

Collaborative Learning for Weakly Supervised Object Detection

Weakly supervised object detection has recently received much attention,...

0 Jiajie Wang, et al. ∙

research

∙ 11/02/2017

Deep Learning from Noisy Image Labels with Quality Embedding

There is an emerging trend to leverage noisy image datasets in many visu...

0 Jiangchao Yao, et al. ∙

Jiangchao Yao

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