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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...
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08/17/2023
Bag of Tricks for Long-Tailed Multi-Label Classification on Chest X-Rays
Clinical classification of chest radiography is particularly challenging...
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08/03/2023
Balanced Destruction-Reconstruction Dynamics for Memory-replay Class Incremental Learning
Class incremental learning (CIL) aims to incrementally update a trained ...
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06/15/2023
On Strengthening and Defending Graph Reconstruction Attack with Markov Chain Approximation
Although powerful graph neural networks (GNNs) have boosted numerous rea...
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06/12/2023
Zero-shot Composed Text-Image Retrieval
In this paper, we consider the problem of composed image retrieval (CIR)...
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06/06/2023
Unleashing Mask: Explore the Intrinsic Out-of-Distribution Detection Capability
Out-of-distribution (OOD) detection is an indispensable aspect of secure...
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04/30/2023
Class-Balancing Diffusion Models
Diffusion-based models have shown the merits of generating high-quality ...
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04/05/2023
Towards Efficient Task-Driven Model Reprogramming with Foundation Models
Vision foundation models exhibit impressive power, benefiting from the e...
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03/01/2023
Combating Exacerbated Heterogeneity for Robust Models in Federated Learning
Privacy and security concerns in real-world applications have led to the...
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02/19/2023
Latent Class-Conditional Noise Model
Learning with noisy labels has become imperative in the Big Data era, wh...
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02/10/2023
Long-Tailed Partial Label Learning via Dynamic Rebalancing
Real-world data usually couples the label ambiguity and heavy imbalance,...
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12/14/2022
FedSkip: Combatting Statistical Heterogeneity with Federated Skip Aggregation
The statistical heterogeneity of the non-independent and identically dis...
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11/23/2022
NAS-LID: Efficient Neural Architecture Search with Local Intrinsic Dimension
One-shot neural architecture search (NAS) substantially improves the sea...
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07/07/2022
Device-Cloud Collaborative Recommendation via Meta Controller
On-device machine learning enables the lightweight deployment of recomme...
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05/25/2022
Contrastive Learning with Boosted Memorization
Self-supervised learning has achieved a great success in the representat...
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11/11/2021
Edge-Cloud Polarization and Collaboration: A Comprehensive Survey
Influenced by the great success of deep learning via cloud computing and...
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09/27/2021
Click-through Rate Prediction with Auto-Quantized Contrastive Learning
Click-through rate (CTR) prediction becomes indispensable in ubiquitous ...
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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...
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08/11/2021
Cooperative Learning for Noisy Supervision
Learning with noisy labels has gained the enormous interest in the robus...
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06/09/2021
Reliable Adversarial Distillation with Unreliable Teachers
In ordinary distillation, student networks are trained with soft labels ...
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04/14/2021
Device-Cloud Collaborative Learning for Recommendation
With the rapid development of storage and computing power on mobile devi...
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03/17/2021
Learning with Group Noise
Machine learning in the context of noise is a challenging but practical ...
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02/18/2021
Sparse-Interest Network for Sequential Recommendation
Recent methods in sequential recommendation focus on learning an overall...
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11/03/2020
Learning on Attribute-Missing Graphs
Graphs with complete node attributes have been widely explored recently....
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08/28/2020
Decoupled Variational Embedding for Signed Directed Networks
Node representation learning for signed directed networks has received c...
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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 ...
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07/23/2019
Node Attribute Generation on Graphs
Graph structured data provide two-fold information: graph structures and...
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03/06/2019
Safeguarded Dynamic Label Regression for Generalized Noisy Supervision
Learning with noisy labels, which aims to reduce expensive labors on acc...
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01/14/2019
How Does Disagreement Benefit Co-teaching?
Learning with noisy labels is one of the most important question in weak...
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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...
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09/22/2018
Variational Collaborative Learning for User Probabilistic Representation
Collaborative filtering (CF) has been successfully employed by many mode...
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07/10/2018
Understanding VAEs in Fisher-Shannon Plane
In information theory, Fisher information and Shannon information (entro...
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05/21/2018
Masking: A New Perspective of Noisy Supervision
It is important to learn classifiers under noisy labels due to their ubi...
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04/12/2018
Variational Composite Autoencoders
Learning in the latent variable model is challenging in the presence of ...
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02/19/2018
Degeneration in VAE: in the Light of Fisher Information Loss
Variational Autoencoder (VAE) is one of the most popular generative mode...
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02/10/2018
Collaborative Learning for Weakly Supervised Object Detection
Weakly supervised object detection has recently received much attention,...
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11/02/2017
