Training a high-performance deep neural network requires large amounts o...
Data-free knowledge distillation (KD) helps transfer knowledge from a
pr...
Federated learning provides a promising privacy-preserving way for utili...
Machine learning (ML) in healthcare presents numerous opportunities for
...
Modern time series forecasting methods, such as Transformer and its vari...
Automatic radiology report summarization is a crucial clinical task, who...
As deep learning blooms with growing demand for computation and data
res...
Deep neural networks (DNNs) are vulnerable to backdoor attacks. Previous...
Recently, adversarial training has been incorporated in self-supervised
...
Increasing concerns have been raised on deep learning fairness in recent...
Federated learning (FL) provides a distributed learning framework for
mu...
In this paper we define and investigate the problem of persona
authentic...
Federated learning (FL) emerges as a popular distributed learning schema...
Federated Learning (FL) is a decentralized machine-learning paradigm, in...
The advances of sensor technology enable people to monitor air quality
t...
DNN-based face recognition models require large centrally aggregated fac...
Learning from Observations (LfO) is a practical reinforcement learning
s...
Training deep neural models in the presence of corrupted supervision is
...
Protecting privacy in learning while maintaining the model performance h...
This paper surveys the field of transfer learning in the problem setting...
Federated learning (FL) learns a model jointly from a set of participati...
In this paper, link selection is investigated in half-duplex (HD) dual-h...
This paper considers secure communication in buffer-aided cooperative
wi...
Model-free deep reinforcement learning (RL) has demonstrated its superio...
In this paper, link selection for half-duplex buffer-aided relay systems...
Sample inefficiency is a long-lasting problem in reinforcement learning ...
In recent years, increasingly augmentation of health data, such as patie...
Nowadays, almost all the online orders were placed through screened devi...
Recommending new items to existing users has remained a challenging prob...
We propose a sparse and low-rank tensor regression model to relate a
uni...
Multi-task learning (MTL) refers to the paradigm of learning multiple re...
Parkinson's Disease (PD) is one of the most prevalent neurodegenerative
...
Drug similarity has been studied to support downstream clinical tasks su...
Recent advances in blockchain technologies have provided exciting
opport...
Generative Adversarial Network (GAN) and its variants have recently attr...
Over the past decade a wide spectrum of machine learning models have bee...
Large-scale online ride-sharing platforms have substantially transformed...
Mild cognitive impairment (MCI) is a prodromal phase in the progression ...
The surging availability of electronic medical records (EHR) leads to
in...
The state-of-the-art mobile edge applications are generating intense tra...
While sparse coding-based clustering methods have shown to be successful...
The l1-regularized logistic regression (or sparse logistic regression) i...