Learning a recommender system model from an item's raw modality features...
Recommender systems (RS) have achieved significant success by leveraging...
Understanding the life cycle of the machine learning (ML) model is an
in...
Causal disentanglement aims to uncover a representation of data using la...
Gaussian process regression (GPR) is a non-parametric model that has bee...
Adapters, a plug-in neural network module with some tunable parameters, ...
Text-based collaborative filtering (TCF) has become the mainstream appro...
An important problem across disciplines is the discovery of intervention...
Electromyography signals can be used as training data by machine learnin...
The multiplexing and de-multiplexing of orbital angular momentum (OAM) b...
Human activity analysis based on sensor data plays a significant role in...
Self-supervised learning approach like contrastive learning is attached ...
Background: Electronic Health Records (EHRs) contain rich information of...
We conduct a subjective experiment to compare the performance of traditi...
Transforming a causal system from a given initial state to a desired tar...
Information compression is essential to reduce communication cost in
dis...
The acceleration of CNNs has gained increasing atten-tion since their su...
To conduct a radiomics or deep learning research experiment, the radiolo...
This research proposes a Ground Penetrating Radar (GPR) data processing
...
This paper proposes a fully asynchronous scheme for policy evaluation
of...
This paper considers the distributed optimization problem over a network...
Because tensor data appear more and more frequently in various scientifi...
Motivated by applications in various scientific fields having demand of
...
This paper proposes a distributed conjugate gradient tracking algorithm
...
This paper studies a decentralized stochastic gradient tracking (DSGT)
a...
Recent works have shown superiorities of decentralized SGD to centralize...
We describe an open-source simulator that creates sensor irradiance and
...
A popular asynchronous protocol for decentralized optimization is random...
Semantic segmentation is one of the basic topics in computer vision, it ...
This paper proposes a novel exact asynchronous subgradient-push algorith...
In this paper, we consider efficient differentially private empirical ri...