AUTOSAR (AUTomotive Open System ARchitecture) is an open industry standa...
We study the problem of learning with selectively labeled data, which ar...
Learning from noisy labels is an important and long-standing problem in
...
Distributed computing enables Internet of vehicle (IoV) services by
coll...
With the end of Moore's Law, there is a growing demand for rapid
archite...
This paper designs a helper-assisted resource allocation strategy in
non...
Model-based mutation analysis is a recent research area, and real-time s...
We present experimental results to explore a form of bivariate glyphs fo...
Semi-supervised learning (SSL) has achieved great success in leveraging ...
Deep networks have achieved great success in image rescaling (IR) task t...
Designing feasible and effective architectures under diverse computation...
We present and discuss the results of a two-year qualitative analysis of...
Self-supervised learning (SSL) has achieved remarkable performance in
pr...
Deep neural networks have exhibited remarkable performance in image
supe...
The Graph Convolutional Networks (GCNs) have achieved excellent results ...
In this paper, we investigate the problem of predicting the future volat...
Many real-world optimization problems such as engineering design can be
...
We present Adaptive Multi-layer Contrastive Graph Neural Networks (AMC-G...
A novel multi-scale temporal convolutional network (TCN) and long short-...
Visualization, as a vibrant field for researchers, practitioners, and hi...
Convolutional Neural Networks (CNNs) have achieved great success due to ...
Edge intelligence, which is a new paradigm to accelerate artificial
inte...
We present document domain randomization (DDR), the first successful tra...
Deep neural networks (DNNs) are vulnerable to adversarial examples that ...
Deep neural networks (DNNs) are vulnerable to adversarial examples with ...
Designing feasible and effective architectures under diverse computation...
Designing effective architectures is one of the key factors behind the
s...
We present the VIS30K dataset, a collection of 29,689 images that repres...
Deep learning has brought great progress for the sequential recommendati...
In this paper, we investigate covert communication in an intelligent
ref...
Though deep neural networks perform challenging tasks excellently, they ...
Batch Normalization (BN) has been a standard component in designing deep...
Black-box nature hinders the deployment of many high-accuracy models in
...
Learning robotic grasps from visual observations is a promising yet
chal...
Model compression aims to reduce the redundancy of deep networks to obta...
This letter investigates secure transmission in an intelligent reflectin...
In this letter, we propose to exploit an intelligent reflecting surface ...
To cope with the unprecedented surge in demand for data computing for th...
We design and evaluate a novel layout fine-tuning technique for node-lin...
Non-orthogonal multiple access (NOMA) is an effective approach to improv...
On basis of functional magnetic resonance imaging (fMRI), researchers ar...
Deep neural networks have exhibited promising performance in image
super...
In the visual decoding domain, visually reconstructing presented images ...
Deep neural networks have exhibited promising performance in image
super...
Designing effective architectures is one of the key factors behind the
s...
Properly modeling the latent image distributions always plays a key role...
Recently, visual encoding based on functional magnetic resonance imaging...
Little is known about how people learn from a brief glimpse of
three-dim...
We present study results from two experiments to empirically validate th...
In image classification of deep learning, adversarial examples where inp...