The neural rendering of humans is a topic of great research significance...
Current learning-based edge caching schemes usually suffer from dynamic
...
Evolutionary reinforcement learning (ERL) algorithms recently raise atte...
Goal-conditioned reinforcement learning (RL) is an interesting extension...
Due to the high flexibility and remarkable performance, low-rank
approxi...
One of the main challenges in modern recommendation systems is how to
ef...
In cooperative multi-agent reinforcement learning (MARL), the environmen...
Although deep learning (DL) shows powerful potential in cell segmentatio...
Neural Architecture Search (NAS) has shown promising performance in the
...
Most existing image restoration methods use neural networks to learn str...
Mixed-precision quantization (MPQ) suffers from time-consuming policy se...
Color image denoising is frequently encountered in various image process...
Multi-agent reinforcement learning (MARL) recently has achieved tremendo...
We study the problem of online multi-task learning where the tasks are
p...
Recently, the compression and deployment of powerful deep neural network...
While reinforcement learning (RL) algorithms are achieving state-of-the-...
Malware continues to evolve rapidly, and more than 450,000 new samples a...
With the advancement of deep learning techniques, major cloud providers ...
Conventional model quantization methods use a fixed quantization scheme ...
Bayesian policy reuse (BPR) is a general policy transfer framework for
s...
Android allows apps to communicate with its system services via system
s...
The exponentially large discrete search space in mixed-precision quantiz...
Temporal Sentence Grounding in Videos (TSGV), which aims to ground a nat...
Multi-agent settings remain a fundamental challenge in the reinforcement...
In this work, we present a fully self-supervised framework for semantic
...
Data centers are carbon-intensive enterprises due to their massive energ...
Temporal sentence grounding in videos(TSGV), which aims to localize one
...
While artificial intelligence (AI) is widely applied in various areas, i...
We study multi-task reinforcement learning (RL) in tabular episodic Mark...
Delivering malware covertly and evasively is critical to advanced malwar...
Models trained with offline data often suffer from continual distributio...
Dynamic analysis based on the full-system emulator QEMU is widely used f...
It is of paramount importance to achieve efficient data collection in th...
In this paper, we investigate the recent studies on multimedia edge
comp...
For real-world deployments, it is critical to allow robots to navigate i...
Accurate assessment of students' ability is the key task of a test.
Asse...
With the progress in AI-based facial forgery (i.e., deepfake), people ar...
The boom of DL technology leads to massive DL models built and shared, w...
We introduce HEBO: Heteroscedastic Evolutionary Bayesian Optimisation th...
With the outbreak of COVID-19, how to mitigate and suppress its spread i...
We present the extension of the Tinker-HP package (Lagardère et al., Che...
The popularity of Bitcoin benefits a lot from its anonymity. However, th...
In many real-world applications, multiple agents seek to learn how to pe...
Since its debut, SGX has been used in many applications, e.g., secure da...
The cloud infrastructure must provide security for High-Performance Comp...
In order for High-Performance Computing (HPC) applications with data sec...
Evolution strategies (ES), as a family of black-box optimization algorit...
Botnet is one of the major threats to computer security. In previous bot...
In this paper, we develop asymptotic theories for a class of latent vari...
Response process data collected from human-computer interactive items co...