The ubiquitous use of face recognition has sparked increasing privacy
co...
Large-scale language models (LLMs) have demonstrated outstanding perform...
Post-training quantization (PTQ) is a popular method for compressing dee...
Test-time adaptation (TTA) has shown to be effective at tackling distrib...
Data explosion and an increase in model size drive the remarkable advanc...
Face recognition technology has been used in many fields due to its high...
Knowledge distillation (KD) is a widely-used technique that utilizes lar...
The probability prediction of multivariate time series is a notoriously
...
Test-time adaptation (TTA) seeks to tackle potential distribution shifts...
Conventional deep models predict a test sample with a single forward
pro...
AI-aided drug discovery (AIDD) is gaining increasing popularity due to i...
In real-world applications, data often come in a growing manner, where t...
Valuation problems, such as attribution-based feature interpretation, da...
Accurate protein structure prediction from amino-acid sequences is criti...
Proteins structure prediction has long been a grand challenge over the p...
Face recognition has been extensively studied in computer vision and
art...
To satisfy the stringent requirements on computational resources in the ...
Neural architecture search (NAS) has gained increasing attention in the
...
Model compression has been widely adopted to obtain light-weighted deep
...
One primary focus in multimodal feature extraction is to find the
repres...
Large-scale distributed optimization is of great importance in various
a...
Recently, convolutional neural networks (CNN) have demonstrated impressi...