Recently, Multi-Scenario Learning (MSL) is widely used in recommendation...
Security concerns about a machine learning model used in a
prediction-as...
Secure aggregation is widely used in horizontal Federated Learning (FL),...
Most existing compound facial expression recognition (FER) methods rely ...
Due to the pivotal role of Recommender Systems (RS) in guiding customers...
As an active network security protection scheme, intrusion detection sys...
It is becoming increasingly common to utilize pre-trained models provide...
Extracting expressive visual features is crucial for accurate
Click-Thro...
With the increasing adoption of NLP models in real-world products, it be...
Over the past few years, deep convolutional neural network-based methods...
Recent studies show that the state-of-the-art deep neural networks are
v...
Human emotions involve basic and compound facial expressions. However,
c...
Machine learning (ML) models need to be frequently retrained on changing...
We propose a novel secure aggregation scheme based on seed-homomorphic
p...
We propose a minimax formulation for removing backdoors from a given poi...
Active learning (AL) aims at reducing labeling effort by identifying the...
Active learning has been a main solution for reducing data labeling cost...
Online gaming is a multi-billion-dollar industry, which is growing faste...
Model inversion (MI) attacks in the whitebox setting are aimed at
recons...
Recommendation Systems (RS) have become an essential part of many online...
Person re-identification (ReID) under occlusions is a challenging proble...
In recent years, deep learning based visual tracking methods have obtain...
Recently, deep learning based facial expression recognition (FER) method...
The bat algorithm (BA) has been shown to be effective to solve a wider r...
Recently, facial attribute classification (FAC) has attracted significan...
Deep Neural Network (DNN) has recently achieved outstanding performance ...
This paper presents a novel quadratic projection based feature extractio...
In this paper, we study a discriminatively trained deep convolutional ne...