Machine Unlearning is an emerging field that addresses data privacy issu...
We propose Neural Gradient Learning (NGL), a deep learning approach to l...
We study illicit account detection on transaction networks of
cryptocurr...
The growing need for accurate and reliable tracking systems has driven
s...
E-commerce authoring involves creating attractive, abundant, and targete...
3D vision-language grounding (3D-VL) is an emerging field that aims to
c...
The Class Incremental Semantic Segmentation (CISS) extends the tradition...
Encrypted traffic classification is receiving widespread attention from
...
The on-orbit processing of massive satellite-native data relies on power...
Fairness-aware recommendation eliminates discrimination issues to build
...
With the prosperity of e-commerce and web applications, Recommender Syst...
Traffic prediction has been an active research topic in the domain of
sp...
Conventional imitation learning assumes access to the actions of
demonst...
Self-attention-based models have achieved remarkable progress in short-t...
Molecule discovery plays a crucial role in various scientific fields,
ad...
Computer end users have spent billions of hours completing daily tasks l...
Robust network design, which aims to guarantee network availability unde...
Machine unlearning (MU) is gaining increasing attention due to the need ...
We propose a novel method called SHS-Net for oriented normal estimation ...
Non-autoregressive models have been widely studied in the Complete
Infor...
Effectively representing medical concepts and patients is important for
...
Contact tracing has been considered as an effective measure to limit the...
Traffic forecasting is one of the most fundamental problems in transport...
Accurate and real-time traffic state prediction is of great practical
im...
Diffusion models, as a novel generative paradigm, have achieved remarkab...
Transformer models gain popularity because of their superior inference
a...
Current computer vision models, unlike the human visual system, cannot y...
Multimodal named entity recognition (MNER) and multimodal relation extra...
A vast body of experiments share the view that social norms are major fa...
We propose a new task to benchmark scene understanding of embodied agent...
We propose a novel normal estimation method called HSurf-Net, which can
...
As one of the most successful AI-powered applications, recommender syste...
Social recommendations utilize social relations to enhance the represent...
Text generative models trained via Maximum Likelihood Estimation (MLE) s...
Point cloud is a crucial representation of 3D contents, which has been w...
Recent studies have shown that deep neural networks-based recommender sy...
GAN inversion aims to invert an input image into the latent space of a
p...
Pool-based Active Learning (AL) has achieved great success in minimizing...
Deep neural networks are vulnerable to adversarial examples, even in the...
Actor-critic Reinforcement Learning (RL) algorithms have achieved impres...
Massive open online courses (MOOCs), which provide a large-scale interac...
While the deep learning-based image deraining methods have made great
pr...
Deep learning became the game changer for image retrieval soon after it ...
Class Activation Mapping (CAM) has been widely adopted to generate salie...
Stock Movement Prediction (SMP) aims at predicting listed companies' sto...
Satellite network is the first step of interstellar voyages. It can prov...
Bi-typed heterogeneous graphs are applied in many real-world scenarios.
...
Recently 3D point cloud learning has been a hot topic in computer vision...
Adaptive subgroup enrichment design is an efficient design framework tha...
Keyphrase provides accurate information of document content that is high...