Dynamic Graph Neural Network (DGNN) has shown a strong capability of lea...
Risk-sensitive reinforcement learning (RL) has garnered significant atte...
Data preprocessing is a crucial step in the machine learning process tha...
Spiking neural networks (SNNs) offer promise for efficient and powerful
...
Federated learning (FL) enables collaborative model training among
distr...
Optical Music Recognition (OMR) is an important technology in music and ...
Relational tables, where each row corresponds to an entity and each colu...
Large language models (LLMs) provide a promising tool that enable robots...
Injecting external knowledge can improve the performance of pre-trained
...
This study introduces the Tempotron, a powerful classifier based on a
th...
Bayesian optimization (BO) is a powerful tool for seeking the global opt...
Detailed 3D reconstruction and photo-realistic relighting of digital hum...
Recall one time when we were in an unfamiliar mall. We might mistakenly ...
The existing resource allocation policy for application instances in
Kub...
Contrastive learning demonstrates great promise for representation learn...
Federated Learning has become a widely-used framework which allows learn...
Gradient-based first-order adaptive optimization methods such as the Ada...
The diverse relationships among real-world events, including coreference...
Small targets are often submerged in cluttered backgrounds of infrared
i...
We propose GeoGCN, a novel geometric dual-domain graph convolution netwo...
Even though the large-scale language models have achieved excellent
perf...
Investigating better ways to reuse the released pre-trained language mod...
Loop closure is an important component of Simultaneous Localization and
...
How to build and use dialogue data efficiently, and how to deploy models...
Reliable navigation systems have a wide range of applications in robotic...
Snow is one of the toughest adverse weather conditions for object detect...
While low-rank matrix prior has been exploited in dynamic MR image
recon...
Several recent studies attempt to address the biological implausibility ...
Existing reference-free metrics have obvious limitations for evaluating
...
Current pre-trained language models (PLM) are typically trained with sta...
Light field disparity estimation is an essential task in computer vision...
Pre-trained language models (PLMs) cannot well recall rich factual knowl...
For binary neural networks (BNNs) to become the mainstream on-device com...
In the paper, we introduce an unconstrained analysis model based on the
...
As many fine-tuned pre-trained language models (PLMs) with promising
per...
Despite decades of efforts, robot navigation in a real scenario with
vol...
Our brain consists of biological neurons encoding information through
ac...
Prompt tuning (PT) is a promising parameter-efficient method to utilize
...
Federated learning has attracted much research attention due to its priv...
How can pre-trained language models (PLMs) learn universal representatio...
Backdoor attacks, which maliciously control a well-trained model's outpu...
The class imbalance problem, as an important issue in learning node
repr...
Transformer-based pre-trained language models can achieve superior
perfo...
Knowledge distillation (KD) has been proved effective for compressing
la...
In recent years, various phase field models have been developed in
varia...
Visual dialogue is a challenging task since it needs to answer a series ...
Visual dialog, which aims to hold a meaningful conversation with humans ...
This paper introduces WeChat AI's participation in WMT 2021 shared news
...
In neural circuits, recurrent connectivity plays a crucial role in netwo...
Although spiking neural networks (SNNs) take benefits from the bio-plaus...