In this paper, we present a novel diffusion model called that generates
...
Traditional Graph Neural Network (GNN), as a graph representation learni...
For safety, AI systems in health undergo thorough evaluations before
dep...
We present a neural rendering-based method called NeRO for reconstructin...
Since the number of incident energies is limited, it is difficult to dir...
In this paper, we present a new method for the multiview registration of...
This paper presents a novel grid-based NeRF called F2-NeRF (Fast-Free-Ne...
Cone Beam Computed Tomography (CBCT) is the most widely used imaging met...
Motivated by the success of Bayesian optimisation algorithms in the Eucl...
Learning effective motion features is an essential pursuit of video
repr...
This paper presents a novel federated reinforcement learning (Fed-RL)
me...
Federated learning achieves joint training of deep models by connecting
...
Lobster eye telescopes are ideal monitors to detect X-ray transients, be...
Distributed Stein Variational Gradient Descent (DSVGD) is a non-parametr...
This article proposes a model-based deep reinforcement learning (DRL) me...
Cone beam computed tomography (CBCT) has been widely used in clinical
pr...
We present a novel method, called NeuralUDF, for reconstructing surfaces...
Deep neural networks for video action recognition easily learn to utiliz...
Data-driven predictive methods which can efficiently and accurately tran...
Machine learning (ML) holds great promise for improving healthcare, but ...
This paper presents a Progressively-connected Light Field network (ProLi...
Mobile edge computing (MEC) is a promising technology for enhancing the
...
Proteins are essential component of human life and their structures are
...
Recent progress in Medical Artificial Intelligence (AI) has delivered sy...
In this paper, we present a generalizable model-free 6-DoF object pose
e...
Fairness and robustness are often considered as orthogonal dimensions wh...
We introduce ApolloRL, an open platform for research in reinforcement
le...
With the development of Internet-of-Things (IoT), we witness the explosi...
In this paper, we propose a novel local descriptor-based framework, call...
Though 3D object detection from point clouds has achieved rapid progress...
This paper presents a neural network for robust normal estimation on poi...
Federated learning (FL) serves as a data privacy-preserved machine learn...
We present a new neural representation, called Neural Ray (NeuRay), for ...
We present a novel neural surface reconstruction method, called NeuS, fo...
In this paper, we propose a class of adaptive multiresolution (also call...
We develop and rigorously evaluate a deep learning based system that can...
Weakly-supervised Temporal Action Localization (WTAL) aims to detect the...
Mobile-edge computing (MEC) enhances the capacities and features of mobi...
Transfer learning is a standard technique to improve performance on task...
Edge intelligence requires to fast access distributed data samples gener...
We introduce Point2Skeleton, an unsupervised method to learn skeletal
re...
Motion coherence is an important clue for distinguishing true correspond...
Occlusion is a long-standing problem that causes many modern tracking me...
Class imbalance is a common problem in medical diagnosis, causing a stan...
The progress of deep learning (DL), especially the recent development of...
This paper develops a high order adaptive scheme for solving nonlinear
S...
The rich mobile data and edge computing enabled wireless networks motiva...
In this paper, we propose a class of adaptive multiresolution (also call...
Federated learning (FL) is an emerging collaborative machine learning me...
Although person re-identification (ReID) has achieved significant improv...