Transparent objects are common in daily life. However, depth sensing for...
Deep reinforcement learning (RL) excels in various control tasks, yet th...
Negative sampling is essential for implicit-feedback-based collaborative...
Recently, Neural Radiance Fields (NeRF) has exhibited significant succes...
Following a leading vehicle is a daily but challenging task because it
r...
Functional sliced inverse regression (FSIR) is one of the most popular
a...
Trajectory data collection is a common task with many applications in ou...
In this paper, we prove that functional sliced inverse regression (FSIR)...
Reinforcement Learning (RL) algorithms have shown tremendous success in
...
Human-robot collaboration (HRC) is one key component to achieving flexib...
Due to the trial-and-error nature, it is typically challenging to apply ...
Semi-supervised semantic segmentation aims to learn from a small amount ...
This paper studies real-time collaborative robot (cobot) handling, where...
Automatic 3D content creation has achieved rapid progress recently due t...
Accurately manipulating articulated objects is a challenging yet importa...
We perform a study on the generalization ability of the wide two-layer R...
In practical recommendation scenarios, users often interact with items u...
Generalizable manipulation skills, which can be composed to tackle
long-...
Despite the tremendous success of Reinforcement Learning (RL) algorithms...
Multi-agent motion planning (MAMP) is a critical challenge in applicatio...
Graph neural networks (GNNs) have received remarkable success in link
pr...
Motivated by recent progress on online linear programming (OLP), we stud...
Creation of 3D content by stylization is a promising yet challenging pro...
Human-robot interaction (HRI) is an important component to improve the
f...
A time domain electric field volume integral equation (TD-EFVIE) solver ...
Multi-view clustering has attracted much attention thanks to the capacit...
Federated learning (FL) over mobile devices has fostered numerous intrig...
The need to increase the flexibility of production lines is calling for
...
We derive optimal control policies for a Connected Automated Vehicle (CA...
For robots to be effectively deployed in novel environments and tasks, t...
Delicate industrial insertion tasks (e.g., PC board assembly) remain
cha...
In this paper, we focus on the simulation of active stereovision depth
s...
In this paper, we focus on estimating the average treatment effect (ATE)...
We introduce a novel masked graph autoencoder (MGAE) framework to perfor...
We study real-time collaborative robot (cobot) handling, where the cobot...
Traditional depth sensors generate accurate real world depth estimates t...
Orbital angular momentum (OAM) at radio frequency (RF) has attracted mor...
Orbital angular momentum (OAM) at radio-frequency provides a novel appro...
Orbital angular momentum (OAM) at radio frequency (RF) provides a novel
...
The intelligent information society, which is highly digitized, intellig...
Graph neural networks (GNNs), which learn the node representations by
re...
Collecting large-scale annotated satellite imagery datasets is essential...
Graph neural networks (GNNs) integrate deep architectures and topologica...
Scientists frequently generalize population level causal quantities such...
Projection algorithms such as t-SNE or UMAP are useful for the visualiza...
Learning individualized treatment rules (ITRs) is an important topic in
...
The problem of finding an ancestral acyclic directed mixed graph (ADMG) ...
The continuous convergence of machine learning algorithms, 5G and beyond...
Federated learning (FL) is a new paradigm for large-scale learning tasks...
Reinforcement learning (RL) has shown great promise in optimizing long-t...