Large-scale text-to-image diffusion models have shown impressive capabil...
Graph Neural Networks (GNNs) are state-of-the-art models for performing
...
The advent of 5G New Radio (NR) technology has revolutionized the landsc...
Preference-based reinforcement learning (PbRL) promises to learn a compl...
In this paper, we present ContExtual Imitation
Learning (CEIL), a genera...
Offline reinforcement learning (RL) aims to learn an optimal policy from...
This study focuses on the topic of offline preference-based reinforcemen...
The capability of continuously learning new skills via a sequence of
pre...
Guiding robots can not only detect close-range obstacles like other guid...
Recent compositional zero-shot learning (CZSL) methods adapt pre-trained...
With technology and societal development, the 5th generation wireless
co...
Gradient-based meta-learning (GBML) algorithms are able to fast adapt to...
In 5G wireless communication, Intelligent Transportation Systems (ITS) a...
Offline reinforcement learning (RL) is a challenging setting where exist...
With wireless communication technology development, the 5G New Radio (NR...
Vision-and-Language Navigation in Continuous Environments (VLN-CE) is a
...
Many recent studies leverage the pre-trained CLIP for text-video cross-m...
Compositional zero-shot learning (CZSL) refers to recognizing unseen
com...
In this paper, we mainly focus on the problem of how to learn additional...
Offline reinforcement learning algorithms promise to be applicable in
se...
The essence of quadrupeds' movements is the movement of the center of
gr...
Due to their ability to adapt to different terrains, quadruped robots ha...
Different application scenarios will cause IMU to exhibit different erro...
Representation learning in dynamic graphs is a challenging problem becau...
Nowadays mobile communication is growing fast in the 5G communication
in...
Unsupervised reinforcement learning aims to acquire skills without prior...
We propose an approach for inverse reinforcement learning from hetero-do...
In this work, we perform semantic segmentation of multiple defect types ...
Meta Reinforcement Learning (MRL) enables an agent to learn from a limit...
While few-shot learning (FSL) aims for rapid generalization to new conce...
It is of significance for an agent to learn a widely applicable and
gene...
Domain adaptation using graph networks is to learn label-discriminative ...
Vision-and-language navigation (VLN) is a challenging task that requires...
The purpose of few-shot recognition is to recognize novel categories wit...
In recent years, the next generation of wireless communication (5G) play...
In recent years, Cellular-Vehicle-to-Everything (CV2X) has been an emerg...
In recent decades, both the industry and the academy society are sparing...