The integration of Large Language Models (LLMs) into robotics has
revolu...
We investigate the problem of learning an ϵ-approximate solution for
the...
Intel processors utilize the retirement to orderly retire the micro-ops ...
Catastrophic interference is common in many network-based learning syste...
Automatic hardhat wearing detection can strengthen the safety management...
In-band full-duplex relay (FDR) has attracted much attention as an effec...
Modern automotive functions are controlled by a large number of small
co...
Clickbait, which aims to induce users with some surprising and even thri...
The identification of compound-protein interactions (CPI) plays a critic...
Offline reinforcement learning (RL) offers an appealing approach to
real...
In the modern CPU architecture, enhancements such as the Line Fill Buffe...
Recent research has focused on using large language models (LLMs) to gen...
In recent decades, due to the emerging requirements of computation
accel...
Nowadays, voice assistants help users complete tasks on the smartphone w...
Manipulation relationship detection (MRD) aims to guide the robot to gra...
Caches are used to reduce the speed differential between the CPU and mem...
For the first time, the repeated wear phenomenon of high-frequency power...
The Electrocardiogram (ECG) measures the electrical cardiac activity
gen...
We address the problem of learning linear system models from observing
m...
Reinforcement learning (RL) agents can leverage batches of previously
co...
Robust Markov Decision Processes (MDPs) are getting more attention for
l...
As digital resources become diverse in the metaverse, a DNS-like system ...
The geographically weighted regression (GWR) is an essential tool for
es...
The physics informed neural network (PINN) is a promising method for sol...
Designing safety-critical control for robotic manipulators is challengin...
When reading a story, humans can rapidly understand new fictional charac...
Modern large-scale Pre-trained Language Models (PLMs) have achieved
trem...
Location-based services (LBS) have been significantly developed and wide...
Causal inference and model interpretability research are gaining increas...
Recent years have witnessed a trend of applying context frames to boost ...
Lifelong language learning aims to stream learning NLP tasks while retai...
Machine learning assisted modeling of the inter-atomic potential energy
...
Outsourcing anomaly detection to third-parties can allow data owners to
...
Recently, the development of machine learning (ML) potentials has made i...
The ongoing COVID-19 pandemic highlights the importance of dashboards fo...
Lifelong Language Learning (LLL) aims to train a neural network to learn...
The performance of reinforcement learning (RL) agents is sensitive to th...
Simultaneous Localization and Mapping (SLAM) is one of the most essentia...
Driving trajectory representation learning is of great significance for
...
The task assignment problem is fundamental in combinatorial optimisation...
The Coronavirus disease 2019 (COVID-19) outbreak quickly spread around t...
Collision avoidance is a widely investigated topic in robotic applicatio...
Prompt-based learning (i.e., prompting) is an emerging paradigm for
expl...
In this paper we investigate the properties of representations learned b...
Federated learning is a framework for distributed optimization that plac...
PromptSource is a system for creating, sharing, and using natural langua...
High-performance computing, together with a neural network model trained...
Due to the representation limitation of the joint Q value function,
mult...
This work considers the problem of learning the Markov parameters of a l...
Multi-hop QA requires the machine to answer complex questions through fi...