Diagrammatic Teaching is a paradigm for robots to acquire novel skills,
...
Robots should exist anywhere humans do: indoors, outdoors, and even unma...
Federated training of Graph Neural Networks (GNN) has become popular in
...
Learning for Demonstration (LfD) enables robots to acquire new skills by...
Version incompatibility issues are rampant when reusing or reproducing d...
Recognizing software entities such as library names from free-form text ...
Over the last decade, Q A platforms have played a crucial role in how
...
The dynamic set cover problem has been subject to extensive research sin...
In this study, we introduce PharmacyGPT, a novel framework to assess the...
The success of Computer Vision (CV) relies heavily on manually annotated...
Language models retain a significant amount of world knowledge from thei...
Large Language Models (LLMs) have been demonstrated effective for code
g...
Software developers often resort to Stack Overflow (SO) to fill their
pr...
Anomaly detection aims to distinguish abnormal instances that deviate
si...
Relational databases play an important role in this Big Data era. Howeve...
In this paper, we study the problem of computing an edge-coloring in the...
Recent accidents involving self-driving cars call for extensive testing
...
Generative search engines directly generate responses to user queries, a...
Cyber-Physical Systems (CPS) have been widely deployed in safety-critica...
Underwater imagery often exhibits distorted coloration as a result of
li...
Given an undirected unweighted graph G = (V, E) on n vertices and m
edge...
We present neural frailty machine (NFM), a powerful and flexible neural
...
We consider a cost sharing problem on a weighted undirected graph, where...
Machine Learning (ML) has been widely used in Natural Language Processin...
Recurrent Neural Networks (RNNs) have been widely used in Natural Langua...
Schema induction builds a graph representation explaining how events unf...
Studies have shown that large pretrained language models exhibit biases
...
The application of graph representation learning techniques to the area ...
We present two new classes of algorithms for efficient field integration...
Large language models (LLMs) have shown promise for automatic summarizat...
Natural language interaction is a promising direction for democratizing ...
Sampling diverse programs from a code language model and reranking with ...
Language models (LMs) are becoming the foundation for almost all major
l...
This paper introduces the shared task of summarizing documents in severa...
When developing autonomous driving systems (ADS), developers often need ...
The rapid on-site evaluation (ROSE) technique can signifi-cantly acceler...
Recent years have witnessed a great success of supervised deep learning,...
Training foundation models, such as GPT-3 and PaLM, can be extremely
exp...
While pretrained language models (PLMs) have greatly improved text
gener...
In this paper, we show that the time complexity of monotone min-plus pro...
The reconfigurable intelligent surface (RIS) technology is a promising
e...
We consider a cost sharing problem to connect all nodes in a weighted
un...
Pancreatic cancer is one of the most malignant cancers in the world, whi...
Thanks to the availability of large bandwidth and high-gain directional
...
A key assumption in supervised learning is that training and test data f...
Federated machine learning is a versatile and flexible tool to utilize
d...
Gomory-Hu tree [Gomory and Hu, 1961] is a succinct representation of pai...
Cyber-physical systems (CPS) have been broadly deployed in safety-critic...
Robot localization remains a challenging task in GPS denied environments...
Autonomous driving shows great potential to reform modern transportation...