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A comprehensive review of digital twin—part 1: modeling and twinning enabling technologies
As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented
attention because of its promise to further optimize process design, quality control, health …
attention because of its promise to further optimize process design, quality control, health …
Biological underpinnings for lifelong learning machines
Biological organisms learn from interactions with their environment throughout their lifetime.
For artificial systems to successfully act and adapt in the real world, it is desirable to similarly …
For artificial systems to successfully act and adapt in the real world, it is desirable to similarly …
Unifying large language models and knowledge graphs: A roadmap
Large language models (LLMs), such as ChatGPT and GPT4, are making new waves in the
field of natural language processing and artificial intelligence, due to their emergent ability …
field of natural language processing and artificial intelligence, due to their emergent ability …
A definition of continual reinforcement learning
In a standard view of the reinforcement learning problem, an agent's goal is to efficiently
identify a policy that maximizes long-term reward. However, this perspective is based on a …
identify a policy that maximizes long-term reward. However, this perspective is based on a …
Intelligent computing: the latest advances, challenges, and future
Computing is a critical driving force in the development of human civilization. In recent years,
we have witnessed the emergence of intelligent computing, a new computing paradigm that …
we have witnessed the emergence of intelligent computing, a new computing paradigm that …
Deep learning for change detection in remote sensing: a review
ABSTRACT A large number of publications have incorporated deep learning in the process
of remote sensing change detection. In these Deep Learning Change Detection (DLCD) …
of remote sensing change detection. In these Deep Learning Change Detection (DLCD) …
Symbolic knowledge distillation: from general language models to commonsense models
The common practice for training commonsense models has gone from-human-to-corpus-to-
machine: humans author commonsense knowledge graphs in order to train commonsense …
machine: humans author commonsense knowledge graphs in order to train commonsense …
Artificial intelligence and machine learning
Within the last decade, the application of “artificial intelligence” and “machine learning” has
become popular across multiple disciplines, especially in information systems. The two …
become popular across multiple disciplines, especially in information systems. The two …
A comprehensive study of knowledge editing for large language models
Large Language Models (LLMs) have shown extraordinary capabilities in understanding
and generating text that closely mirrors human communication. However, a primary …
and generating text that closely mirrors human communication. However, a primary …
Dynabench: Rethinking benchmarking in NLP
We introduce Dynabench, an open-source platform for dynamic dataset creation and model
benchmarking. Dynabench runs in a web browser and supports human-and-model-in-the …
benchmarking. Dynabench runs in a web browser and supports human-and-model-in-the …