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 …

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 …

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 …

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 …

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 …

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 …

This paper provides an extensive overview of the use of knowledge graphs in the context of
Explainable Machine Learning. As of late, explainable AI has become a very active field of …

Abstract Knowledge Graphs (KG) are multi-relational graphs consisting of entities as nodes
and relations among them as typed edges. Goal of the Question Answering over KG (KGQA) …

We introduce a new large-scale NLI benchmark dataset, collected via an iterative,
adversarial human-and-model-in-the-loop procedure. We show that training models on this …

Mining valuable hidden knowledge from large-scale data relies on the support of reasoning
technology. Knowledge graphs, as a new type of knowledge representation, have gained …