Neural Information Processing Systems http://nips.cc/

We build such explanations and counterfactuals iteratively using constraint solving techniques and a heuristic based on feature-level sensitivity ranking.

One of them is WN18RR [8], which is a wordnet subset containing40,943 entities, 11 relations, and86,835 training triples. The other is FB15k-237 [21], which is a Freebase subset containing 14,541 entities, 237 relations, and272,115 training triples. Random walks onhypergraphs with edge-dependent vertex weights.

Over the course of 2025, we had the pleasure of finding out more about a whole range of AI topics from researchers around the world. Here, we highlight some of our favourite interviews from the past 12 months. We caught up with Erica Kimei to find out about her research studying gas emissions from agriculture, specifically ruminant livestock. Erica combines machine learning and remote sensing technology to monitor and forecast such emissions. We spoke to Yuki Mitsufuji, Lead Research Scientist at Sony AI, to find out more about two pieces of research that his team presented at the Conference on Neural Information Processing Systems (NeurIPS 2024).

When solving tasks in the field of natural language processing, we sometimes need dictionary tools, such as lexicons, word form dictionaries or knowledge bases. However, the availability of dictionary data is insufficient in many languages, especially in the case of low resourced languages. In this article, we introduce a new conceptual dictionary for the Slovak language as the first linguistic tool of this kind. Since Slovak language is a language with limited linguistic resources and there are currently not available any machine-readable linguistic data sources with a sufficiently large volume of data, many tasks which require automated processing of Slovak text achieve weaker results compared to other languages and are almost impossible to solve.