Towards Machine Learning Induction
Induction lies at the heart of mathematics and computer science. However, automated theorem proving of inductive problems is still limited in its power. In this abstract, we first summarize our progress in automating inductive theorem proving for Isabelle/HOL. Then, we present MeLoId, our approach to suggesting promising applications of induction without completing a proof search.
Code (0)
등록된 구현이 없습니다.
Tasks
Automated Theorem ProvingBIG-bench Machine LearningSimilar Papers 제목 키워드 기반
Physics-Informed Induction Machine Modelling
This rapid communication devises a Neural Induction Machine (NeuIM) model, which pilots the use of physics-informed machine learning to enable AI-based electromagnetic transient simulations. The contributions are threefo…
Physics-informed machine learningUltimate Intelligence Part I: Physical Completeness and Objectivity of Induction
We propose that Solomonoff induction is complete in the physical sense via several strong physical arguments. We also argue that Solomonoff induction is fully applicable to quantum mechanics. We show how to choose an obj…
Fault Diagnosis on Induction Motor using Machine Learning and Signal Processing
The detection and identification of induction motor faults using machine learning and signal processing is a valuable approach to avoiding plant disturbances and shutdowns in the context of Industry 4.0. In this work, we…
Fault DetectionFault DiagnosisImproving Stability of Low-Inertia Systems using Virtual Induction Machine Synchronization for Grid-Following Converters
This paper presents a novel strategy for the synchronization of grid-following Voltage Source Converters (VSCs) in power systems with low rotational inertia. The proposed synchronization unit is based on emulating the ph…
On the Limitations of Unsupervised Bilingual Dictionary Induction
Unsupervised machine translation---i.e., not assuming any cross-lingual supervision signal, whether a dictionary, translations, or comparable corpora---seems impossible, but nevertheless, Lample et al. (2018) recently pr…
Graph SimilarityMachine TranslationTranslationUnsupervised Machine Translation