Machine Learning-based Intelligent Formal Reasoning and Proving System

Learning Intelligent Theorem Proving from Large Formal Corpora

The talk will discuss several AI methods used to learn proving of conjectures over large formal mathematical corpora. This includes (i) machine-learning methods that learn from previous proofs how to suggest the most relevant lemmas for proving the next conjectures, (ii) methods that guide low-level proof-search algorithms based on previous proof traces, and (iii) methods that automatically inv...

Intelligent storage system with machine learning

Storage system is the key component in big data. As data size grows, we need to scale storage system efficiently. Majority of today’s storage devices present a simple linear block address space to the operating system. This simplistic addressing scheme treats each block address as a generic bucket of opaque data. While this architecture allows the operating system to easily abstract the stored ...

Introduction to Machine Learning & Case-Based Reasoning

Prologue These notes refer to the course of Machine Learning (course 395), Computing Department, Imperial College London. The goal of this syllabus is to summarize the basics of machine learning and to provide a detailed explanation of case-based reasoning. This chapter introduces the term " machine learning " and defines what do we mean while using this term. This is a very short summary of th...

An Intelligent Machine Learning-Based Protection of AC Microgrids Using Dynamic Mode Decomposition

An intelligent strategy for the protection of AC microgrids is presented in this paper. This method was halving to an initial signal processing step and a machine learning-based forecasting step. The initial stage investigates currents and voltages with a window-based approach based on the dynamic decomposition method (DDM) and then involves the norms of the signals to the resultant DDM data. T...

Machine learning and automated theorem proving