The discovery process of building new theoretical physics models involves the dual aspect of both fitting to the existing experimental data and satisfying abstract theorists’ criteria like beauty, naturalness, etc. We design loss functions for performing …
Read more »Building Machine Learning apps with GitLab AI-Powered DevSecOps PlatformLink to the demo project: https://go.gitlab.com/PxkqvwCheck out this demo to learn ho…
Read more »A keynote talk from Code BEAM Europe 2023✨Follow as and don’t miss any info about Code BEAM conferences: https://twitter.com/CodeBEAMio ✨Christopher Grainger…
Read more »Machine Learning (ML) is increasingly used to implement advanced applications with non-deterministic behavior, which operate on the cloud-edge continuum. The pervasive adoption of ML is urgently calling for assurance solutions assessing applications non-f…
Read more »Livestream powered by baiosphere – the bavarian ai networkHow could machines learn as efficiently as humans and animals? How could machines learn to reason a…
Read more »Geometric Deep Learning Models for Vehicle Routing Problem – GitHub – isotlaboratory/ml4vrp: Geometric Deep Learning Models for Vehicle Routing Problem…
Read more »Machine Learning Engineering Guides and Tools. Contribute to stas00/ml-engineering development by creating an account on GitHub.
Read more »Discover A Hands-On Introduction to Machine Learning, 1st Edition, Chirag Shah, HB ISBN: 9781009123303 on Higher Education from Cambridge…
Read more »Compute, data, and algorithmic advances are the three fundamental factors that guide the progress of modern Machine Learning (ML). In this paper we study trends in the most readily quantified factor – compute. We show that before 2010 training compute gre…
Read more »We survey the nascent literature on machine learning in the study of financial markets. We highlight the best examples of what this line of research has to offe…
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