Can a machine write good poetry? Any positive answer raises fundamental questions about the nature and value of art. We report a seven-month poetry workshop in which a large language model was shaped into a digital poet through iterative in-context e...
Infrastructure as code (IaC) is the process of managing and provisioning computer data center resources through machine-readable definition files, rather
The area of declarative data analytics explores the application of the declarative paradigm on data science and machine learning. It proposes declarative languages for expressing data analysis tasks and develops systems which optimize programs writte...
Variable Selection (M.Sc. thesis). University of Antwerp. John R. Koza; Martin A. Keane; James P. Rice (1993). "Performance improvement of machine learning
I (24F) am a freelance graphic designer. My laptop is basically my entire life. I saved up for 2 years to buy this specific machine because i need it for heavy rendering and detailed work. It cost me ...
We introduce gridfm-datakit-v1, a Python library for generating realistic and diverse Power Flow (PF) and Optimal Power Flow (OPF) datasets for training Machine Learning (ML) solvers. Existing datasets and libraries face three main challenges: (1) la...
Cory grew up at the foot of a sewing machine. Everyday she would watch her mother break down design ideas into pattern pieces that fit together into 3d creations. Cory embraces this method of creation to …
World Curling Tour 2006/2007 CTRS Standings - Men [1] Archived 2008-12-18 at the Wayback Machine 2006/2007 CTRS Standings - Women [2] Archived 2008-12-18
Machine learning for NeuroImaging in Python (⭐ 1367)
Source code for 'Python Machine Learning Case Studies' by Danish Haroon (⭐ 70)
Marketing Budget Optimization for Online Retailers using AI & Machine Learning. (⭐ 81)
In this paper, we present a system called Checkbochs, a machine simulator that checks rules about its guest operating system and applications at the hardware level. The properties to be checked can be implemented as `plugins' in the Checkbochs simu...
Empirical risk minimization (ERM) is a cornerstone of modern machine learning (ML), supported by advances in optimization theory that ensure efficient solutions with provable algorithmic convergence rates, which measure the speed at which optimizatio...
...
...
numerous buildings, in an act of revenge, with a modified bulldozer in Granby, Colorado, in June 2004. Heemeyer's machine was posthumously labeled by
Code repository for the paper "Posterior Urethral Valves Outcomes Prediction (PUVOP): a machine learning tool to predict clinically relevant outcomes in boys with posterior urethral valves" (⭐ 1)
Federated Learning (FL) has emerged as a promising technique for edge devices to collaboratively learn a shared prediction model, while keeping their training data on the device, thereby decoupling the ability to do machine learning from the need to...
This paper outlines the state of the art in AI. It then describes basic machine learning and knowledge processing techniques. Based on this, some possibilities and limitations of future AI developments are discussed....
In model-based optimisation (MBO) we are interested in using machine learning to design candidates that maximise some measure of reward with respect to a black box function called the (ground truth) oracle, which is expensive to compute since it invo...