Training machine learning (ML) models on large datasets requires considerable computing power. To speed up training, it is typical to distribute training across several machines, often with specialized hardware like GPUs or TPUs. Managing a distribut...
Three state-of-the-art statistical ranking methods for forecasting football matches are combined with several other predictors in a hybrid machine learning model. Namely an ability estimate for every team based on historic matches; an ability estimat...
The RMS Titanic was known as the unsinkable ship and was the largest, most luxurious passenger ship of its time. Sadly, the British ocean liner sank on April 15, 1912, killing over 1500 people while just 705 survived. In this article, we will analyze the Titan…
False assumptions about sex and gender are deeply embedded in the medical system, including that they are binary, static, and concordant. Machine learning researchers must understand the nature of these assumptions in order to avoid perpetuating them...
In the contemporary film industry, accurately predicting a movie's earnings is paramount for maximizing profitability. This project aims to develop a machine learning model for predicting movie earnings based on input features like the movie name, th...
This paper analyzes the relation between bank profit performance and business models. Using a machine learning-based approach, we propose a methodological strategy in which balance sheet components' contributions to profitability are the identificati...
The ALTA shared tasks have been running annually since 2010. In 2024, the purpose of the task is to detect machine-generated text in a hybrid setting where the text may contain portions of human text and portions machine-generated. In this paper, we...
Over the past decade machine learning has made significant advances in approximating density functionals, but whether this signals the end of human-designed functionals remains to be seen. Ryan Pederson, Bhupalee Kalita and Kieron Burke discuss the r...
Machine Congratulation Arch. Felino Palafox; Gusi Prize Winner 2011, worldclass-filipino.blogspot.com Archived 2014-12-25 at the Wayback Machine Architect
In the dynamic landscape of digital forensics, the integration of Artificial Intelligence (AI) and Machine Learning (ML) stands as a transformative technology, poised to amplify the efficiency and precision of digital forensics investigations. Howeve...
We propose protected pipelines or props for short, a new approach for authenticated, privacy-preserving access to deep-web data for machine learning (ML). By permitting secure use of vast sources of deep-web data, props address the systemic bottlenec...
We present OrbNet Denali, a machine learning model for electronic structure that is designed as a drop-in replacement for ground-state density functional theory (DFT) energy calculations. The model is a message-passing neural network that uses symmet...
Machine(accessed January 10, 2012) Jillian Michaels to Host All-Star Game Charity 5K & Fun Run Archived January 20, 2012, at the Wayback Machine (accessed January
Points: 1 | Comments: 0 | Author: czuriaga
Recently developed machine learning techniques, in association with the Internet of Things (IoT) allow for the implementation of a method of increasing oil production from heavy-oil wells. Steam flood injection, a widely used enhanced oil recovery te...
The Gumbel-max trick is a method to draw a sample from a categorical distribution, given by its unnormalized (log-)probabilities. Over the past years, the machine learning community has proposed several extensions of this trick to facilitate, e.g., d...
Points: 8 | Comments: 1 | Author: mrborgen
Two machine learning techniques for jet measurements at the LHCb experiment are presented: a regression-based method for jet-energy calibration and a deep neural network algorithm for jet flavour tagging, distinguishing between $b$-quark, $c$-quark,...
We provide an analysis of theory ladenness in machine learning in science, where "theory", that we call "domain theory", refers to the domain knowledge of the scientific discipline where ML is used. By constructing an account of ML models based on a...
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