A general guide to applying machine learning to computer architecture [PDF]
, 2018 The resurgence of machine learning since the late 1990s has been enabled by significant advances in computing performance and the growth of big data.
Arkose, Tugberk +6 more
core +1 more source
Discriminative Cooperative Networks for Detecting Phase Transitions [PDF]
, 2018 The classification of states of matter and their corresponding phase transitions is a special kind of machine-learning task, where physical data allow for the analysis of new algorithms, which have not been considered in the general computer-science ...
Liu, Ye-Hua +1 more
core +2 more sources
Big Universe, Big Data: Machine Learning and Image Analysis for Astronomy [PDF]
, 2017 Astrophysics and cosmology are rich with data. The advent of wide-area digital cameras on large aperture telescopes has led to ever more ambitious surveys of the sky.
Gieseke, Fabian +4 more
core +2 more sources
Machine Learning in Computational Surface Science and Catalysis: Case Studies on Water and Metal–Oxide Interfaces [PDF]
Frontiers in Chemistry, 2020 The goal of many computational physicists and chemists is the ability to bridge the gap between atomistic length scales of about a few multiples of an Ångström (Å), i. e., 10−10 m, and meso- or macroscopic length scales by virtue of simulations. The same applies to timescales. Machine learning techniques appear to bring this goal into reach.
Li, Xiaoke +2 more
openaire +3 more sources
Machine learning and artificial neural network accelerated computational discoveries in materials science [PDF]
WIREs Computational Molecular Science, 2019 AbstractArtificial intelligence (AI) has been referred to as the “fourth paradigm of science,” and as part of a coherent toolbox of data‐driven approaches, machine learning (ML) dramatically accelerates the computational discoveries. As the machinery for ML algorithms matures, significant advances have been made not only by the mainstream AI ...
Yang Hong +3 more
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Climbing down Charney’s ladder: machine learning and the post-Dennard era of computational climate science [PDF]
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2021 The advent of digital computing in the 1950s sparked a revolution in the science of weather and climate. Meteorology, long based on extrapolating patterns in space and time, gave way to computational methods in a decade of advances in numerical weather forecasting.
openaire +4 more sources
Data Deluge in Astrophysics: Photometric Redshifts as a Template Use Case [PDF]
, 2018 Astronomy has entered the big data era and Machine Learning based methods have found widespread use in a large variety of astronomical applications. This is demonstrated by the recent huge increase in the number of publications making use of this new ...
A. J. Connolly +33 more
core +2 more sources
Machine Learning Bell Nonlocality in Quantum Many-body Systems
, 2017 Machine learning, the core of artificial intelligence and big data science, is one of today's most rapidly growing interdisciplinary fields. Recently, its tools and techniques have been adopted to tackle intricate quantum many-body problems. In this work,
Deng, Dong-Ling
core +1 more source
A Boxology of Design Patterns for Hybrid Learning and Reasoning Systems [PDF]
, 2019 We propose a set of compositional design patterns to describe a large variety of systems that combine statistical techniques from machine learning with symbolic techniques from knowledge representation.
Teije, Annette ten, van Harmelen, Frank
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Mapping the evolution of mitochondrial complex I through structural variation
FEBS Letters, EarlyView.Respiratory complex I (CI) is crucial for bioenergetic metabolism in many prokaryotes and eukaryotes. It is composed of a conserved set of core subunits and additional accessory subunits that vary depending on the organism. Here, we categorize CI subunits from available structures to map the evolution of CI across eukaryotes. Respiratory complex I (CI)
Dong‐Woo Shin +2 more
wiley +1 more source