Results 21 to 30 of about 2,904,824 (280)

Quantum machine learning: a classical perspective [PDF]

, 2018
Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning techniques to impressive results in regression, classification, data-generation and reinforcement learning tasks.
Ben-David S, Bishop CM, Bottou L, Breuer H-P, Chiang C-F, Getoor L, Golub GH, Grötschel M, Khardon R, Lanckriet GR, Li S, Messiah A, Murphy KP, Papadimitriou CH, Rasmussen CE, Vapnik VN   +15 more
core   +2 more sources

Machine Learning Force Fields [PDF]

Chemical Reviews, 2021
In recent years, the use of Machine Learning (ML) in computational chemistry has enabled numerous advances previously out of reach due to the computational complexity of traditional electronic-structure methods. One of the most promising applications is the construction of ML-based force fields (FFs), with the aim to narrow the gap between the accuracy
Oliver T. Unke, Stefan Chmiela, Huziel E. Sauceda, Michael Gastegger, Igor Poltavsky, Kristof T. Schütt, Alexandre Tkatchenko, Klaus-Robert Müller   +7 more
openaire   +5 more sources

Industry-scale application and evaluation of deep learning for drug target prediction [PDF]

, 2019
Artificial intelligence (AI) is undergoing a revolution thanks to the breakthroughs of machine learning algorithms in computer vision, speech recognition, natural language processing and generative modelling.
Ashby, Thomas J., Böhm, Stanislav, Ceulemans, Hugo, Chen, Hongming, Chupakhin, Vladimir, Cima, Vojtěch, Engkvist, Ola, Golib-Dzib, Jose-Felipe, Greene, Nigel, Hochreiter, Sepp, Jeliazkova, Nina, Klambauer, Günter, Martinovič, Jan, Mayr, Andreas, Sturm, Noe, Van, Thanh Le, Vander Aa, Tom, Vandriessche, Yves, Wegner, Joerg   +18 more
core   +2 more sources

Machine Learned Learning Machines

, 2017
There are two common approaches for optimizing the performance of a machine: genetic algorithms and machine learning. A genetic algorithm is applied over many generations whereas machine learning works by applying feedback until the system meets a performance threshold. Though these are methods that typically operate separately, we combine evolutionary
Sheneman, Leigh, Hintze, Arend
openaire   +2 more sources

Synthetic learning machines [PDF]

BioData Mining, 2014
Using a collection of different terminal nodesize constructed random forests, each generating a synthetic feature, a synthetic random forest is defined as a kind of hyperforest, calculated using the new input synthetic features, along with the original features.Using a large collection of regression and multiclass datasets we show that synthetic random
Ishwaran, Hemant, Malley, James D
openaire   +2 more sources

Quantum-chemical insights from deep tensor neural networks

Nature Communications, 2017
Machine learning is an increasingly popular approach to analyse data and make predictions. Here the authors develop a ‘deep learning’ framework for quantitative predictions and qualitative understanding of quantum-mechanical observables of chemical ...
Kristof T. Schütt, Farhad Arbabzadah, Stefan Chmiela, Klaus R. Müller, Alexandre Tkatchenko   +4 more
doaj   +1 more source

Distributional Prototypical Methods for Reliable Explanation Space Construction

IEEE Access, 2023
As deep learning has been successfully deployed in diverse applications, there is an ever increasing need to explain its decision. To explain decisions, case-based reasoning has proved to be effective in many areas.
Hyungjun Joo, Jaemyung Kim, Hyeonggeun Han, Jungwoo Lee   +3 more
doaj   +1 more source

Power Allocation Schemes Based on Deep Learning for Distributed Antenna Systems

IEEE Access, 2020
In recent years, a lot of power allocation algorithms have been proposed to maximize spectral efficiency (SE) and energy efficiency (EE) for the distributed antenna systems (DAS).
Gongbin Qian, Zhukun Li, Chunlong He, Xingquan Li, Xue Ding   +4 more
doaj   +1 more source

Machine Learning vs Human Learning

2021 44th International Convention on Information, Communication and Electronic Technology (MIPRO), 2021
Machine Learning (ML) is a technology to make messages created by humans (text, images, speech etc.) more understandable for computers so that they could better answer humans' queries and needs when recalling this information. Here is considered the ML sub-area - Natural Language Processing (NLP) and presented examples of its methods using text ...
Henno, Jaak, Jaakkola, Hannu, Mäkelä, Jukka   +2 more
openaire   +2 more sources

Machine Learning Hidden Symmetries

Physical Review Letters, 2022
We present an automated method for finding hidden symmetries, defined as symmetries that become manifest only in a new coordinate system that must be discovered. Its core idea is to quantify asymmetry as violation of certain partial differential equations, and to numerically minimize such violation over the space of all invertible transformations ...
Ziming Liu, Max Tegmark
openaire   +3 more sources