Mach. Learn. Sci. Technol.最新文献_第2页

Fast derivation of Shapley based feature importances through feature extraction methods for nanoinformatics 基于纳米信息学特征提取方法的Shapley特征重要度快速推导

Mach. Learn. Sci. Technol. Pub Date : 2021-01-01 DOI: 10.1088/2632-2153/ac0167

Tommy Liu, A. Barnard

引用次数: 8

Machine learning modeling of materials with a group-subgroup structure 具有组-子-组结构的材料的机器学习建模

Mach. Learn. Sci. Technol. Pub Date : 2020-12-31 DOI: 10.1088/2632-2153/abffe9

Prakriti Kayastha, R. Ramakrishnan

引用次数: 2

Defence against adversarial attacks using classical and quantum-enhanced Boltzmann machines 使用经典和量子增强玻尔兹曼机防御对抗性攻击

Mach. Learn. Sci. Technol. Pub Date : 2020-12-21 DOI: 10.1088/2632-2153/abf834

Aidan Kehoe, P. Wittek, Yanbo Xue, Alejandro Pozas-Kerstjens

引用次数: 4

Deep Molecular Dreaming: Inverse machine learning for de-novo molecular design and interpretability with surjective representations 深度分子梦:反机器学习用于从头分子设计和满射表征的可解释性

Mach. Learn. Sci. Technol. Pub Date : 2020-12-17 DOI: 10.1088/2632-2153/ac09d6

Cynthia X Shen, M. Krenn, S. Eppel, Alán Aspuru-Guzik

{"title":"Deep Molecular Dreaming: Inverse machine learning for de-novo molecular design and interpretability with surjective representations","authors":"Cynthia X Shen, M. Krenn, S. Eppel, Alán Aspuru-Guzik","doi":"10.1088/2632-2153/ac09d6","DOIUrl":"https://doi.org/10.1088/2632-2153/ac09d6","url":null,"abstract":"Computer-based de-novo design of functional molecules is one of the most prominent challenges in cheminformatics today. As a result, generative and evolutionary inverse designs from the field of artificial intelligence have emerged at a rapid pace, with aims to optimize molecules for a particular chemical property. These models 'indirectly' explore the chemical space; by learning latent spaces, policies, distributions or by applying mutations on populations of molecules. However, the recent development of the SELFIES string representation of molecules, a surjective alternative to SMILES, have made possible other potential techniques. Based on SELFIES, we therefore propose PASITHEA, a direct gradient-based molecule optimization that applies inceptionism techniques from computer vision. PASITHEA exploits the use of gradients by directly reversing the learning process of a neural network, which is trained to predict real-valued chemical properties. Effectively, this forms an inverse regression model, which is capable of generating molecular variants optimized for a certain property. Although our results are preliminary, we observe a shift in distribution of a chosen property during inverse-training, a clear indication of PASITHEA's viability. A striking property of inceptionism is that we can directly probe the model's understanding of the chemical space it was trained on. We expect that extending PASITHEA to larger datasets, molecules and more complex properties will lead to advances in the design of new functional molecules as well as the interpretation and explanation of machine learning models.","PeriodicalId":18148,"journal":{"name":"Mach. Learn. Sci. Technol.","volume":"82 1","pages":"03"},"PeriodicalIF":0.0,"publicationDate":"2020-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74223411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 28

Natural Evolutionary Strategies for Variational Quantum Computation 变分量子计算的自然进化策略

Mach. Learn. Sci. Technol. Pub Date : 2020-11-30 DOI: 10.1088/2632-2153/ABF3AC

A. Anand, M. Degroote, Alán Aspuru-Guzik

{"title":"Natural Evolutionary Strategies for Variational Quantum Computation","authors":"A. Anand, M. Degroote, Alán Aspuru-Guzik","doi":"10.1088/2632-2153/ABF3AC","DOIUrl":"https://doi.org/10.1088/2632-2153/ABF3AC","url":null,"abstract":"Natural evolutionary strategies (NES) are a family of gradient-free black-box optimization algorithms. This study illustrates their use for the optimization of randomly-initialized parametrized quantum circuits (PQCs) in the region of vanishing gradients. We show that using the NES gradient estimator the exponential decrease in variance can be alleviated. We implement two specific approaches, the exponential and separable natural evolutionary strategies, for parameter optimization of PQCs and compare them against standard gradient descent. We apply them to two different problems of ground state energy estimation using variational quantum eigensolver (VQE) and state preparation with circuits of varying depth and length. We also introduce batch optimization for circuits with larger depth to extend the use of evolutionary strategies to a larger number of parameters. We achieve accuracy comparable to state-of-the-art optimization techniques in all the above cases with a lower number of circuit evaluations. Our empirical results indicate that one can use NES as a hybrid tool in tandem with other gradient-based methods for optimization of deep quantum circuits in regions with vanishing gradients.","PeriodicalId":18148,"journal":{"name":"Mach. Learn. Sci. Technol.","volume":"40 1","pages":"45012"},"PeriodicalIF":0.0,"publicationDate":"2020-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82655881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 30

Machine learning spatio-temporal epidemiological model to evaluate Germany-county-level COVID-19 risk 基于机器学习时空流行病学模型评估德国县级COVID-19风险

Mach. Learn. Sci. Technol. Pub Date : 2020-11-30 DOI: 10.1088/2632-2153/ac0314

Lingxiao Wang, Tian Xu, Till Hannes Stoecker, H. Stoecker, Yin Jiang, K. Zhou

{"title":"Machine learning spatio-temporal epidemiological model to evaluate Germany-county-level COVID-19 risk","authors":"Lingxiao Wang, Tian Xu, Till Hannes Stoecker, H. Stoecker, Yin Jiang, K. Zhou","doi":"10.1088/2632-2153/ac0314","DOIUrl":"https://doi.org/10.1088/2632-2153/ac0314","url":null,"abstract":"As the COVID-19 pandemic continues to ravage the world, it is of critical significance to provide a timely risk prediction of the COVID-19 in multi-level. To implement it and evaluate the public health policies, we develop a framework with machine learning assisted to extract epidemic dynamics from the infection data, in which contains a county-level spatiotemporal epidemiological model that combines a spatial Cellular Automaton (CA) with a temporal Susceptible-Undiagnosed-Infected-Removed (SUIR) model. Compared with the existing time risk prediction models, the proposed CA-SUIR model shows the multi-level risk of the county to the government and coronavirus transmission patterns under different policies. This new toolbox is first utilized to the projection of the multi-level COVID-19 prevalence over 412 Landkreis (counties) in Germany, including t-day-ahead risk forecast and the risk assessment to the travel restriction policy. As a practical illustration, we predict the situation at Christmas where the worst fatalities are 34.5 thousand, effective policies could contain it to below 21 thousand. Such intervenable evaluation system could help decide on economic restarting and public health policies making in pandemic.","PeriodicalId":18148,"journal":{"name":"Mach. Learn. Sci. Technol.","volume":"25 1","pages":"35031"},"PeriodicalIF":0.0,"publicationDate":"2020-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78326923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 16

Atom cloud detection and segmentation using a deep neural network 原子云检测和分割使用深度神经网络

Mach. Learn. Sci. Technol. Pub Date : 2020-11-20 DOI: 10.1088/2632-2153/abf5ee

L. Hofer, Milan Krstaji'c, P'eter Juh'asz, A. L. Marchant, Robert P. Smith

引用次数: 2

Spherical convolutions on molecular graphs for protein model quality assessment 用于蛋白质模型质量评价的分子图球面卷积

Mach. Learn. Sci. Technol. Pub Date : 2020-11-16 DOI: 10.1088/2632-2153/ABF856

Ilia Igashov, Nikita Pavlichenko, S. Grudinin

{"title":"Spherical convolutions on molecular graphs for protein model quality assessment","authors":"Ilia Igashov, Nikita Pavlichenko, S. Grudinin","doi":"10.1088/2632-2153/ABF856","DOIUrl":"https://doi.org/10.1088/2632-2153/ABF856","url":null,"abstract":"Processing information on 3D objects requires methods stable to rigid-body transformations, in particular rotations, of the input data. In image processing tasks, convolutional neural networks achieve this property using rotation-equivariant operations. However, contrary to images, graphs generally have irregular topology. This makes it challenging to define a rotation-equivariant convolution operation on these structures. In this work, we propose Spherical Graph Convolutional Network (S-GCN) that processes 3D models of proteins represented as molecular graphs. In a protein molecule, individual amino acids have common topological elements. This allows us to unambiguously associate each amino acid with a local coordinate system and construct rotation-equivariant spherical filters that operate on angular information between graph nodes. Within the framework of the protein model quality assessment problem, we demonstrate that the proposed spherical convolution method significantly improves the quality of model assessment compared to the standard message-passing approach. It is also comparable to state-of-the-art methods, as we demonstrate on Critical Assessment of Structure Prediction (CASP) benchmarks. The proposed technique operates only on geometric features of protein 3D models. This makes it universal and applicable to any other geometric-learning task where the graph structure allows constructing local coordinate systems.","PeriodicalId":18148,"journal":{"name":"Mach. Learn. Sci. Technol.","volume":"20 1","pages":"45005"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90650751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Quantum autoencoders with enhanced data encoding 增强数据编码的量子自动编码器

Mach. Learn. Sci. Technol. Pub Date : 2020-10-13 DOI: 10.1088/2632-2153/ac0616

Carlos Bravo-Prieto

引用次数: 30

Adaptive Partial Scanning Transmission Electron Microscopy with Reinforcement Learning 自适应部分扫描透射电子显微镜与强化学习

Mach. Learn. Sci. Technol. Pub Date : 2020-04-06 DOI: 10.1088/2632-2153/ABF5B6

Jeffrey M. Ede

引用次数: 10