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Nature communications 2023年第1期14卷 4116页

作者： Adil Kabylda Valentin Vassilev-Galindo Stefan Chmiela Igor Poltavsky Alexandre Tkatchenko Department of Physics and Materials Science University of Luxembourg L-1511 Luxembourg City Luxembourg. Machine Learning Group Technische Universität Berlin 10587 Berlin Germany. BIFOLD - Berlin Institute for the Foundations of Learning and Data 10587 Berlin Germany. Department of Physics and Materials Science University of Luxembourg L-1511 Luxembourg City Luxembourg. alexandre.tkatchenko@uni.lu.

Toward a 'Standard Model' of machine learning

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arXiv 2021年

作者： Hu, Zhiting Xing, Eric P. Halıcıoğlu Data Science Institute University of California San Diego San Diego United States Machine Learning Department Carnegie Mellon University Pittsburgh United States Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates Petuum Inc. Pittsburgh United States

machine learning (ML) is about computational methods that enable machines to learn concepts from experience. In handling a wide variety of experience ranging from data instances, knowledge, constraints, to rewards, adversaries, and lifelong interaction in an ever-growing spectrum of tasks, contemporary ML/AI (artificial intelligence) research has resulted in a multitude of learning paradigms and methodologies. Despite the continual progresses on all different fronts, the disparate narrowly focused methods also make standardized, composable, and reusable development of ML approaches difficult, and preclude the opportunity to build AI agents that panoramically learn from all types of experience. This article presents a standardized ML formalism, in particular a 'standard equation' of the learning objective, that offers a unifying understanding of many important ML algorithms in the supervised, unsupervised, knowledge-constrained, reinforcement, adversarial, and online learning paradigms, respectively-those diverse algorithms are encompassed as special cases due to different choices of modeling components. The framework also provides guidance for mechanical design of new ML approaches and serves as a promising vehicle toward panoramic machine learning with all experience. © 2021, CC BY.

关键词： learning algorithms

From quantum alchemy to Hammett's equation: Covalent bonding from atomic energy partitioning

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arXiv 2022年

作者： Sahre, Michael J. von Rudorff, Guido Falk von Lilienfeld, O. Anatole University of Vienna Faculty of Physics Kolingasse 14-16 Vienna1090 Austria Währinger Str. 42 Vienna1090 Austria University Kassel Department of Chemistry Heinrich-Plett-Str.40 Kassel34132 Germany Vector Institute for Artificial Intelligence TorontoONM5S 1M1 Canada Departments of Chemistry Materials Science and Engineering and Physics University of Toronto St. George Campus TorontoON Canada Machine Learning Group Technische Universität Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany

We present an intuitive and general analytical approximation estimating the energy of covalent single and double bonds between participating atoms in terms of their respective nuclear charges with just three parameters, [EAB ≈ a − bZAZB + c(ZA7/3 + ZB7/3)]. The functional form of our expression models an alchemical atomic energy decomposition between participating atoms A and B. After calibration, reasonably accurate bond energy estimates are obtained for hydrogen-saturated diatomics composed of p-block elements coming from the same row 2 ≤ n ≤ 4 in the periodic table. Corresponding changes in bond energies due to substitution of atom B by C can be obtained via simple formulas. While being of different functional form and origin, our model is as simple and accurate as Pauling’s well-known electronegativity model. Analysis indicates that the model’s response in covalent bonding to variation in nuclear charge is near-linear—which is consistent with Hammett’s equation. © 2022, CC BY.

关键词： Atoms

Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep learning Models: A Review

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arXiv 2022年

作者： Jafari, Mahboobeh Shoeibi, Afshin Khodatars, Marjane Ghassemi, Navid Moridian, Parisa Delfan, Niloufar Alizadehsani, Roohallah Khosravi, Abbas Ling, Sai Ho Zhang, Yu-Dong Wang, Shui-Hua Gorriz, Juan M. Rokny, Hamid Alinejad Acharya, U. Rajendra Internship in BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia Data Science and Computational Intelligence Institute University of Granada Spain Department of Medical Engineering Mashhad Branch Islamic Azad University Mashhad Iran Faculty of Computer Engineering Dept. of Artificial Intelligence Engineering K. N. Toosi University of Technology Tehran Iran Deakin University VIC3217 Australia Australia School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Department of Psychiatry University of Cambridge United Kingdom BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia UNSW Data Science Hub The University of New South Wales SydneyNSW2052 Australia Research Centre Macquarie University Sydney2109 Australia Ngee Ann Polytechnic Singapore599489 Singapore Dept. of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan Dept. of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore

In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of mortality globally. CVDs appear with minor symptoms and progressively get worse. The majority of people experience symptoms such as exhaustion, shortness of breath, ankle swelling, fluid retention, and other symptoms when starting CVD. Coronary artery disease (CAD), arrhythmia, cardiomyopathy, congenital heart defect (CHD), mitral regurgitation, and angina are the most common CVDs. Clinical methods such as blood tests, electrocardiography (ECG) signals, and medical imaging are the most effective methods used for the detection of CVDs. Among the diagnostic methods, cardiac magnetic resonance imaging (CMR) is increasingly used to diagnose, monitor the disease, plan treatment and predict CVDs. Coupled with all the advantages of CMR data, CVDs diagnosis is challenging for physicians due to many slices of data, low contrast, etc. To address these issues, deep learning (DL) techniques have been employed to the diagnosis of CVDs using CMR data, and much research is currently being conducted in this field. This review provides an overview of the studies performed in CVDs detection using CMR images and DL techniques. The introduction section examined CVDs types, diagnostic methods, and the most important medical imaging techniques. In the following, investigations to detect CVDs using CMR images and the most significant DL methods are presented. Another section discussed the challenges in diagnosing CVDs from CMR data. Next, the discussion section discusses the results of this review, and future work in CVDs diagnosis from CMR images and DL techniques are outlined. The most important findings of this study are presented in the conclusion section. © 2022, CC BY-NC-ND.

关键词： Electrocardiography

Reducing Training data Needs with Minimal Multilevel machine learning (M3L)

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arXiv 2023年

作者： Heinen, Stefan Khan, Danish von Rudorff, Guido Falk Karandashev, Konstantin Arrieta, Daniel Jose Arismendi Price, Alastair J.A. Nandi, Surajit Bhowmik, Arghya Hermansson, Kersti Anatole von Lilienfeld, O. Vector Institute for Artificial Intelligence TorontoONM5S 1M1 Canada Department of Chemistry University of Toronto St. George Campus TorontoON Canada University Kassel Department of Chemistry Heinrich-Plett-Str.40 Kassel34132 Germany Heinrich-Plett-Straße 40 Kassel34132 Germany University of Vienna Faculty of Physics Kolingasse 14-16 WienAT-1090 Austria Department of Chemistry-Ångström Laboratory Uppsala University Box 538 UppsalaSE-75121 Sweden Acceleration Consortium University of Toronto 80 St George St TorontoONM5S 3H6 Canada Departments of Chemistry University of Toronto St. George Campus TorontoON Canada Department of Energy Conversion and Storage DTU Anker Engelunds Vej Kgs. LyngbyDK-2800 Denmark Department of Materials Science and Engineering University of Toronto St. George campus TorontoON Canada Department of Physics University of Toronto St. George campus TorontoON Canada Machine Learning Group Technische Universität Berlin Berlin Institute for the Foundations of Learning and Data Berlin Germany

For many machine learning applications in science, data acquisition, not training, is the bottleneck even when avoiding experiments and relying on computation and simulation. Correspondingly, and in order to reduce cost and carbon footprint, training data efficiency is key. We introduce minimal multilevel machine learning (M3L) which optimizes training data set sizes using a loss function at multiple levels of reference data in order to minimize a combination of prediction error with overall training data acquisition costs (as measured by computational wall-times). Numerical evidence has been obtained for calculated atomization energies and electron affinities of thousands of organic molecules at various levels of theory including HF, MP2, DLPNO-CCSD(T), DFHFCABS, PNOMP2F12, and PNOCCSD(T)F12, and treating tens with basis sets TZ, cc-pVTZ, and AVTZF12. Our M3L benchmarks for reaching chemical accuracy in distinct chemical compound sub-spaces indicate substantial computational cost reductions by factors of ∼ 1.01, 1.1, 3.8, 13.8 and 25.8 when compared to heuristic sub-optimal multilevel machine learning (M2L) for the data sets QM7b, QM9LCCSD(T), EGP, QM9CCSD(T)AE , and QM9CCSD(T)EA , respectively. Furthermore, we use M2L to investigate the performance for 76 density functionals when used within multilevel learning and building on the following levels drawn from the hierarchy of Jacobs Ladder: LDA, GGA, mGGA, and hybrid functionals. Within M2L and the molecules considered, mGGAs do not provide any noticeable advantage over GGAs. Among the functionals considered and in combination with LDA, the three on average top performing GGA and Hybrid levels for atomization energies on QM9 using M3L correspond respectively to PW91, KT2, B97D, and τ-HCTH, B3LYP∗(VWN5), TPSSH. © 2023, CC0.

关键词： Carbon footprint

Deepregression: A Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression

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arXiv 2021年

作者： Rügamer, David Kolb, Chris Fritz, Cornelius Pfisterer, Florian Kopper, Philipp Bischl, Bernd Shen, Ruolin Bukas, Christina de Andrade e Sousa, Lisa Barros Thalmeier, Dominik Baumann, Philipp F.M. Kook, Lucas Klein, Nadja Müller, Christian L. Helmholtz AI Munich Germany ETH Zurich Switzerland University of Zurich Switzerland Humboldt-Universität zu Berlin Germany LMU Munich ICB Helmholtz Zentrum Munich CCM Flatiron Institute New York United States Statistical Learning & Data Science Department of Statistics LMU Munich Munich80539 Germany

In this paper we describe the implementation of semi-structured deep distributional regression, a flexible framework to learn conditional distributions based on the combination of additive regression models and deep networks. Our implementation encompasses (1) a modular neural network building system based on the deep learning library TensorFlow for the fusion of various statistical and deep learning approaches, (2) an orthogonalization cell to allow for an interpretable combination of different subnetworks, as well as (3) pre-processing steps necessary to set up such models. The software package allows to define models in a user-friendly manner via a formula interface that is inspired by classical statistical model frameworks such as mgcv. The packages' modular design and functionality provides a unique resource for both scalable estimation of complex statistical models and the combination of approaches from deep learning and statistics. This allows for state-of-the-art predictive performance while simultaneously retaining the indispensable interpretability of classical statistical models. Copyright © 2021, The Authors. All rights reserved.

关键词： Regression analysis

Encrypted machine learning of molecular quantum properties

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arXiv 2022年

作者： Weinreich, Jan von Rudorff, Guido Falk von Lilienfeld, O. Anatole University of Vienna Faculty of Physics Kolingasse 14-16 WienAT-1090 Austria University of Vienna Vienna Doctoral School in Physics Boltzmanngasse 5 Vienna1090 Austria University Kassel Department of Chemistry Heinrich-Plett-Str.40 Kassel34132 Germany Vector Institute for Artificial Intelligence TorontoONM5S 1M1 Canada Departments of Chemistry Materials Science and Engineering and Physics University of Toronto St. George Campus TorontoON Canada Machine Learning Group Technische Universität Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany

Large machine learning models with improved predictions have become widely available in the chemical sciences. Unfortunately, these models do not protect the privacy necessary within commercial settings, prohibiting the use of potentially extremely valuable data by others. Encrypting the prediction process can solve this problem by double-blind model evaluation and prohibits the extraction of training or query data. Unfortunately, contemporary ML models based on fully homomorphic encryption or federated learning are either too expensive for practical use or have to trade higher speed for weaker security. We have implemented secure and computationally feasible encrypted machine learning models using oblivious transfer enabling and secure predictions of molecular quantum properties across chemical compound space. However, we find that encrypted predictions using kernel ridge regression models are a million times more expensive than without encryption. This demonstrates a dire need for a compact machine learning model architecture, including molecular representation and kernel matrix size, that minimizes model evaluation costs. © 2022, CC BY.

关键词： Forecasting

Attention-based fully gated CNN-BGRU for Russian handwritten text

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arXiv 2020年

作者： Abdallah, Abdelrahman Hamada, Mohamed Nurseitov, Daniyar Machine Learning & Data Science Satbayev University Almaty Kazakhstan Is Department International IT University Almaty Kazakhstan National Open Research Laboratory For Information and Space Technologies Satbayev University Almaty Kazakhstan

This research approaches the task of handwritten text with attention encoder-decoder networks that are trained on Kazakh and Russian language. We developed a novel deep neural network model based on Fully Gated CNN, supported by Multiple bidirectional GRU and Attention mechanisms to manipulate sophisticated features that achieve 0.045 Character Error Rate (CER), 0.192 Word Error Rate (WER) and 0.253 Sequence Error Rate (SER) for the first test dataset and 0.064 CER, 0.24 WER and 0.361 SER for the second test dataset. Also, we propose fully gated layers by taking the advantage of multiple the output feature from Tahn and input feature, this proposed work achieves better results and We experimented with our model on the Handwritten Kazakh & Russian database (HKR). Our research is the first work on the HKR dataset and demonstrates state-of-the-art results to most of the other existing models. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

Set-valued prediction in multi-class classification 31

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Set-valued prediction in multi-class classification

31st Benelux Conference on Artificial Intelligence and the 28th Belgian Dutch Conference on machine learning, BNAIC/BENELEARN 2019

作者： Mortier, Thomas Wydmuch, Marek Dembczýnski, Krzysztof Hüllermeier, Eyke Waegeman, Willem Department of Data Analysis and Mathematical Modelling Ghent University Belgium Institute of Computing Science Poznán University of Technology Poland Intelligent Systems and Machine Learning Universität Paderborn Germany

In cases of uncertainty, a multi-class classifier preferably returns a set of candidate classes instead of predicting a single class label with little guarantee. More precisely, the classifier should strive for an optimal balance between the correctness (the true class is among the candidates) and the precision (the candidates are not too many) of its prediction. We formalize this problem within a general decision-theoretic framework that unifies most of the existing work in this area. In this framework, uncertainty is quantified in terms of conditional class probabilities, and the quality of a predicted set is measured in terms of a utility function. We then address the problem of finding the Bayes-optimal prediction, i.e., the subset of class labels with highest expected utility. © 2019 for this paper by its authors.

关键词： Forecasting

MOLTR: Multiple object localisation, tracking, and reconstruction from monocular RGB videos

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arXiv 2020年

作者： Li, Kejie Rezatofighi, Hamid Reid, Ian School of Computer Science Australian Institute for Machine Learning University of Adelaide Australia Australian Centre for Robotic Vision Australia Department of Data Science and AI Faculty of Information Technology Monash University ClaytonVIC Australia

Semantic aware reconstruction is more advantageous than geometric-only reconstruction for future robotic and AR/VR applications because it represents not only where things are, but also what things are. Object-centric mapping is a task to build an object-level reconstruction where objects are separate and meaningful entities that convey both geometry and semantic information. In this paper, we present MOLTR, a solution to object-centric mapping using only monocular image sequences and camera poses. It is able to localise, track and reconstruct multiple rigid objects in an online fashion when an RGB camera captures a video of the surrounding. Given a new RGB frame, MOLTR firstly applies a monocular 3D detector to localise objects of interest and extract their shape codes representing the object shape in a learnt embedding. Detections are then merged to existing objects in the map after data association. Motion state (i.e. kinematics and the motion status) of each object is tracked by a multiple model Bayesian filter and object shape is progressively refined by fusing multiple shape code. We evaluate localisation, tracking and reconstruction on benchmarking datasets for indoor and outdoor scenes, and show superior performance over previous approaches. Copyright © 2020, The Authors. All rights reserved.

关键词： Mapping