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statistical machine learning

GeneTech 知识引擎共收录 1055 篇相关科研实体，横跨 30 个垂直领域。数据来自 OpenAlex、arXiv、Crossref、PubMed、Europe PMC 等公开学术源，全部可溯源到原文。

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代表性文献（按质量分排序，前 60 条）

• Interpolation and differentiation of alchemical degrees of freedom in machine learning interatomic potentials
  2025 · AI 驱动科学发现 · Abstract Machine learning interatomic potentials (MLIPs) have become a workhorse of modern atomistic simulations, and recently published universal MLIPs, pre-trained on large datasets, have demonstrated remarkable accura…
• Self-Consistency Error Correction for Accurate Machine Learning Potentials from Variational Monte Carlo.
  2025 · AI 驱动科学发现 · Variational Monte Carlo (VMC) can be used to train accurate machine learning interatomic potentials (MLIPs), enabling molecular dynamics (MD) simulations of complex materials on time scales and system sizes previously un…
• Is the Future of Materials Amorphous? Challenges and Opportunities in Simulations of Amorphous Materials.
  2025 · AI 驱动科学发现 · Amorphous solids form an enormous and underutilized class of materials. In order to drive the discovery of new useful amorphous materials further we need to achieve a closer convergence between computational and experime…
• Full-stack quantification of variability in predicting ion transport properties using machine-learned interatomic potentials
  2026 · AI 驱动科学发现 · Machine-learned interatomic potentials (MLIPs) have become the state-of-the-art for performing accurate, scalable molecular dynamics (MD) simulations. It is, therefore, crucial to understand and quantify the reliability …
• Zn K-edge X-ray Absorption Spectroscopy Dataset and Graph Neural Network Models for Aqueous ZnCl2 Solutions
  2026 · AI 驱动科学发现 · This dataset supports the machine learning prediction of Zn K-edge X-ray absorption spectra (XAS) from atomic structures of aqueous ZnCl₂ solutions. Atomic structures were sampled from molecular dynamics (MD) simulations…
• Zn K-edge X-ray Absorption Spectroscopy Dataset and Graph Neural Network Models for Aqueous ZnCl2 Solutions
  2026 · AI 驱动科学发现 · This dataset supports the machine learning prediction of Zn K-edge X-ray absorption spectra (XAS) from atomic structures of aqueous ZnCl₂ solutions. Atomic structures were sampled from molecular dynamics (MD) simulations…
• Constant Potential Electrochemistry with Multiscale Quantum Mechanical / Machine Learning Simulations
  2026 · AI 驱动科学发现 · Abstract Deeper theoretical understandings of electrified interfaces become important for the development of electrochemical devices. While much progress has been in computational electrochemistry, constant-potential-sim…
• Gaussian process modelling of an industrial flotation bank
  2026 · 地外矿物 · A control-oriented Gaussian process regression (GPR) model of froth flotation is developed and compared to a previously developed parametric model. The model aims to predict the behaviour of froth flotation, taking into …
• Agent Based Modeling (ABM) and AI integration for smart tourism simulations
  2025 · AI Agent 生态 · Abstract The ability to predict visitor demand at popular points of interest (POIs) and to understand tourists' visiting patterns in general is of vital importance for tourism management. We present an approach that inte…
• Coordinating monetary contributions in participatory budgeting
  2025 · AI Agent 生态 · Abstract We formalize a framework for coordinating funding and selecting projects, the costs of which are shared among agents with quasi-linear utility functions and individual budgets. Our model contains the discrete pa…
• A Deep Learning Based Posture Correction System
  2025 · AI Agent 生态 · A computerized system developed using machine learning to monitor correct posture for an exercise being performed. The posture for exercise is important for the physical benefit of it. The system for detecting exercise i…
• Evaluation of uncertainty estimations for Gaussian process regression based machine learning interatomic potentials
  2025 · AI 驱动科学发现 · Abstract Uncertainty estimations for machine learning interatomic potentials (MLIPs) are crucial for quantifying model error and identifying informative training samples in active learning (AL) strategies. In this study,…
• Constant Potential Electrochemistry with Multiscale Quantum Mechanical / Machine Learning Simulations
  2025 · AI 驱动科学发现 · A deeper theoretical understanding of electrified interfaces becomes important for the development and control of electrochemical devices. While much progress has been in computational electrochemistry, constant potentia…
• Comparison of intermediate-range order in GeO2 glass: Molecular dynamics using machine-learning interatomic potential vs reverse Monte Carlo fitting to experimental data.
  2024 · AI 驱动科学发现 · The short-range order and intermediate-range order in GeO2 glass are investigated by molecular dynamics using machine-learning interatomic potential trained on ab initio calculation data and compared with the revers…
• The importance of sampling the dynamical modes: Reevaluating benchmarks for invariant and equivariant features of machine learning potentials for simulation of free energy landscapes.
  2024 · AI 驱动科学发现 · Machine learning interatomic potentials (MLIPs) are rapidly gaining interest for molecular modeling, as they provide a balance between quantum-mechanical level descriptions of atomic interactions and reasonable computati…
• Transfer learning for accurate description of atomic transport in Al-Cu melts.
  2024 · AI 驱动科学发现 · Machine learning interatomic potentials (MLIPs) provide an optimal balance between accuracy and computational efficiency and allow studying problems that are hardly solvable by traditional methods. For metallic alloys, M…
• RODM-Alignment: An Inertial Alignment Algorithm for Sliding Window Reverse Optimization Delayed Marginalization
  2024 · AI 安全与对齐 · In the initial alignment of the strapdown inertial navigation system, it is difficult to obtain sufficient data information to make the optimized state of convergence in a short time, which cannot realize fast alignment,…
• Regional Source-type Discrimination Using Nonlinear Alignment Algorithms
  2024 · AI 安全与对齐 · The discrimination problem in seismology aims to accurately classify different underground source types based on local, regional or teleseismic observations of ground motion. Typical discriminant approaches are rooted in…
• Machine Learning in X-ray Scattering for Materials Discovery and Characterization
  2024 · AI 驱动科学发现 · X-ray diffraction (XRD) is an immediate and powerful characterization technique that provides detailed information on the lattice structure and long-range order in crystalline materials. In recent decades, the quality an…
• Combinatorics in AI: Principles & Algorithms An Extended Abstract
  2024 · AI 驱动科学发现 · Therein we describe a few cases where combinatorics appears in machine learning. The fundamental problem of learning is intimately connected to combinatorial geometry. The geometric invariant Vapnik-Chvronenkis dimension…
• Training machine learning interatomic potentials for accurate phonon properties
  2024 · AI 驱动科学发现 · Abstract One of the major challenges in the development of universal machine learning interatomic potentials is accurately reproducing phonon properties. This issue appears to arise from the limitations of available data…
• Benchmarking of machine learning interatomic potentials for reactive hydrogen dynamics at metal surfaces
  2024 · AI 驱动科学发现 · Abstract Simulations of chemical reaction probabilities in gas surface dynamics require the calculation of ensemble averages over many tens of thousands of reaction events to predict dynamical observables that can be com…
• Machine Learning-Based Crop Prediction: A Way Towards Smart Farming
  2023 · 智慧农业 · The provision of a certain percentage of domestic goods to ensure food security makes agriculture essential to the Indian economy. Since it contains essential nutrients, the soil is the most crucial component for flouris…
• Multireference Alignment for Lead-Lag Detection in Multivariate Time Series and Equity Trading
  2023 · AI 安全与对齐 · We introduce a methodology based on Multireference Alignment (MRA) for lead-lag detection in multivariate time series, and demonstrate its applicability in developing trading strategies. Specifically designed for low sig…
• Unknown Threat Detection using AI, ML and DL methods
  2026 · AI 安全与对齐 · Cyber threats in any organization is a constant concern for the IT teams. Many tools available in the market only detect known threats and many uses fixed signatures which hackers and attackers are unlikely to reuse. Any…
• Data Quality and Benchmarking Rigor in Machine Learning for Drug Discovery: A Perspective on Aqueous Solubility Modeling
  2026 · AI 驱动科学发现 · As machine learning (ML) becomes increasingly integrated into drug discovery, reliance on legacy datasets and superficial performance metrics threatens to stall genuine progress. This perspective examines common pitfalls…
• Training sets for A unified microscopic picture of cation and anion migration in MAPbI3
  2026 · AI 驱动科学发现 · The training sets of the MAPbI3 machine learning potentials used in arXiv.2605.02685 are provided. These training sets, in combination with the training parameters provided in the publication, can be used for generating …
• Training sets for A unified microscopic picture of cation and anion migration in MAPbI3
  2026 · AI 驱动科学发现 · The training sets of the MAPbI3 machine learning potentials used in arXiv.2605.02685 are provided. These training sets, in combination with the training parameters provided in the publication, can be used for generating …
• Training sets for Halide Diffusion in Mixed-Halide Perovskites and Heterojunctions
  2026 · AI 驱动科学发现 · The training sets for the machine learning potentials used in '10.1021/acs.chemmater.6c00258' are provided. These training sets, in combination with the training parameters provided in the publication, can be used for ge…
• Training sets for Halide Diffusion in Mixed-Halide Perovskites and Heterojunctions
  2026 · AI 驱动科学发现 · The training sets for the machine learning potentials used in '10.1021/acs.chemmater.6c00258' are provided. These training sets, in combination with the training parameters provided in the publication, can be used for ge…
• Potential Evapotranspiration (PET) Prediction using Machine Learning Algorithms
  2026 · AI 驱动科学发现 · The principle of Petro-Evaporative Rainfall. With the use of AI, a system may be built to predict the PIC and establish the range. Climate, humidity, wind, and sunlight are the causes of transpiration and evaporation. Us…
• A New Explainable AI Approach to Legal Judgement Prediction: Detecting Model Uncertainty and Analyzing the Alignment Between Judges and Models
  2025 · AI 安全与对齐 · In this study, a binary classification problem is studied for the predicting outcomes of Turkish Constitutional Court decisions with deep learning models. New approaches have been developed to calculate the uncertainty i…
• Improving the Interpretability of Artificial Neural Networks Using the Example of the Pricing Options Problems
  2025 · AI 安全与对齐 · Abstract In this paper, we demonstrate how to improve the interpretability of neural networks using standard problems of computational finance as an example. In our first approach, we justify that a feedforward neural ne…
• Geometric Machine Learning
  2025 · AI 驱动科学发现 · Abstract A cornerstone of machine learning is the identification and exploitation of structure in high‐dimensional data. While classical approaches assume that data lies in a high‐dimensional Euclidean space, geometric m…
• Identifying Potential Missteps of Machine Learning in Molecular Chemistry
  2025 · AI 驱动科学发现 · Machine learning-based methods are widely used today in chemical tasks, particularly in drug design. Graph Convolutional Neural Networks (GCNNs) compete with one another in predicting chemical properties, achieving error…
• Data and scripts for "Displacive quantum critical point in superconducting hydrides: The case of H3S"
  2026 · AI 驱动科学发现 · The datasets support the paper "Displacive quantum critical point in superconducting hydrides: The case of H3S" The files contain Final MACE machine learning interatomic potential used for all simulations (MACE.model) Th…
• Data and scripts for "Displacive quantum critical point in superconducting hydrides: The case of H3S"
  2026 · AI 驱动科学发现 · The datasets support the paper "Displacive quantum critical point in superconducting hydrides: The case of H3S" The files contain Final MACE machine learning interatomic potential used for all simulations (MACE.model) Th…
• A new criterion based on estimator variance for model sampling in precision agriculture
  2022 · 智慧农业 · Model sampling has proven to be an interesting approach to optimize the sampling of an agronomic variable of interest at the field level. The use of a model improves the quality of the estimates by making it possible to …
• Construction of Surface Water Pollution Prediction Model Based on Machine Learning
  2022 · AI 驱动科学发现 · Existing prediction models have low prediction accuracy for surface water pollution with many influencing factors. Taking algal bloom prediction as the entry point of surface water pollution research, LASSO-LARS algorith…
• Machine Learning and First-Principles Discovery of Ternary Superhard Materials
  2022 · AI 驱动科学发现 · Superhard compounds defined as those with a Vickers hardness H V > 40 GPa have a wide range of industrial and extreme-environment applications. However, unlike other mechanical properties such as bulk and shear moduli, t…
• Robust and scalable uncertainty estimation with conformal prediction for machine-learned interatomic potentials
  2022 · AI 驱动科学发现 · Abstract Uncertainty quantification (UQ) is important to machine learning (ML) force fields to assess the level of confidence during prediction, as ML models are not inherently physical and can therefore yield catastroph…
• Identifying competition phenotypes in synthetic biochemical circuits
  2022 · 生物计算 · Abstract Synthetic gene circuits require cellular resources, which are often limited. This leads to competition for resources by different genes, which alter a synthetic genetic circuit’s behavior. However, the manner in…
• Dimensionality Reduction Techniques for High-dimensional Data in Precision Agriculture
  2021 · 智慧农业 · High-dimensionality of data refers to a situation in which each data point lies in a high-dimensional space. A high-dimensional space is the one with a large number of coordinates (e.g., >100). When dealing with high-dim…
• From Alignment to Assignment: Frustratingly Simple Unsupervised Entity Alignment
  2021 · AI 安全与对齐 · Cross-lingual entity alignment (EA) aims to find the equivalent entities between crosslingual KGs (Knowledge Graphs), which is a crucial step for integrating KGs. Recently, many GNN-based EA methods are proposed and show…
• A Formal Account of Structuring Motor Actions With Sensory Prediction for a Naive Agent
  2020 · AI Agent 生态 · For naive robots to become truly autonomous, they need a means of developing their perceptive capabilities instead of relying on hand crafted models. The sensorimotor contingency theory asserts that such a way resides in…
• Compound-SNE: Comparative alignment of t-SNEs for multiple single-cell omics data visualisation
  2024 · AI 安全与对齐 · Abstract One of the first steps in single-cell omics data analysis is visualization, which allows researchers to see how well-separated cell-types are from each other. When visualizing multiple datasets at once, data int…
• Mechanisms of threat: How Finland and Sweden abandoned non-alignment
  2024 · AI 安全与对齐 · Finland and Sweden are two countries with long histories of military non-alignment. In 2022, after the Russian invasion of Ukraine, both countries swiftly decided to apply for membership in the North Atlantic Treaty Orga…
• Faster algorithms for the alignment of sparse correlated Erdős–Rényi random graphs
  2024 · AI 安全与对齐 · Abstract The correlated Erdős–Rényi random graph ensemble is a probability law on pairs of graphs with n vertices, parametrized by their average degree λ and their correlation coefficient s . It can be used as a benchmar…
• Portfolio Selection with Convolutional Neural Network
  2024 · AI 安全与对齐 · <p>In this work, we applied Convolutional Neural network (CNN) models to select the portfolio weights that lead to the highest realized return in one time-step ahead. In fact, given four possible portfolio optimiza…
• Convolution Neural Network Based Image Recognition
  2024 · AI 安全与对齐 · The identification of an image is one thorny task in computer vision problems. Using the python programming language and a few other machine learning algorithms and python libraries, this image recognition and feature ex…
• Neural Network Based Constitutive Modelling of Inelastic Materials
  2024 · AI 安全与对齐 · New materials that are associated with novel micro-structures are becoming available at a rapid speed. The mechanical characteristics of solid materials are studied using constitutive models. The formulation of constitut…
• Multi-Perspective Explanation of Data Bias in AI: A Case Study
  2024 · AI 驱动科学发现 · Explanations are largely lacking in some Machine Learning (ML) systems but having explanations is very helpful because they clarify events. In this case study, we use 7 bias metrics in the AI Fairness 360 (AIF360) librar…
• High Level Overview of AI and ML in Science and Engineering
  2024 · AI 驱动科学发现 · This video describes how to incorporate physics into the machine learning process. The process of machine learning is broken down into five stages: (1) formulating a problem to model, (2) collecting and curating training…
• Fast Dual-Regularized Autoencoder for Sparse Biological Data
  2024 · 生物计算 · Relationship inference from sparse data is an important task with applications ranging from product recommendation to drug discovery. A recently proposed linear model for sparse matrix completion has demonstrated surpris…
• Use of geographic information systems (GIS) in crop protection warning service
  2023 · 智慧农业 · One of the important aims of the Governmental Crop Protection Services (GCPS) in Germany is to reduce spraying intensity and to guarantee an environmentally friendly and economical crop protection strategy. ZEPP is the c…
• Anisotropy in spatial measurements of mouldboard plough draught
  2023 · 智慧农业 · Directional semi-variograms parallel and perpendicular to the direction of travel were calculated for normalized mouldboard plough draught data obtained in a clay loam soil. The perpendicular semi-variogram reached the s…
• Standardized variograms from ancillary for kriging soil data
  2023 · 智慧农业 · The size of sample required to estimate the variogram reliably is often prohibitive for soil surveys of farms. As a result there are often too few data to compute an accurate variogram, which has implications for predict…
• Bifurcation of an SIRS Model with a Modified Nonlinear Incidence Rate
  2023 · AI 驱动科学发现 · An SIRS epidemic model with a modified nonlinear incidence rate is studied, which describes that the infectivity is strong at first as the emergence of a new disease or the reemergence of an old disease, but then the psy…
• Evaluation of a functional Bayesian method to analyse time series data in precision viticulture
  2019 · 智慧农业 · In precision agriculture, most studies focus on spatial crop variability whereas temporal variability and its role in decision-making is equally important. The classical methods for temporal analysis have limitations, po…
• Interatomic Potential Model Development: Finite‐Temperature Dynamics Machine Learning
  2019 · AI 驱动科学发现 · Abstract Developing an accurate interatomic potential model is a prerequisite for achieving reliable results from classical molecular dynamics (CMD) simulations; however, most of the potentials are biased as specific sim…

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