FLOATBench is a tabular benchmark dataset with 582,120 fatigue labels from 19,404 OpenFAST simulations of three 22 MW FOWT towers, featuring alpha-shape regime partitioning and three evaluation protocols for surrogate models.
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Machine Learning 45(1), 5–32 (Oct 2001)
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representative citing papers
TSFMAudit detects pretraining contamination in time series foundation models via probe adaptation dynamics (faster loss drop, smaller backbone shift), tested on 6 models and 187 datasets against 10 LLM-derived baselines.
Four self-stigma personas identified via LPA on 1,174 Reddit users; persona-conditioned LLMs achieve targeted shifts but experts prefer generic empathy baselines.
A calibration strategy using full-Jones corrections with an in-field unpolarised calibrator and visibility-based multi-epoch alignment enables sub-arcsecond polarimetric imaging with LOFAR at metre wavelengths.
TIDAL recovers temporal phase signals from LLM-derived semantics of provisioning metadata to enable complementary CVD placement, reducing overload frequency by 79.1% on production traces.
TabPFN-MT is a multitask in-context learner for tabular data that sets a new state-of-the-art on deep multitask learning for datasets under 1000 samples while reducing inference cost from O(T) to O(1) passes.
Presents the first public synthetic spectra database for novae and demonstrates a PCA/AI framework for retrieving physical properties from limited spectral data as a proof of concept for future surveys.
MulTaBench is a new collection of 40 image-tabular and text-tabular datasets designed to test target-aware representation tuning in multimodal tabular models.
LG-CoTrain, an LLM-guided co-training method, outperforms classical semi-supervised baselines for crisis tweet classification in low-resource settings with 5-25 labeled examples per class.
An intrinsic effective sample size for manifold MCMC is defined via kernel discrepancy as the number of independent draws yielding equivalent expected squared discrepancy to the target.
EstGraph benchmark evaluates LLMs on estimating properties of very large graphs from random-walk samples that fit in context limits.
The profile maximum likelihood estimator for the location in anisotropic hyperbolic wrapped normal models is strongly consistent, asymptotically normal, and attains the Hájek-Le Cam minimax lower bound under squared geodesic loss.
SynQL synthesizes diverse, execution-ready SQL workloads by deterministically traversing foreign-key graphs to populate ASTs, yielding high topological entropy and cost-model training data with R² ≥ 0.79 on held-out sets.
A 1825 storm created a new sea connection in Denmark, producing a 27 percent population increase (elasticity 1.6 to market access) driven by fertility and occupational change toward fishing and manufacturing, with symmetric medieval declines after waterway closure.
SRA-derived photospheric activity indicators track stellar RV variability up to a factor of two better than classical proxies like logR'HK across 14 G/K stars.
Introduces autorelevance functions as lag importance measures for time series forecasting using Shapley values and a novel one-step forecast replacement for absent features.
NEB-adapted ravine ensembles for QNNs classifying concentratable entanglement outperform naive methods when local-prediction variability is high and reduce costs, with ravines persisting under depth and qubit scaling.
The paper defines five AI system categories for public administration and reports that 55% of 91 recent papers leave the system type underspecified while 31% study one type but motivate with another.
SimPhysNet achieves 96.06% accuracy classifying laser welding penetration states using self-supervised contrastive learning with a physics-informed neural network and prototypical networks on only 200 labeled images.
The paper demonstrates two ML pipelines — clustering-based artifact rejection and a CVAE-plus-regressor trained on simulations — that classify MMC pulses and extract energy spectra comparable to FIR filtering.
dille detects silent semantic faults in random forest ML pipelines with 91% precision via data-informed static analysis on Kaggle notebooks, finding 12-18% of scripts affected.
Machine learning on the largest curated alkali-activated slag dataset shows that average metal oxide dissociation energy serves as a compact, physically interpretable reactivity descriptor enabling strength prediction and low-emission design space exploration.
Path-based adaptive weighting of random forest trees via decision path patterns delivers statistically significant accuracy gains on 36 binary classification benchmarks with minimal class-recall regression.
Develops a skew-adaptive split conformal prediction method that learns local skewness via a gauge-derived conformity score and an asinh residual model while preserving marginal validity under exchangeability.
citing papers explorer
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Exploring the Transitional Parameter Space of Blazars using Gamma-ray and X-ray Population Diagnostics
Changing-look blazars occupy intermediate regions in gamma-ray and X-ray parameter spaces but lie statistically closer to flat-spectrum radio quasars than to BL Lac objects according to centroids, PCA, UMAP, and random-forest classification.
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Controllable Molecular Generative Foundation Models
CoMole combines motif-aware graph diffusion with RL policy optimization to deliver controllable molecular generation that outperforms baselines on nine targets across materials and drug benchmarks while keeping high validity.
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Proposal and study of statistical features for string similarity computation and classification
Adapts COM and RLM statistical features from visual computing to string similarity, outperforming distance-based and other measures in synthetic tests (P<0.001 in 3/4 cases) and achieving best results on a plagiarism dataset.
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Mind the Gap? A Distributional Comparison of Real and Synthetic Priors for Tabular Foundation Models
The synthetic prior for tabular foundation models covers only a narrow part of real table distributions, but this mismatch does not degrade model generalization.
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Data-Efficient Indentation Size Effect Correction in Steels Using Machine Learning and Physics-Constrained Neural Network
A bottleneck neural network trained on ~700 shallow nanoindentations from three steels estimates high-load reference hardness on a fourth steel with ~0.47 GPa RMSE, while the abstract's 0.28 GPa physics-constrained result is absent from the body.
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Knowledge-Data Dually Driven Paradigm for Accurate Landslide Susceptibility Prediction under Data-Scarce Conditions Using Geomorphic Priors and Tabular Foundation Model
A knowledge-data dual paradigm using geomorphic priors and a tabular foundation model achieves baseline-level landslide susceptibility prediction accuracy with only 30% of typical data in tested regions.
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Interpretable Quantile Regression by Optimal Decision Trees
A novel algorithm learns sets of optimal quantile regression trees to predict full conditional distributions interpretably and efficiently.
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Is the `Known' Enough? An Integrated Machine Learning Framework for Eclipsing Binary Classification and Parameter Estimation Based on Well-Characterized Systems
An ensemble ML framework achieves 90.7% morphology classification accuracy and R² values of 0.77–0.92 for key parameters on held-out test data, with external validation against OGLE and Kepler catalogs.
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The T16 Planet Hunt: 10,000 New Planet Candidates from TESS Cycle 1 and the Confirmation of a Hot Jupiter Around TIC 183374187
A transit search on TESS Cycle 1 full-frame images produced 10,091 new planet candidates down to T=16 mag, more than doubling the known TESS total, with one hot Jupiter confirmed by radial velocity.
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From Time-series Generation, Model Selection to Transfer Learning: A Comparative Review of Pixel-wise Approaches for Large-scale Crop Mapping
A comparative review with experiments identifying optimal preprocessing, models, and transfer strategies for large-scale pixel-wise crop mapping using Landsat 8 data across five sites.
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Model Internal Sleuthing: Finding Lexical Identity and Inflectional Features in Modern Language Models
Inflectional features stay linearly decodable across all layers while lexical identity weakens with depth in modern transformers.
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Fuzzy Convolution Neural Networks for Tabular Data Classification
FCNN maps tabular features to fuzzy memberships, arranges them as images, and uses CNNs to classify, reporting competitive or superior results versus DT, SVM, FNN, Bayes, and RF on six generated noisy datasets.
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Data-driven prediction of vortex-induced vibration response of marine risers subjected to three-dimensional current
Random forest regression trained on clustered 3D-current VIV experiments predicts riser response statistics and is compared against the VIVANA-FD semi-empirical tool.
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Application of Deep Learning to Jet Charge Discrimination
Graph neural network achieves AUC of 0.883 for up versus anti-up quark jet charge discrimination in controlled QCD simulations.
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Analysing drivers and interdependencies in European electricity markets using XAI
DNNs plus SHAP/SSHAP applied to 39 European bidding zones identify solar and gas as key price drivers and simulate a single-price EU market.
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A Hybrid Quantum-Classical Approach for Melt Pool Prediction in Laser Powder Bed Fusion
Hybrid quantum-classical model with quantum feature encoding and clustering outperforms classical neural networks for LPBF melt pool prediction.
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Enhancing Collective Self-Consumption through Water Storage Heater Flexibility
Simulation of water heater flexibility in a 41-household CSC community yields 70€/year per household savings with 6% higher self-consumption and 22% higher self-production; real-world deployment examines technical performance and user acceptance.
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A Comparative Analysis of Machine Learning Algorithms for Multi-Task Prediction of the Parameters of the Pectin Hydrolysis--Extraction Process
CatBoost achieved the highest average R-squared value of about 0.946 in a multi-task regression task for pectin process parameters, with raw material type identified as the most influential input feature.
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Interpretable Policy Distillation for Power Grid Topology Control
PPO policy for grid topology control is distilled into decision trees and random forests that outperform the teacher on reward and survival time with lower inference cost and high interpretability.
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AI-Guided Design and Optimization of Graphite-Based Anodes via Iterative Experimental Feedback
Iterative AI-guided optimization of graphite anodes via the Citrine Platform raised fabrication success to 100 percent, high-capacity cell fraction to 84.8 percent, and capacity retention to 97.3 percent.
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Kolmogorov--Arnold Networks as Implicit Regularizers: Noise Robustness and Interpretability for Stellar Classification
KAN noise robustness in star/galaxy/quasar classification arises from implicit C2-spline regularization rather than architecture, as weight-decay-tuned MLPs match performance on SDSS and DESI data.
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Missing Links in Public Email and Covert Networks: A Comparative Evaluation of Link Prediction, Hyperlink Prediction, and ERGM Estimation
Comparative evaluation finds LP strong for dyadic recovery while HP (esp. CHESHIRE) gains on higher-order structures in email and covert networks.
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Ti-iLSTM: A TinyDL Approach for Logic-Level Anomaly Detection in Industrial Water Treatment Systems
Ti-iLSTM optimizes LSTM for TinyDL to detect logic-layer deception anomalies in PLC-based IWTS, reporting F1=0.983 and AUC=0.998 on SWaT with validation on WADI.
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On Improving Graph Neural Networks for QSAR by Pre-training on Extended-Connectivity Fingerprints
Pre-training GNNs on ECFP prediction produces statistically significant QSAR gains on five of six Biogen benchmarks with OOD splits, but underperforms on heterogeneous datasets and complex endpoints like binding affinity.
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Generating Synthetic Malware Samples Using Generative AI
Opcode-sequence generative models produce synthetic malware data that raises minor-class classification accuracy by up to 60% and overall detection to 96%.
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Predicting Redshift in Seyfert Galaxies Using Machine Learning
Random Forest regression on combined optical plus mid-infrared colors yields NMAD of 0.0188, R-squared of 0.9561, and 0.294 percent outliers for photometric redshifts in 23,797 Seyfert II galaxies selected from SDSS and WISE.
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Implicit neural representations as a coordinate-based framework for continuous environmental field reconstruction from sparse ecological observations
Implicit neural representations enable stable, resolution-independent reconstruction of continuous environmental fields from sparse and irregular ecological data.
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Impact of Validation Strategy on Machine Learning Performance in EEG-Based Alcoholism Classification
Nested cross-validation reveals optimistic bias in standard validation for EEG alcoholism classification, with AdaBoost reaching 78.3% accuracy and most model differences not statistically significant per McNemar's test.
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Comparative Evaluation of Machine Learning Models for Predicting Donor Kidney Discard
On 4080 German deceased donors, an ensemble ML model reached MCC 0.76 for kidney discard prediction, with standardized preprocessing and feature selection proving more important than the specific algorithm chosen.
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Community-Based Early-Stage Chronic Kidney Disease Screening using Explainable Machine Learning for Low-Resource Settings
Machine learning models trained on Bangladeshi community data achieve 89-90% balanced accuracy for early CKD detection using few accessible features, outperforming traditional screening tools and generalizing across external datasets from India, UAE, and Bangladesh.
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Harvesting AlphaEarth: Benchmarking the Geospatial Foundation Model for Agricultural Downstream Tasks
AEF embeddings perform competitively with RS models for local agricultural tasks but show limited spatial transferability, time sensitivity, and interpretability.
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Finding Quasars behind the Galactic Plane. IV. Candidate Selection from Chandra with Random Forest
A Random Forest classifier on Chandra, Gaia, and CatWISE data identifies 1060 new quasar candidates behind the Galactic plane, with two spectroscopically confirmed at z~1.1-1.3.
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An aggregate learning approach for interpretable semi-supervised population prediction and disaggregation using ancillary data
An aggregate learning approach with a simple interpretable model achieves state-of-the-art or better performance on population disaggregation using ancillary data.
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Autoencoder Architectures for Athlete Performance Scoring from Wearable Telemetry
Deep autoencoders outperform PCA and VAE variants on a composite of reconstruction MSE and interpretability metrics when reducing runner wearable data to a single latent performance score.
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Modelling magnetic material properties with uncertainty-aware neural networks
Uncertainty-aware neural networks using Gaussian negative log-likelihood and dropout are applied to predict intrinsic magnetic properties and coercivity via graph neural networks in permanent magnet research.
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A Guide to Estimating Conditional Average Treatment Effects in Competing Risks Settings
Compares six meta-learners (Cox/RSF risk models paired with elastic net/RF CATE models) via simulations differing in hazard complexity and censoring, and releases the R package crsurvlearners.
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A machine-learning-assisted progressive digit-randomness screening framework for detecting non-random patterns in raw numerical research data
FDRS combines digit frequency tests, association metrics, entropy, KL divergence, and ML models to assign risk grades to numerical datasets, showing separation between normal and irregular simulated data with high AUC.
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A Proof-of-Concept Simulation-Driven Digital Twin Framework for Decision-Aware Diabetes Modeling
A simulation-driven digital twin framework is shown to generate interpretable diabetes trajectories for decision-aware analysis by combining benchmark data with controlled synthetic scenarios.
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An Explainable Unsupervised-to-Supervised Machine Learning Framework for Dietary Pattern Discovery Using UK National Dietary Survey Data
An unsupervised-to-supervised ML pipeline on UK NDNS data discovers four dietary patterns, reproduces them with macro-F1 0.963 using a surrogate classifier, and interprets them via SHAP for potential clinical use.
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A Machine Learning Framework for EEG-Based Prediction of Treatment Efficacy in Chronic Neck Pain
A preprocessing pipeline for resting-state and motor-task EEG is described to support future machine learning models that predict treatment efficacy in chronic neck pain.
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STRIKE: Additive Feature-Group-Aware Stacking Framework for Credit Default Prediction
STRIKE improves credit default prediction AUC-ROC by training independent models on feature groups and aggregating their outputs via a meta-learner, outperforming tree baselines and conventional stacking on three real datasets.
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fastml: Guarded Resampling Workflows for Safer Automated Machine Learning in R
fastml is an R package that enforces leakage-free preprocessing through guarded resampling and provides a unified interface for safer automated ML including survival analysis.
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Towards Accurate and Efficient Waste Image Classification: A Hybrid Deep Learning and Machine Learning Approach
A hybrid deep learning plus classical ML pipeline for waste image classification reaches up to 100% accuracy on TrashNet and a corrected household dataset while cutting feature dimensionality by over 95%.
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