MGP uses a MERGE-based Markovian process from linguistic minimalism to discover and combine atomic building blocks into exact symbolic regression models, avoiding bloat when a suitable lexicon is provided.
Principal components analysis (pca).Computers & Geosciences, 19(3):303–342, 1993
4 Pith papers cite this work. Polarity classification is still indexing.
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DAD4TS trains a diffusion-based generator jointly with a forecaster under RL control and geometric projections to produce augmentation samples that boost accuracy on small-scale time-series data, with validation reported on five of six real-world datasets.
A facet-resolved adsorption energy distribution method with ML force fields identifies active and methanol-selective alloy nanocatalyst surfaces for CO2 hydrogenation.
Optimizes ImageNet-pretrained AlexNet, UMAP, and a bagging multi-cluster voting scheme with K-means, Birch and Agg for unsupervised galaxy morphology classification, reporting improved stability and consistency with galaxy evolution expectations.
citing papers explorer
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Minimalist Genetic Programming
MGP uses a MERGE-based Markovian process from linguistic minimalism to discover and combine atomic building blocks into exact symbolic regression models, avoiding bloat when a suitable lexicon is provided.
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DAD4TS: Data-Augmentation-Oriented Diffusion Model for Time-Series Forecasting with Small-Scale Data
DAD4TS trains a diffusion-based generator jointly with a forecaster under RL control and geometric projections to produce augmentation samples that boost accuracy on small-scale time-series data, with validation reported on five of six real-world datasets.
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Selectivity- and Activity-Aware Catalyst Descriptors for CO$_2$ Hydrogenation on Alloy Nanocatalysts using Machine-Learned Force Fields
A facet-resolved adsorption energy distribution method with ML force fields identifies active and methanol-selective alloy nanocatalyst surfaces for CO2 hydrogenation.
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Robustness Analysis of USmorph: II. Optimizing Feature Extraction, Dimensionality Reduction, and Clustering for Unsupervised Galaxy Morphology Classification
Optimizes ImageNet-pretrained AlexNet, UMAP, and a bagging multi-cluster voting scheme with K-means, Birch and Agg for unsupervised galaxy morphology classification, reporting improved stability and consistency with galaxy evolution expectations.