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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

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0111 · Feb 201719922001200920172026
4 results for “MCML”
A Study of the Learnability of Relational Properties: Model Counting Meets Machine Learning (MCML)cs.LG

MCML uses ML to study learnability of Alloy properties, showing simple models can perform well but fail on full input space.

problem Empirical study of learnability of relational properties in Alloy.
method MCML combines ML with model counting to evaluate performance on bounded input spaces.
result Simple ML models can achieve high accuracy and F1-score on training/test datasets but fail on full input space, highlighting complexity of learning relational properties.
MCMLmodel countingmachine learningAlloyrelational properties
2019-12-25abs ↗pdf ↗
EmbraceNet for Activity: A Deep Multimodal Fusion Architecture for Activity Recognitioneess.SP

EmbraceNet fusion model for multi-sensor activity recognition.

problem Human activity recognition using multiple sensors is challenging.
method Independent processing of each sensor, feature fusion with EmbraceNet, post-processing, and additional processes.
result Improved performance in SHL recognition challenge.
activity recognitiondeep learningmultimodal fusionEmbraceNetsensor data
2020-04-29abs ↗pdf ↗
Supervised Dimensionality Reduction and Visualization using Centroid-encodercs.LG

Centroid-Encoder reduces high-dimensional data for better visualization.

problem Visualizing high-dimensional data efficiently and accurately.
method Centroid-Encoder integrates label information to keep similar objects close in reduced space.
result Centroid-Encoder outperforms other techniques in visualizing high-dimensional data.
Centroid-EncoderDimensionality ReductionVisualizationAutoencoderLabel Information
2020-02-27abs ↗pdf ↗
Exemplar-Centered Supervised Shallow Parametric Data Embeddingcs.LG

Metric learning methods for dimensionality reduction in combination with k-Nearest Neighbors (kNN) have been extensively deployed in many classification, data embedding, and information retrieval applications. However, most of these approaches involve pairwise training data comparisons, and thus have quadratic computat…

2017-02-21abs ↗pdf ↗