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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

Trend · papers per month

count% of corpus
0111 · Nov 201819922001200920172026
4 results for “subject-invariant”
Disentangled Adversarial Autoencoder for Subject-Invariant Physiological Feature Extractioncs.LG

Proposes a framework for extracting consistent physiological features across users.

problem Variability of biosignals across different users and tasks.
method Adversarial feature extractor for disentangled universal representations.
result Up to 8.8% improvement in average accuracy of classification.
biosignal processingtransfer learningdisentangled representationsadversarial networkssubject-invariant features
2020-08-26abs ↗pdf ↗
Mutual Information-driven Subject-invariant and Class-relevant Deep Representation Learning in BCIcs.LG

Proposes a new framework for EEG-based BCIs without adversarial learning.

problem High intra- and inter-subject variabilities in EEG data.
method Mutual information-driven deep learning approach to learn class-relevant and subject-invariant feature representations.
result Effective in learning class-relevant and subject-invariant feature representations without adversarial learning.
mutual informationdeep learningBCIdomain adaptationsubject-invariant
2019-10-17abs ↗pdf ↗