Lexical non-learning hashes match near-duplicates well, while BGE-based quantized embeddings better preserve rewritten scientific similarity, under a shared ranking protocol on CSFCube and RELISH.
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3 Pith papers cite this work, alongside 31 external citations. Polarity classification is still indexing.
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Pith papers citing it
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2026 3representative citing papers
Adding loop composition to branching quantum walk models produces a variable-time quantum search algorithm whose complexity matches the best known results.
Introduces L_ht-SVM using a hybrid truncated loss for robust sparse single-view classification with an ADMM solver, plus a multi-view extension MvL_ht-SVM.
citing papers explorer
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H3D: Benchmarking Unsupervised Text Hashing for Fine-Grained Document Deduplication
Lexical non-learning hashes match near-duplicates well, while BGE-based quantized embeddings better preserve rewritten scientific similarity, under a shared ranking protocol on CSFCube and RELISH.
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Loop Composition in Quantum Algorithms
Adding loop composition to branching quantum walk models produces a variable-time quantum search algorithm whose complexity matches the best known results.
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Robust and sparse support vector machine via hybrid truncated loss for supervised classification
Introduces L_ht-SVM using a hybrid truncated loss for robust sparse single-view classification with an ADMM solver, plus a multi-view extension MvL_ht-SVM.