On-device research index

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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4 results for “query-aware”

OneFlowSBI: One Model, Many Queries for Simulation-Based Inferencestat.ML

Unified framework for simulation-based inference learns a single model for multiple tasks.

problem Simulation-based inference for multiple tasks with limited model retraining.

method Unified flow-matching generative model with query-aware masking distribution.

result Competitive performance on various inference tasks and real-world problems.

simulation-based inference flow-matching generative model query-aware posterior sampling

2026-01-30abs ↗pdf ↗

BioNLP-OST 2019 RDoC Tasks: Multi-grain Neural Relevance Ranking Using Topics and Attention Based Query-Document-Sentence Interactionscs.LG

This paper presents our system details and results of participation in the RDoC Tasks of BioNLP-OST 2019. Research Domain Criteria (RDoC) construct is a multi-dimensional and broad framework to describe mental health disorders by combining knowledge from genomics to behaviour. Non-availability of RDoC labelled dataset …

2019-10-01abs ↗pdf ↗

DUPLE: An Intelligent Cross-Deployment Recognition Framework for Fiber-Optic Perimeter Security under Scarce Target Labelscs.LG

DUPLE tackles cross-deployment recognition in fiber-optic perimeter security with meta-learning.

problem Cross-deployment recognition challenges in fiber-optic perimeter security due to label scarcity and distribution shifts.

method DUPLE employs statistically guided meta-learning to enhance recognition robustness across unseen deployments.

result DUPLE consistently outperforms traditional and meta-learning baselines in cross-deployment DFOS benchmarks.

meta-learning cross-deployment fiber-optic perimeter security distribution shifts label scarcity

2025-11-22abs ↗pdf ↗

PSEUDo: Interactive Pattern Search in Multivariate Time Series with Locality-Sensitive Hashing and Relevance Feedbackcs.LG

PSEUDo learns patterns in multivariate time series with locality-sensitive hashing and relevance feedback.

problem Efficient pattern detection in large, multi-track sequential data with high variance and lack of ground truth.

method Query-aware locality-sensitive hashing for feature learning, sub-linear training and inference time.

result PSEUDo achieves sub-linear time efficiency for pattern modeling and comparison of 10,000 multivariate time series.

time series multivariate pattern detection locality-sensitive hashing relevance feedback

2021-04-30abs ↗pdf ↗