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.

168,657 papers · 148 categories

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4 results for “dead-zone”

CoDeQ: End-to-End Joint Model Compression with Dead-Zone Quantizer for High-Sparsity and Low-Precision Networkscs.LG

CoDeQ simplifies joint model compression by integrating pruning and quantization.

problem Joint pruning and quantization methods are complex and require additional procedures.

method CoDeQ uses a dead-zone quantizer to directly induce sparsity and learn quantization parameters.

result CoDeQ achieves high sparsity and low-precision accuracy with minimal bit operations.

model compression joint pruning quantization dead-zone sparsity

2025-12-15abs ↗pdf ↗

Bias-Corrected Adaptive Conformal Inference for Multi-Horizon Time Series Forecastingcs.LG

BC-ACI corrects time series forecast bias, improving prediction intervals.

problem Persistent bias in time series forecasts leads to overly conservative prediction intervals.

method Augments ACI with an EWM estimate of forecast bias to correct nonconformity scores and re-center intervals.

result Reduces Winkler interval scores by 13-17% under distribution shifts, improving calibration.

time series forecasting bias correction conformal inference distribution shift prediction intervals

2026-04-14abs ↗pdf ↗

Temporal Kolmogorov-Arnold Networks (T-KAN) for High-Frequency Limit Order Book Forecasting: Efficiency, Interpretability, and Alpha Decaycs.LG

T-KAN improves HFT LOB forecasting with learnable splines.

problem Alpha decay in HFT LOB forecasting models.

method T-KAN uses learnable B-spline activation functions to model market signals.

result 19.1% relative improvement in F1-score at k = 100 horizon.

HFT LOB Alpha decay T-KAN B-spline

2026-01-05abs ↗pdf ↗

Group Orthogonalized Policy Optimization:Group Policy Optimization as Orthogonal Projection in Hilbert Spacecs.LG

GOPO optimizes large models in Hilbert space, avoiding Kullback-Leibler's curvature.

problem Optimizing large language models with Kullback-Leibler divergence's curvature issues.

method GOPO uses Hilbert space L2(pi_k) with orthogonality constraints and a work-dissipation functional.

result GOPO achieves competitive generalization with stable gradient dynamics and entropy preservation.

Group Policy Optimization Hilbert Space Orthogonality Work-Dissipation Functional Large Language Models

2026-02-24abs ↗pdf ↗