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 “Vector-Quantisation”

Relaxed-Responsibility Hierarchical Discrete VAEsstat.ML

Improved hierarchical discrete VAEs for better stability and performance.

problem Training stable and efficient hierarchical discrete VAEs with numerous latent variables.

method Introducing Relaxed-Responsibility Vector-Quantisation to parameterise discrete latent variables in a hierarchical structure.

result Achieved state-of-the-art bits-per-dim results for various standard datasets.

Hierarchical VAEs Discrete Latent Variables Vector-Quantisation Relaxed-Responsibility End-to-End Training

2020-07-14abs ↗pdf ↗

Pricing and Calibration of VIX Derivatives in Mixed Bergomi Models via Quantisationq-fin.PR

Efficiently calibrates Bergomi models to VIX derivatives using vector quantization.

problem Calibrating Bergomi models to VIX derivatives for accurate pricing.

method Applied vector quantization in mixed Bergomi models for fast and efficient option pricing.

result Calibration of Bergomi models to VIX derivatives is feasible and accurate over daily data.

vector quantization Bergomi models VIX derivatives option pricing calibration

2025-06-29abs ↗pdf ↗

A Temporal Extension of Latent Dirichlet Allocation for Unsupervised Acoustic Unit Discoveryeess.AS

Improved topic modeling captures temporal relationships in speech.

problem Lack of temporal information in LDA for speech analysis.

method Temporal Markov chain extension to LDA for acoustic unit discovery.

result Improved phone segmentation results compared to base LDA.

Latent Dirichlet Allocation Speech Analysis Temporal Modeling Markov Chain Acoustic Units

2022-06-23abs ↗pdf ↗

Learning Sampling in Financial Statement Audits using Vector Quantised Autoencoder Neural Networkscs.LG

Neural networks help auditors efficiently assess financial statements by learning underlying data patterns.

problem Efficiently auditing large volumes of financial statements and journal entries.

method Vector Quantised-Variational Autoencoder (VQ-VAE) neural networks.

result VQ-VAE neural networks can learn a quantized representation of accounting data, uncovering latent factors and providing a representative audit sample.

audit sampling financial statement auditing neural networks vector quantization journal entries

2020-08-06abs ↗pdf ↗