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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5 results for “oats”

Predicting Mycotoxin Contamination in Irish Oats Using Deep and Transfer Learningcs.LG

Study uses deep learning to predict mycotoxin levels in Irish oats.

problem Predicting mycotoxin contamination in Irish oats to improve crop quality and safety.

method Investigated neural networks and transfer learning models for multi-response prediction.

result Transfer learning model TabPFN provided the best performance.

mushroom toxins deep learning oats mycotoxin prediction transfer learning

2025-12-23abs ↗pdf ↗

Profit and loss attribution: An empirical studyq-fin.PM

Study shows OAT decomposition generates unexplained profit and loss, while SU decompositions depend on risk factor order.

problem Understanding profit and loss attribution in financial markets.

method Used financial market data from 2003 to 2022 to compare OAT, SU, and ASU decompositions.

result SU decompositions are sensitive to risk factor order and cannot identify all relevant risk factors.

profit and loss attribution Solvency 2 OAT decomposition SU decomposition ASU decomposition

2023-09-14abs ↗pdf ↗

Efficient Reinforcement Learning in Probabilistic Reward Machinesstat.ML

Efficient RL in PRMs with improved regret bound.

problem Reinforcement learning in probabilistic reward machines with non-Markovian rewards.

method Design of an algorithm with a new regret bound of

\widetilde{O}(\sqrt{HOAT} + H^2O^2A^{3/2} + H\sqrt{T})

result Improved regret bound over existing methods, matching lower bound up to a logarithmic factor.

Reinforcement Learning Probabilistic Reward Machines Non-Markovian Rewards Regret Bound Simulation Lemma

2024-08-19abs ↗pdf ↗

The Temporal Markov Transition Fieldcs.LG

TMTF improves time series visualization by separating dynamic regimes.

problem Misleading global transition matrix in time series analysis.

method Temporal chunking, local transition matrices, and image assembly.

result Temporal segmentation reveals distinct transition dynamics.

time series Markov transition field temporal chunking convolutional neural networks regime change

2026-03-09abs ↗pdf ↗

Deep Learning Forecasting of the U.S. Aggregate Bond Indexq-fin.PM

Study forecasts U.S. bond index using deep learning, finding persistence is key.

problem Forecasting U.S. aggregate bond index with deep learning methods.

method Constructed a stationary but maximally persistent representation of the bond index, evaluated using MLPs and CNNs.

result Deep learning models outperform traditional methods in short-horizon forecasting of bond indices.

Deep Learning Bond Index Forecasting Persistence Stationarity

2026-05-27abs ↗pdf ↗