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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.

169,051 papers · 148 categories

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4 results for “temperature-based”

Learning Goal-Oriented Visual Dialog via Tempered Policy Gradientcs.LG

New method improves dialogue agents focusing on simple utterances.

problem Dialogue agents often focus on simple utterances and suboptimal policies.

method Tempered Policy Gradient (TPG) methods to improve dialogue performance.

result Significant improvements in dialogue performance, especially in producing convincing utterances.

dialogue agents reinforcement learning policy gradient temperature-based convincing utterances

2018-07-02abs ↗pdf ↗

Learning to Scale Logits for Temperature-Conditional GFlowNetscs.LG

Logit-GFN accelerates GFlowNets training by scaling logits based on temperature.

problem Training temperature-conditional GFlowNets is numerically challenging.

method Logit-GFN uses a learned function of temperature to scale policy logits.

result Logit-GFN greatly accelerates GFlowNets training and improves generalization and mode discovery.

GFlowNets temperature logits scaling exploration

2023-10-04abs ↗pdf ↗

FLUXtrapolation: A benchmark on extrapolating ecosystem fluxescs.LG

FLUXtrapolation benchmarks machine learning for extrapolating ecosystem fluxes under distribution shifts.

problem Machine learning challenges in extrapolating ecosystem fluxes under distribution shifts.

method Defined temporal, spatial, and temperature-based extrapolation scenarios; evaluated performance across domains, temporal aggregations, and tail errors.

result Baselines perform similarly under median hourly RMSE but differ under tail-focused and multi-scale evaluations.

machine learning ecosystem fluxes distribution shift upscaling temporal aggregation

2026-05-19abs ↗pdf ↗

Can a CNN trained on the Ising model detect the phase transition of the

q

-state Potts model?cond-mat.dis-nn

CNN detects phase transitions in Potts models without prior knowledge.

problem Detecting phase transitions in

q

-state Potts models using deep learning.

method Trained a deep CNN on Ising model spin configurations and temperatures, then tested on Potts model images.

result Deep CNN accurately detects phase transitions in Potts models, including high- and low-temperature regions.

deep learning phase transitions Potts model Ising model neural networks

2021-04-08abs ↗pdf ↗