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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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10 results for “Pathfinding”

Pathfinder: Parallel quasi-Newton variational inferencestat.ML

Pathfinder uses quasi-Newton optimization for variational inference.

problem Approximating complex posterior distributions efficiently.

method Pathfinder combines quasi-Newton optimization with variational methods to approximate log densities.

result Pathfinder produces draws with lower KL divergence than ADVI and comparable to HMC, requiring fewer evaluations.

variational inference quasi-Newton log densities Monte Carlo Wasserstein distance

2021-08-09abs ↗pdf ↗

Deep Heuristic Learning for Real-Time Urban Pathfindingcs.AI

Paper optimizes urban navigation with deep learning models.

problem Real-time urban pathfinding challenges.

method Enhanced A* algorithm and neural network model.

result Neural network model outperforms traditional methods, reducing travel times by up to 40%.

urban pathfinding deep learning A*neural networks real-time optimization

2024-11-07abs ↗pdf ↗

Learning Shortest Paths with Generative Flow Networkscs.LG

Generative Flow Networks solve shortest path problems in graphs.

problem Finding shortest paths in graphs.

method Generative Flow Networks with flow regularization.

result Training a GFlowNet can solve pathfinding problems in arbitrary graphs.

shortest paths Generative Flow Networks flow regularization pathfinding graph theory

2026-03-02abs ↗pdf ↗

Reinforcement Learning with A* and a Deep Heuristiccs.LG

Study combines A* and DNN for better pathfinding.

problem Limited applicability of A* in domains without good heuristics.

method Trains a DNN to represent heuristic for A* and integrates it.

result Significantly better performance in driving simulation.

Reinforcement Learning A*Deep Neural Networks Pathfinding Heuristic

2018-11-19abs ↗pdf ↗

Dynamic Portfolio Rebalancing: A Hybrid new Model Using GNNs and Pathfinding for Cost Efficiencyq-fin.PM

Hybrid model uses GNNs and pathfinding to optimize portfolio rebalancing costs.

problem Optimizing transaction costs in dynamic portfolio rebalancing.

method Combines GNNs for cost prediction and Dijkstra's algorithm for pathfinding.

result Significantly reduces transaction costs in financial asset graphs.

portfolio rebalancing transaction costs Graph Neural Networks Dijkstra's algorithm financial optimization

2024-10-02abs ↗pdf ↗

Trustless Price Feeds of Cryptocurrencies: Pathfinderq-fin.CP

Paper proposes a new method to compute cryptocurrency prices securely.

problem Accurate price feeds without a third party.

method Algorithmic method to compute prices from potentially dishonest sources.

result The proposed method can report accurate prices even from dishonest sources.

cryptocurrency price feeds blockchain security price manipulation

2023-05-22abs ↗pdf ↗

Learning with Combinatorial Optimization Layers: a Probabilistic Approachstat.ML

Paper introduces probabilistic approach to CO layers in ML.

problem Challenges in training CO layers with stochastic gradient descent.

method Probabilistic perspective, approximate differentiation, structured losses.

result InferOpt.jl package for training CO layers.

combinatorial optimization machine learning probabilistic approach differentiable layers structured losses

2022-07-27abs ↗pdf ↗

Evolutionary Map of the Universe (EMU):Compact radio sources in the SCORPIO field towards the Galactic planeastro-ph.GA

ASKAP observes a region of the Galactic plane, identifying 3963 radio sources.

problem Characterizing radio sources in the Galactic plane using ASKAP observations.

method 912 MHz observations with 15 ASKAP antennas, source characterization using CAESAR.

result Differential source counts in agreement with previous surveys, spectral index estimation for sources.

ASKAP Galactic plane radio sources source characterization spectral index

2021-01-11abs ↗pdf ↗

CAESAR source finder: recent developments and testingastro-ph.IM

CAESAR source finder improves automated source extraction for ASKAP surveys.

problem Automated source extraction challenges in ASKAP surveys.

method Extended CAESAR source finder for compact and extended sources.

result Improved algorithm performances and scalability for future ASKAP surveys.

source finder radio astronomy ASKAP automated sensitivity

2019-09-13abs ↗pdf ↗

Classification of compact radio sources in the Galactic plane with supervised machine learningastro-ph.IM

Classifies compact radio sources in the Galactic plane using machine learning.

problem Challenges in processing large volumes of radio continuum survey data.

method Produced a curated dataset of ~20,000 images, trained two classifiers: gradient-boosted decision trees and convolutional neural networks.

result High classification accuracy (F1-score>90%) for separating Galactic objects from the extragalactic background.

radio astronomy machine learning classification compact sources Galactic plane

2024-02-23abs ↗pdf ↗