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

Trend · papers per month

count % of corpus

48 results for “Fast dynamics”

Nonlinear model reduction for slow-fast stochastic systems near unknown invariant manifoldsstat.ML

Efficiently simulates slow dynamics of high-dimensional stochastic systems.

problem Simulating high-dimensional stochastic systems with slow dynamics and fast modes.

method Designs an algorithm to estimate an invariant manifold and its dynamics, averaging out fast modes.

result Efficient simulator of effective dynamics on low-dimensional invariant manifold.

stochastic systems invariant manifold slow-fast dynamics model reduction efficient simulation

2021-04-05abs ↗pdf ↗

Variational Temporal IRT: Fast, Accurate, and Explainable Inference of Dynamic Learner Proficiencycs.LG

VTIRT speeds up IRT inference for dynamic learner proficiency.

problem Expensive and slow inference algorithms for dynamic IRT models.

method Variational Temporal IRT (VTIRT) for fast, accurate inference.

result Orders of magnitude speedup in inference runtime with accurate results.

IRT dynamic learning learner proficiency fast inference modular design

2023-11-14abs ↗pdf ↗

Learning effective dynamics from data-driven stochastic systemsstat.ML

This work learns effective dynamics from short-term data of stochastic systems.

problem Learning effective dynamics from short-term data of stochastic systems.

method Proposes a novel algorithm using a neural network (Auto-SDE) to learn invariant slow manifold from data.

result Validated through numerical experiments to be accurate, stable, and effective.

stochastic systems dynamics learning Auto-SDE neural networks slow-fast systems

2022-05-09abs ↗pdf ↗

Evolution of worldwide stock markets, correlation structure and correlation based graphsq-fin.ST

We investigate the daily correlation present among market indices of stock exchanges located all over the world in the time period Jan 1996 - Jul 2009. We discover that the correlation among market indices presents both a fast and a slow dynamics. The slow dynamics reflects the development and consolidation of globaliz…

2011-03-29abs ↗pdf ↗

A projected nonlinear state-space model for forecasting time series signalsstat.ME

The paper introduces a fast algorithm for learning and forecasting nonlinear dynamics from noisy time series data.

problem Challenges in capturing nonlinear dynamics from noisy time series data.

method A projected nonlinear state-space model with kernel functions applied to projected lines.

result The model effectively learns and forecasts complex nonlinear dynamics with computational efficiency.

time series forecasting nonlinear dynamics kernel functions state-space model stochastic time series

2023-11-22abs ↗pdf ↗

Learning stochastic multiscale models through normalizing flowsstat.ML

A framework learns multiscale dynamics from single trajectories using normalizing flows.

problem Learning effective stochastic dynamics from single observed paths of slow variables.

method Data-driven approach based on coupled multiscale SDEs, stochastic averaging, and normalizing flows for density modeling.

result Scalable approach to capturing epistemic uncertainty in multiscale systems.

multiscale dynamics normalizing flows stochastic averaging invariant distribution epistemic uncertainty

2026-05-10abs ↗pdf ↗

Fast spectral separation method for kinetic equation with anisotropic non-stationary collision operator retaining micro-model fidelitymath.NA

A fast method learns plasma collision kernels from simulations, improving kinetic models.

problem Improving kinetic models for plasma dynamics beyond the weakly coupled regime.

method Data-driven collisional operator, fast spectral separation method.

result Accurately captures plasma dynamics in moderately coupled regime.

plasma dynamics kinetic models collisional operators fast spectral methods anisotropic kernels

2025-10-16abs ↗pdf ↗

Evolution of correlation structure of industrial indices of US equity marketsq-fin.ST

We investigate the dynamics of correlations present between pairs of industry indices of US stocks traded in US markets by studying correlation based networks and spectral properties of the correlation matrix. The study is performed by using 49 industry index time series computed by K. French and E. Fama during the tim…

2013-06-20abs ↗pdf ↗

Role-Dynamics: Fast Mining of Large Dynamic Networkscs.SI

To understand the structural dynamics of a large-scale social, biological or technological network, it may be useful to discover behavioral roles representing the main connectivity patterns present over time. In this paper, we propose a scalable non-parametric approach to automatically learn the structural dynamics of …

2012-03-09abs ↗pdf ↗

A note on the theory of fast money flow dynamicsq-fin.TR

The gauge theory of arbitrage was introduced by Ilinski in [arXiv:hep-th/9710148] and applied to fast money flows in [arXiv:cond-mat/9902044]. The theory of fast money flow dynamics attempts to model the evolution of currency exchange rates and stock prices on short, e.g.\ intra-day, time scales. It has been used to ex…

2010-06-14abs ↗pdf ↗

Reinforcement Learning with Fast Stabilization in Linear Dynamical Systemscs.LG

Improved RL algorithm stabilizes unknown linear systems with polynomial regret.

problem Learning and stabilizing unknown linear dynamical systems.

method Proposes an algorithm with an improved exploration strategy for fast stabilization.

result Achieves

ilde{\mathcal{O}}(\sqrt{T})

regret after

T

time steps.

Reinforcement Learning Linear Dynamical Systems Stabilization Regret Exploration

2020-07-23abs ↗pdf ↗

Scale-Invariant Fast Convergence in Gamescs.GT

New learning dynamics achieve fast convergence in games without needing to know utility scales.

problem Fast convergence guarantees in learning games require prior knowledge of utility scales.

method Developed scale-free and scale-invariant learning dynamics using optimistic follow-the-regularized-leader with adaptive learning rates and clipping techniques.

result Achieved fast convergence rates to Nash and correlated equilibria without prior utility scale knowledge.

game theory fast convergence scale-invariance optimistic follow-the-regularized-leader clipping techniques

2026-02-12abs ↗pdf ↗

Fast Approximate Spectral Clustering for Dynamic Networksstat.ML

Spectral clustering is a widely studied problem, yet its complexity is prohibitive for dynamic graphs of even modest size. We claim that it is possible to reuse information of past cluster assignments to expedite computation. Our approach builds on a recent idea of sidestepping the main bottleneck of spectral clusterin…

2017-06-12abs ↗pdf ↗

Ensemble learning for portfolio valuation and risk managementq-fin.CP

Ensemble method for fast portfolio valuation and risk management.

problem Dynamic portfolio valuation and risk management from cash flow data.

method Regression trees for dynamic value process learning.

result Fast and accurate estimator with closed-form solution.

ensemble learning portfolio valuation risk management regression trees dynamic value process

2022-04-12abs ↗pdf ↗

Two-Time-Scale Learning Dynamics: A Population View of Neural Network Trainingcs.LG

Aims to describe neural network training dynamics using two-time-scale models.

problem Lack of a general mathematical description of neural network training.

method Introduces a theoretical framework based on two-time-scale population dynamics.

result Derives selection-mutation equations and effective fitness for hyperparameters.

neural networks training dynamics two-time-scale population-based replicator-mutator

2026-03-20abs ↗pdf ↗

Forward variable selection enables fast and accurate dynamic system identification with Karhunen-Loève decomposed Gaussian processescs.LG

A new method selects variables efficiently for fast and accurate dynamic system identification.

problem Efficiently selecting variables for scalable Gaussian processes.

method Forward variable selection using Karhunen-Loève decomposition and Gibbs sampling.

result Method yields competitive accuracies and inference times for dynamic systems.

variable selection Gaussian processes Karhunen-Loève dynamic systems Bayesian smoothing

2022-05-26abs ↗pdf ↗

Understanding Approximate Fisher Information for Fast Convergence of Natural Gradient Descent in Wide Neural Networksstat.ML

This study explains why approximate NGD works well in wide neural networks.

problem Understanding why NGD with approximate Fisher information converges fast in wide neural networks.

method Analyzing asymptotic training dynamics in function space via the neural tangent kernel.

result NGD with approximate Fisher information achieves the same fast convergence as exact NGD under specific conditions.

Natural Gradient Descent Wide Neural Networks Fisher Information Convergence Asymptotic Dynamics

2020-10-02abs ↗pdf ↗

Community detection in sparse time-evolving graphs with a dynamical Bethe-Hessiancs.SI

A fast spectral algorithm detects community structure in evolving graphs.

problem Detecting community structure in time-evolving sparse graphs.

method Extension of the Bethe-Hessian matrix for spectral community detection.

result The algorithm reaches the optimal detectability threshold and outperforms other methods.

community detection sparse graphs time-evolving spectral algorithms Bethe-Hessian

2020-06-03abs ↗pdf ↗

Learning Nonlinear Projections for Reduced-Order Modeling of Dynamical Systems using Constrained Autoencodersmath.DS

New method learns nonlinear projections for reduced-order modeling of complex dynamical systems.

problem Modeling transient dynamics near a manifold in nonlinear systems.

method Constrained autoencoder neural networks with invertible activation functions and biorthogonal weight matrices.

result Demonstrated effectiveness on a vortex shedding model, learning oblique fibers for fast dynamics.

reduced-order modeling nonlinear projections dynamical systems constrained autoencoders vortex shedding

2023-07-28abs ↗pdf ↗

A Note on Kernel Methods for Multiscale Systems with Critical Transitionsnlin.PS

We study the maximum mean discrepancy (MMD) in the context of critical transitions modelled by fast-slow stochastic dynamical systems. We establish a new link between the dynamical theory of critical transitions with the statistical aspects of the MMD. In particular, we show that a formal approximation of the MMD near …

2018-04-25abs ↗pdf ↗

SoftMatcha 2: A Fast and Soft Pattern Matcher for Trillion-Scale Corporacs.CL

Ultra-fast search algorithm for trillion-scale corpora with semantic flexibility.

problem Efficiently searching over large natural language corpora with semantic variations.

method String matching based on suffix arrays, vector representation of words, dynamic corpus-aware pruning, fast exact lookup.

result Substantially lower search latency compared to existing methods on FineWeb-Edu corpus.

fast search trillion-scale corpora semantic flexibility suffix arrays vector representation

2026-02-11abs ↗pdf ↗

Generalizing to New Physical Systems via Context-Informed Dynamics Modelcs.LG

CoDA adapts dynamics models to new physical systems by conditioning on context.

problem Generalizing to new physical systems with shared dynamics but different contexts.

method Context-informed dynamics adaptation (CoDA) using multiple environments and a hypernetwork.

result State-of-the-art generalization results on nonlinear dynamics.

generalization dynamics modeling contextual adaptation data-driven physical systems

2022-02-01abs ↗pdf ↗

Characterizing Dependence of Samples along the Langevin Dynamics and Algorithms via Contraction of

Φ

-Mutual Informationmath.ST

The paper studies how quickly samples from Langevin dynamics become independent.

problem Understanding the dependence between samples along Langevin dynamics and related algorithms.

method Measures dependence via

Φ

-mutual information and proves strong data processing inequalities.

result The

Φ

-mutual information between samples decreases exponentially to zero.

Langevin dynamics Markov chains independence mutual information log-concave distributions

2024-02-26abs ↗pdf ↗

Continuous-Time Homeostatic Dynamics for Reentrant Inference Modelsmath.DS

FHRN uses continuous-time dynamics to stabilize reentrant neural computation.

problem Stabilizing reentrant neural computation.

method Formulated as a continuous-time neural-ODE system, revealing norm-regulated reentry.

result Achieves stable oscillatory trajectories through population-level gain modulation.

neural networks reentrant dynamics homeostasis continuous-time stability

2025-12-04abs ↗pdf ↗

Physics-Informed Graph Neural Network for Dynamic Reconfiguration of Power Systemscs.LG

GraPhyR uses GNNs to optimize power grid reconfiguration in real-time.

problem Optimizing power grid reconfiguration for reliability and efficiency.

method Physics-informed Graph Neural Network (GNN) framework.

result GraPhyR learns to optimize DyR tasks efficiently.

Power Grid Graph Neural Networks Dynamic Reconfiguration Optimization Reliability

2023-10-01abs ↗pdf ↗

LD-EnSF: Synergizing Latent Dynamics with Ensemble Score Filters for Fast Data Assimilation with Sparse Observationsstat.ML

LD-EnSF speeds up data assimilation with sparse observations.

problem Efficiently assimilate sparse and noisy data into complex dynamical systems.

method LD-EnSF uses latent dynamics networks and history-aware LSTM encoders to process sparse observations without full-space simulations.

result Achieves significant speedups over existing methods while maintaining high accuracy.

data assimilation score-based methods latent dynamics sparse observations LSTM encoders

2024-11-28abs ↗pdf ↗

Dichotomy of Feature Learning and Unlearning: Fast-Slow Analysis on Neural Networks with Stochastic Gradient Descentcs.LG

Study reveals conditions for neural networks to forget learned features.

problem Understanding feature unlearning in neural networks.

method Infinite-width limit analysis with stochastic gradient descent, fast-slow dynamics.

result Conditions for feature unlearning are determined by the strength of nonlinear terms and initial weights.

feature unlearning neural networks stochastic gradient descent fast-slow dynamics singular perturbation theory

2026-02-07abs ↗pdf ↗

Uncertainty quantification in a mechanical submodel driven by a Wasserstein-GANstat.ML

A GAN method for stochastic boundary conditions in fast dynamics.

problem Uncertainty quantification in fast dynamics and wave propagation.

method Physics-informed GANs for stochastic boundary conditions.

result Improved convergence and better stochastic boundary conditions.

Uncertainty quantification Wasserstein-GAN Physics-informed neural networks Fast dynamics Wave propagation

2021-10-26abs ↗pdf ↗

Prediction of Unobserved Bifurcation by Unsupervised Extraction of Slowly Time-Varying System Parameter Dynamics from Time Series Using Reservoir Computingstat.ML

Predicting unobserved bifurcations in time series with unsupervised parameter extraction.

problem Predicting system behavior with unknown parameters from time series data.

method Reservoir computing framework for unsupervised extraction of slowly varying system parameters.

result Model predicts unknown bifurcations not present in training data.

reservoir computing unsupervised learning bifurcation prediction time series analysis slowly varying parameters

2024-06-20abs ↗pdf ↗

Fast Escape, Slow Convergence: Learning Dynamics of Phase Retrieval under Power-Law Datastat.ML

Study shows how anisotropic data affects learning dynamics in phase retrieval.

problem Understanding learning dynamics in phase retrieval with anisotropic Gaussian inputs.

method Developed a tractable reduction to reveal a three-phase trajectory and derived scaling laws.

result Found that anisotropy leads to a three-phase trajectory: fast escape, slow convergence, and spectral-tail learning.

phase retrieval anisotropic data scaling laws learning dynamics power-law covariance

2025-11-24abs ↗pdf ↗

Herding as a Learning System with Edge-of-Chaos Dynamicsstat.ML

Herding defines a deterministic dynamical system at the edge of chaos. It generates a sequence of model states and parameters by alternating parameter perturbations with state maximizations, where the sequence of states can be interpreted as "samples" from an associated MRF model. Herding differs from maximum likelihoo…

2016-02-09abs ↗pdf ↗

An Efficient Machine Learning Framework for Option Pricing via Fourier Transformq-fin.CP

A hybrid framework uses machine learning to price options faster and more accurately.

problem Rapid recalibration of option pricing models in dynamic markets.

method Integrates smooth offset algorithm with supervised machine learning models.

result Surrogate pricing operators achieve up to 1000x speedup over direct SOA evaluation.

machine learning option pricing Fourier transform supervised learning Lévy dynamics

2025-12-18abs ↗pdf ↗

Fast Convergence of Langevin Dynamics on Manifold: Geodesics meet Log-Sobolevcs.LG

Our work improves Langevin dynamics convergence on manifolds.

problem Sampling from distributions defined on manifolds.

method Generalized Langevin dynamics to manifolds, proving KL decrease rate.

result KL divergence decreases geometrically on manifolds with log-Sobolev inequality.

Langevin dynamics manifolds log-Sobolev inequality KL divergence convergence rate

2020-10-11abs ↗pdf ↗

Sampling by averaging: A multiscale approach to score estimationstat.CO

A new sampling method estimates scores without training or nested MCMC.

problem Efficient sampling from complex, unnormalised distributions.

method Multiscale averaging in SDEs for score estimation.

result Empirical results show competitive accuracy and efficiency.

sampling score estimation multiscale dynamics stochastic differential equations Langevin dynamics

2025-08-20abs ↗pdf ↗

Fast Convergence of

Φ

-Divergence Along the Unadjusted Langevin Algorithm and Proximal Samplermath.ST

The paper analyzes convergence rates of Langevin dynamics and Proximal Sampler using $Φ$ -divergence.

problem Analyzing convergence rates of Langevin dynamics and Proximal Sampler.

method Extending mixing time analyses to

Φ

-divergence, using strong data processing inequalities.

result Convergence of

Φ

-divergence to 0 exponentially fast along Unadjusted Langevin Algorithm and Proximal Sampler.

Langevin dynamics Proximal Sampler Mixing time Φ-divergence Strong data processing inequalities

2024-10-14abs ↗pdf ↗

ReZero is All You Need: Fast Convergence at Large Depthcs.LG

A simple gating mechanism improves deep learning convergence.

problem Vanishing or exploding gradients in deep networks.

method Introducing a zero-initialized parameter to each residual connection.

result Training deep networks (up to 120 layers) with fast convergence and better performance.

Deep learning ResNet Transformer Vanishing gradient Exploding gradient

2020-03-10abs ↗pdf ↗

Stationary Distributions of the Mode-switching Chiarella Modelq-fin.TR

We find stationary distributions in a financial model with trends and mean-reversion.

problem Financial markets with competing trends and mean-reversion.

method Analytical derivation of stationary distributions in various noise and feedback regimes.

result The distributions are unimodal Gaussians in small noise, small feedback limits, but can be bimodal for stronger trends.

financial markets stochastic dynamics stationary distributions mean-reversion trending

2025-11-17abs ↗pdf ↗

A Qualitative Study of the Dynamic Behavior for Adaptive Gradient Algorithmscs.LG

The study examines the dynamic behavior of RMSprop and Adam algorithms.

problem Understanding the training loss curve patterns of adaptive gradient algorithms.

method Careful numerical experiments and theoretical explanations using the signGD flow.

result Adam converges smoother and faster when momentum factors are close to each other.

adaptive gradient algorithms RMSprop Adam training loss curve signGD flow

2020-09-14abs ↗pdf ↗

A fast asynchronous MCMC sampler for sparse Bayesian inferencestat.CO

A fast MCMC sampler for sparse Bayesian inference.

problem Sparse Bayesian inference problems with high computational cost.

method Asynchronous Gibbs sampler extended with data sub-sampling.

result The Markov chain admits an invariant distribution that recovers the main signal with high probability.

MCMC sparse Bayesian inference asynchronous sampling stochastic gradient Langevin dynamics Bayesian iterated sure independent screening

2021-08-14abs ↗pdf ↗

Learning Fast and Slow for Online Time Series Forecastingcs.LG

FSNet improves online time series forecasting by balancing fast adaptation and old knowledge.

problem Online time series forecasting challenges in handling abrupt and recurring patterns.

method Inspired by CLS theory, FSNet uses a dynamic balance between fast adaptation and old knowledge retrieval.

result FSNet achieves robustness to both new and recurring patterns through dynamic balancing and associative memory.

time series forecasting online learning catastrophic forgetting fast adaptation recurring patterns

2022-02-23abs ↗pdf ↗

CTD: Fast, Accurate, and Interpretable Method for Static and Dynamic Tensor Decompositionsmath.NA

How can we find patterns and anomalies in a tensor, or multi-dimensional array, in an efficient and directly interpretable way? How can we do this in an online environment, where a new tensor arrives each time step? Finding patterns and anomalies in a tensor is a crucial problem with many applications, including buildi…

2017-10-09abs ↗pdf ↗

Option pricing under normal dynamics with stochastic volatilityq-fin.PR

In this paper, we derive the price of a European call option of an asset following a normal process assuming stochastic volatility. The volatility is assumed to follow the Cox Ingersoll Ross (CIR) process. We then use the fast Fourier transform (FFT) to evaluate the option price given we know the characteristic functio…

2019-09-17abs ↗pdf ↗

Fast inference of latent space dynamics in huge relational event networkscs.SI

Fast algorithm for analyzing huge social networks.

problem Analyzing dynamic social networks with large numbers of actors.

method Hierarchical strategy for latent space inference with spline processes and machine learning optimization.

result Can fit millions of nodes in a few minutes.

social networks latent space dynamic networks Bayesian inference machine learning

2023-03-29abs ↗pdf ↗

Fast Gaussian Process Based Gradient Matching for Parameter Identification in Systems of Nonlinear ODEsstat.ML

Parameter identification and comparison of dynamical systems is a challenging task in many fields. Bayesian approaches based on Gaussian process regression over time-series data have been successfully applied to infer the parameters of a dynamical system without explicitly solving it. While the benefits in computationa…

2018-04-12abs ↗pdf ↗

High-Frequency Volatility Estimation with Fast Multiple Change Points Detectionq-fin.ST

New method for fast volatility estimation robust to change points.

problem Robust high-frequency volatility estimation with change points.

method ℓ1-regularized power variation estimators using LARS for sparse estimation and dynamic programming for change point refinement.

result Minimax rates achieved for volatility estimators, providing accurate and smooth forecasts.

volatility estimation change points high-frequency trading ℓ1-regularization LARS

2023-03-19abs ↗pdf ↗

Bubblewrap: Online tiling and real-time flow prediction on neural manifoldscs.LG

Bubblewrap predicts neural dynamics online, scaling to thousands of neurons.

problem Direct testing of neural population hypotheses requires online inference of neural state.

method Soft tiling of neural manifold with fast, stable dimensionality reduction.

result Bubblewrap model outperforms existing methods in noisy conditions.

neural dynamics neural manifold online prediction dimensionality reduction expectation maximization

2021-08-31abs ↗pdf ↗

Recurrent switching linear dynamical systemsstat.ML

Many natural systems, such as neurons firing in the brain or basketball teams traversing a court, give rise to time series data with complex, nonlinear dynamics. We can gain insight into these systems by decomposing the data into segments that are each explained by simpler dynamic units. Building on switching linear dy…

2016-10-26abs ↗pdf ↗

CoNBONet: Conformalized Neuroscience-inspired Bayesian Operator Network for Reliability Analysisstat.ML

CoNBONet improves reliability analysis of complex systems with fast, energy-efficient predictions.

problem Time-dependent reliability analysis of nonlinear systems under stochastic excitations is computationally demanding.

method CoNBONet combines deep operator networks with neuroscience-inspired neuron models for fast, energy-efficient inference.

result CoNBONet provides reliable coverage of failure probabilities with theoretical guarantees.

reliability analysis neuroscience Bayesian operator network time-dependent systems stochastic excitations

2026-03-23abs ↗pdf ↗