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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.

169,291 papers · 148 categories

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48 results for Input History

RNNs can store information in parameters and input history, with capacity and trainability being key factors.

problem Understanding and optimizing the capacity and trainability of RNNs.
method Experimental analysis of various RNN architectures, including comparison of training difficulty and per-task parameter capacity.
result RNNs can store an amount of task information linear in the number of parameters (approximately 5 bits per parameter) and can store one real number from input history per hidden unit.

Dual neural network improves treatment recommendations from medical history.

problem Improving treatment recommendations from patient medical history.
method Memory-augmented neural network with dual controllers.
result Dual controller write-protected memory-augmented neural network outperformed traditional methods.

Extends IPC to time-variant systems, revealing information processing in diverse dynamics.

problem Limited applicability of IPC to time-invariant systems and specific input distributions.
method Established connection between IPC and PC expansion, extended orthogonal bases to time and input history.
result IPC equivalent to squared norm of PC expansion coefficients, applicable to arbitrary input distributions.

Brief history and challenges of interpretable machine learning.

problem Challenges in interpreting machine learning models, especially in scientific applications.
method Overview of state-of-the-art methods and discussion of challenges.
result Interpretable machine learning has a rich history but faces significant challenges.

Bayesian inference over admissible histories leads to irreversible kinetics.

problem Modeling irreversible processes in systems with uncertain histories.
method A Gibbs-type measure weighted by energy-dissipation action and observation constraints, interpreted as a Bayesian posterior.
result The measure concentrates on maximum-a-posteriori (MAP) histories, recovering classical deterministic evolution.

HS-FNO models non-Markovian PDEs by learning history and future states.

problem Non-Markovian dynamics where future states depend on past history.
method History-Space Fourier Neural Operator (HS-FNO) for delay and memory-driven PDEs.
result HS-FNO achieves lowest aggregate errors across various PDE families.

Paper proposes a surrogate model for efficient experience rating in large insurance portfolios.

problem Inexpensive and transparent computation of Bayesian premiums for large insurance portfolios.
method Surrogate modeling approach using likelihood-based summary statistics.
result Reduced computational burden and provided a transparent way of computing Bayesian premiums.

Study learns linear system dynamics from noisy bilinear data.

problem Learning linear dynamics from bilinear observations with process and measurement noise.
method Regression with Kronecker product design, data-dependent and independent error bounds.
result Upper bounds on statistical error rates and sample complexity for learning dynamics matrices.

RNNs are suboptimal at compressing past sensory inputs for future prediction.

problem RNNs do not optimally compress past sensory inputs for future prediction.
method Investigated RNNs trained with maximum likelihood and found they extract unnecessary information. Injected noise into hidden states to improve performance.
result Injecting noise into RNN hidden states improves predictive information, sample quality, likelihood, and classification performance.

Volterra signature provides a clear, interpretable feature for history-dependent systems.

problem Learning from non-Markovian time series with implicit memory mechanisms.
method Develops Volterra signature as a tensor algebra representation weighted by a temporal kernel, proving injectivity and universal approximation.
result Volterra signature leads to linear functionals and universal approximation, improving dynamic learning tasks.

SRMC framework reduces Monte Carlo variance by history-based sampling in high-dimensional spaces.

problem Efficient sampling in high-dimensional discrete or continuous state spaces.
method Score-Repellent Monte Carlo (SRMC) framework that summarizes history through running average of score evaluations.
result Improves estimator variance and mode coverage with constant memory usage.

Paper analyzes history-based RL methods for MDPs, introduces a theoretical framework and practical algorithm.

problem Improving RL performance in MDPs using history-based features.
method Theoretical framework for history-based RL, practical algorithm design.
result Practical RL algorithm shows effectiveness on continuous control tasks.

Pseudorandom inputs in diffusion models affect generation quality.

problem Pseudorandom inputs in diffusion models can be learned and affect model performance.
method Used a small multilayer perceptron to predict next values in pseudorandom orbits and a diffusion probe to replace real images with random tensors.
result Pseudorandom inputs can produce markedly different diffusion losses and generation quality.

Neural networks improve cancer risk prediction from family history data.

problem Improving cancer risk prediction from family history data using machine learning.
method Developed and trained neural network models on large pedigrees to predict hereditary cancers.
result Neural networks can achieve nearly optimal prediction performance and outperform traditional models in misreported data.

The paper explains why estimating a history-dependent policy can reduce MSE in reinforcement learning.

problem Understanding why history-dependent policies can improve MSE in off-policy evaluation.
method The paper derives a bias-variance decomposition of MSE for various OPE estimators, showing how history-dependent policies can decrease variance and increase bias.
result History-dependent policies can decrease the variance of importance sampling estimators, leading to lower MSE.

Neural networks improve loss reserving with case estimates and transaction data.

problem Improving loss reserving accuracy using neural networks.
method Comparison of feed-forward and recurrent neural networks trained on case estimates and transaction data.
result Case estimates significantly improve predictions, but memory-equipped neural networks offer minimal additional benefit.

Neural Assistant integrates knowledge reasoning and dialogue generation in a single model.

problem Challenges in task-oriented dialog systems, including multi-turn language understanding and generation, knowledge retrieval and reasoning, and action prediction.
method Develops a single neural network model that jointly predicts text responses and actions from conversation history and external knowledge.
result The model learns to reason on external knowledge with weak supervision, improving factual accuracy and language generation performance.

This paper suggests claim history will be deprecated in future auto insurance rates.

problem The role of historical claim records in auto insurance rates.
method Proposes a new risk variable elimination method and real-time road risk model design.
result Claim history will be considered a 'noise' factor and deprecated in Pay-How-You-Drive models.

We present in this chapter (Chapter II) the history of ideas which lead up to the development of modern knot theory. We are more detailed when pre-XX century history is reported. With more recent times we are more selective, stressing developments related to Jones type invariants of links. In the Appendix, A.Przybyszew…

2007-03-03abs ↗pdf ↗

A new model learns demand patterns from data, reducing complexity and improving accuracy.

problem Forecasting short-term demand from spatiotemporal data with complex patterns.
method Temporal-Guided Network (TGNet) using graph networks and temporal-guided embedding.
result TGNet achieves competitive performance with fewer parameters compared to state-of-the-art models.

PHE adds pseudo-rewards to history to minimize regret in stochastic bandits.

problem Minimizing cumulative regret in stochastic multi-armed bandits.
method PHE algorithm that adds O(t)O(t) i.i.d. pseudo-rewards to history and pulls the best arm based on the perturbed history.
result Near-optimal regret bounds derived for PHE.

The paper tackles the problem of recovering network history from structure, identifying a phase transition in recoverability.

problem Recovering the history of complex networks from their current structure.
method Bayesian formulation, sequential Monte Carlo algorithm, heuristic approach.
result Identification of a phase transition in the quality of reconstructed history.

DR-FRL learns functional states from irregular histories for causal inference.

problem Causal inference with irregularly sampled longitudinal data.
method DR-FRL workflow combining functional and temporal encoders, nuisance heads, and EIF-targeted validation.
result DR-FRL can improve causal inference when pseudo-outcomes are heavy-tailed or measurement is informative.

Corrects mortality data anomalies for longevity risk assessment in Solvency 2 framework.

problem Impact of mortality data anomalies on longevity risk assessment in Solvency 2 framework.
method Developed and extended an approach to correct mortality tables for three countries, using historical data and stochastic models.
result Corrected mortality tables improve data quality and slightly decrease the Solvency Capital Requirement.

Paper tackles reinforcement learning for STL specifications with state history.

problem Learning optimal policies to satisfy STL specifications often requires too much state history, making the problem computationally intractable.
method Proposes a compact augmented state-space representation to capture state history and an approximation method to solve the objective.
result Shows the performance bound of the approximate solution and compares it with an existing technique.

This paper reviews deep time-series forecasting focusing on autocorrelation modeling.

problem Modeling autocorrelation in history and label sequences for time-series forecasting.
method Proposes a novel taxonomy for model architectures and learning objectives.
result Provides a comprehensive review and analysis of deep time-series forecasting.

Epsilon-machines are minimal, unifilar presentations of stationary stochastic processes. They were originally defined in the history machine sense, as hidden Markov models whose states are the equivalence classes of infinite pasts with the same probability distribution over futures. In analyzing synchronization, though…

2011-11-18abs ↗pdf ↗

Combining deep learning and ensemble smoothers for better history matching.

problem Dealing with complex facies distributions in history matching.
method Using autoencoders and generative adversarial networks to parameterize facies models, applying distance-based localization.
result Improved history matching performance with deep learning parameterizations.

Transformers improve Alzheimer's disease progression prediction by accounting for irregular biomarker histories.

problem Difficult prediction of medium-horizon Alzheimer's disease progression due to tied clinical scores and irregular biomarker observations.
method Developed a residual gap-aware transformer that combines statistical reference with transformer-based residual learning.
result The proposed model reduces mean error and improves prediction-observation correlation compared to baseline models.

Foundation models improve wage gap decomposition by capturing omitted career history factors.

problem Estimating wage disparities using incomplete career history data.
method Fine-tuning foundation models to mitigate omitted variable bias and estimate wage gaps.
result Foundation models can decompose gender wage gaps more accurately than traditional econometric methods.

A dangerously brief history of the developments of the main ideas in economics, as observed by a physicist, is given. This was published in 'Econophysics of Stock and Other Markets', Eds. A. Chatterjee, B. K. Chakrabarti, New Economic Windows Series, Springer, Milan, 2006, pp~219-224.

2007-09-26abs ↗pdf ↗

Marden's Tameness Conjecture predicts that every hyperbolic 3-manifold with finitely generated fundamental group is homeomorphic to the interior of a compact 3-manifold. It was recently established by Agol and Calegari-Gabai. We will survey the history of work on this conjecture and discuss its many applications.

2010-07-31abs ↗pdf ↗

Deep learning calibrates CO2 storage formations from seismic and well data.

problem Uncertainty in CO2 storage formation properties.
method Two deep learning models for well and seismic data, integrated into MCMC history matching.
result Significant uncertainty reduction in key parameters and accurate CO2 plume predictions.