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.
The paper introduces a method to explain redundancy in deep CNNs using unit impulse response.
Bayesian regularization tackles collinearity in large-scale systems with correlated inputs.
New method for estimating and testing impulse responses in high-dimensional VAR systems.
Estimates impulse response functions using machine learning in time series data.
Bayesian approach tackles collinearity in large-scale linear system identification.
Incorporating nonlinearity is paramount to predicting the future states of a dynamical system, its response to shocks, and its underlying causal network. However, most existing methods for causality detection and impulse response, such as Vector Autoregression (VAR), assume linearity and are thus unable to capture the …
New method decomposes local projections to reveal historical drivers of estimates.
New mathematical foundations for stable RKHSs improve system identification.
We consider the problem of impulse response estimation of stable linear single-input single-output systems. It is a well-studied problem where flexible non-parametric models recently offered a leap in performance compared to the classical finite-dimensional model structures. Inspired by this development and the success…
Recent developments in linear system identification have proposed the use of non-parameteric methods, relying on regularization strategies, to handle the so-called bias/variance trade-off. This paper introduces an impulse response estimator which relies on an -type regularization including a rank-penalty derive…
Regularized least-squares approaches have been successfully applied to linear system identification. Recent approaches use quadratic penalty terms on the unknown impulse response defined by stable spline kernels, which control model space complexity by leveraging regularity and bounded-input bounded-output stability. T…
The classical approach to linear system identification is given by parametric Prediction Error Methods (PEM). In this context, model complexity is often unknown so that a model order selection step is needed to suitably trade-off bias and variance. Recently, a different approach to linear system identification has been…
Paper presents efficient algorithms for convolutional neural networks using Winograd minimal filtering.
Gaussian processes improve system identification models.
In this paper we propose a new identification scheme for Hammerstein systems, which are dynamic systems consisting of a static nonlinearity and a linear time-invariant dynamic system in cascade. We assume that the nonlinear function can be described as a linear combination of basis functions. We reconstruct the …
This paper compares classical parametric methods with recently developed Bayesian methods for system identification. A Full Bayes solution is considered together with one of the standard approximations based on the Empirical Bayes paradigm. Results regarding point estimators for the impulse response as well as for conf…
The paper proposes a new model for predicting and analyzing economic variables.
A new nonparametric approach for system identification has been recently proposed where the impulse response is seen as the realization of a zero--mean Gaussian process whose covariance, the so--called stable spline kernel, guarantees that the impulse response is almost surely stable. Maximum entropy properties of the …
This paper simplifies complex game dynamics by using a recursive representation.
Unified Bayesian framework for LTV system identification using neural networks and Gaussian Processes.
The paper proposes a control strategy for systems with sparse parameters using compressed sensing.
Estimation of response functions is an important task in dynamic medical imaging. This task arises for example in dynamic renal scintigraphy, where impulse response or retention functions are estimated, or in functional magnetic resonance imaging where hemodynamic response functions are required. These functions can no…
Deep learning classifies animal behavior from wearable accelerometers.
The study finds a long-term relationship between Dubai crude oil and US natural gas prices.
New method uses VAEs for blind channel equalization and decoding.
Additive asynchronous and cyclostationary impulsive noise limits communication performance in OFDM powerline communication (PLC) systems. Conventional OFDM receivers assume additive white Gaussian noise and hence experience degradation in communication performance in impulsive noise. Alternate designs assume a parametr…
kNNSampler imputes missing values from their distributions using kNN.
Paper tackles risk-sensitive impulse control for continuous-time processes.
A new Bayesian approach to linear system identification has been proposed in a series of recent papers. The main idea is to frame linear system identification as predictor estimation in an infinite dimensional space, with the aid of regularization/Bayesian techniques. This approach guarantees the identification of stab…
Study proves interaction of three impulsive gravitational waves, showing local solution and Lipschitz continuity.
Long-range climate forecasts use integrated assessment models to link the global economy to greenhouse gas emissions. This paper evaluates an alternative economic framework outlined in part 1 of this study (Garrett, 2014) that approaches the global economy using purely physical principles rather than explicitly resolve…
Proposes a deep learning framework for estimating counterfactual outcomes.
In this paper we investigate a new class of growth rate maximization problems based on impulse control strategies such that the average number of trades per time unit does not exceed a fixed level. Moreover, we include proportional transaction costs to make the portfolio problem more realistic. We provide a Verificatio…
The superior temporal gyrus (STG) region of cortex critically contributes to speech recognition. In this work, we show that a proposed WaveNet, with limited available data, is able to reconstruct speech stimuli from STG intracranial recordings. We further investigate the impulse response of the fitted model for each re…
The paper tackles system identification via Hankel nuclear norm regularization, improving estimation rates and singular value gaps.
A nonlinear channel estimator using complex Least Square Support Vector Machines (LS-SVM) is proposed for pilot-aided OFDM system and applied to Long Term Evolution (LTE) downlink under high mobility conditions. The estimation algorithm makes use of the reference signals to estimate the total frequency response of the …
Develops a numerical algorithm for stochastic impulse control using regression surrogates.
Impulsive waves contradict a 1962 conjecture about pp-waves.
Random neural networks with ReLU activations are non-Gaussian processes.
Study strategic competition in commodity markets using impulse-switching controls.
Novel DCD-based algorithms improve RLS performance in noisy channels.
We propose a novel receiver for orthogonal frequency division multiplexing (OFDM) transmissions in impulsive noise environments. Impulsive noise arises in many modern wireless and wireline communication systems, such as Wi-Fi and powerline communications, due to uncoordinated interference that is much stronger than the…
Optimal trading strategy between CEXs and DEXs with priority fees and stochastic delays.
Neural language models (LMs) based on recurrent neural networks (RNN) are some of the most successful word and character-level LMs. Why do they work so well, in particular better than linear neural LMs? Possible explanations are that RNNs have an implicitly better regularization or that RNNs have a higher capacity for …
Paper studies long-run risk optimization with dyadic impulses for unbounded processes.
New method predicts nonfactuality in LLM responses using semantic isotropy.
Study bank salvage model with stochastic impulse controls to minimize costs.
This paper solves a Bayes sequential impulse control problem for a diffusion, whose drift has an unobservable parameter with a change point. The partially-observed problem is reformulated into one with full observations, via a change of probability measure which removes the drift. The optimal impulse controls can be ex…