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.
Sinh-acceleration speeds up B-spline option pricing.
Efficiently accelerates attention calculation for Transformers with relative positional encoding.
New MCMC method speeds up quantum physics simulations by a factor of 100.
A new FFT method for Heston model option pricing with explicit error bounds.
The rough Heston model emerges from scaling bivariate INAR processes, linking microstructure to option pricing.
Deep learning has delivered its powerfulness in many application domains, especially in image and speech recognition. As the backbone of deep learning, deep neural networks (DNNs) consist of multiple layers of various types with hundreds to thousands of neurons. Embedded platforms are now becoming essential for deep le…
Paper extends FFT-based differential privacy method to heterogeneous compositions.
New algorithms compute Volterra signature efficiently for time series analysis.
A new algorithm computes Fourier coefficients for a specified range efficiently.
In the Lagrangian approach to 2-dimensional sigma models, B-fields and D-branes contribute topological terms to the action of worldsheets of both open and closed strings. We show that these terms naturally fit into a 2-dimensional, smooth open-closed functorial field theory (FFT) in the sense of Atiyah, Segal, and Stol…
We discuss various analytic and numerical methods that have been used to get option prices within a framework of the VG model. We show that some popular methods, for instance, Carr-Madan's FFT method could blow up for certain values of the model parameters even for an European vanilla option. Alternative methods - one …
Scalable kernel methods for large datasets using Fourier representations and NUFFT.
DAFNO learns surrogates for complex systems on irregular geometries.
An efficient adaptive direct numerical integration (DNI) algorithm is developed for computing high quantiles and conditional Value at Risk (CVaR) of compound distributions using characteristic functions. A key innovation of the numerical scheme is an effective tail integration approximation that reduces the truncation …
We compare the CPU effort and pricing biases of seven Fourier-based implementations. Our analyses show that truncation and discretization errors significantly increase as we move away from the Black-Scholes-Merton framework. We rank the speed and accuracy of the competing choices, showing which methods require smaller …
A new FFT-based method for fast rigid alignment of 2D closed curves.
The paper prices energy spread options using a complex stochastic model.
This work introduces 'Artificial Entanglement' to understand LLMs' fine-tuning effectiveness.
A new FFT-based method simplifies causal structure recovery for linear dynamical systems.
WideBNet learns inverse scattering from wide-band data efficiently and stably.
The paper improves privacy accounting for discrete-valued mechanisms and the subsampled Gaussian mechanism.
New simulation technique speeds up Lévy-driven OU process pricing.
Extends option pricing framework without risk-free asset using Levy jumps.
Large-scale deep neural networks (DNNs) are both compute and memory intensive. As the size of DNNs continues to grow, it is critical to improve the energy efficiency and performance while maintaining accuracy. For DNNs, the model size is an important factor affecting performance, scalability and energy efficiency. Weig…
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…
In this paper, we present a reverberation removal approach for speaker verification, utilizing dual-label deep neural networks (DNNs). The networks perform feature mapping between the spectral features of reverberant and clean speech. Long short term memory recurrent neural networks (LSTMs) are trained to map corrupted…
Local laGPR speeds up multiscale mechanics simulations without neural networks.
We propose a new method for the numerical solution of backward stochastic differential equations (BSDEs) which finds its roots in Fourier analysis. The method consists of an Euler time discretization of the BSDE with certain conditional expectations expressed in terms of Fourier transforms and computed using the fast F…
We present a new algorithm for the 2D Sliding Window Discrete Fourier Transform (SWDFT). Our algorithm avoids repeating calculations in overlapping windows by storing them in a tree data-structure based on the ideas of the Cooley- Tukey Fast Fourier Transform (FFT). For an array and wi…
Automatically detecting sound units of humpback whales in complex time-varying background noises is a current challenge for scientists. In this paper, we explore the applicability of Convolution Neural Network (CNN) method for this task. In the evaluation stage, we present 6 bi-class classification experimentations of …
We introduce a tractable multi-currency model with stochastic volatility and correlated stochastic interest rates that takes into account the smile in the FX market and the evolution of yield curves. The pricing of vanilla options on FX rates can be performed effciently through the FFT methodology thanks to the affinit…
SKI speeds up Toeplitz Neural Networks by avoiding explicit decay bias and using frequency response.
In image deconvolution problems, the diagonalization of the underlying operators by means of the FFT usually yields very large speedups. When there are incomplete observations (e.g., in the case of unknown boundaries), standard deconvolution techniques normally involve non-diagonalizable operators, resulting in rather …
FastSecAgg improves federated learning security and efficiency.
This paper computes exact posterior distributions of mixture weights in hierarchical Bayesian models.
The Heston model stands out from the class of stochastic volatility (SV) models mainly for two reasons. Firstly, the process for the volatility is non-negative and mean-reverting, which is what we observe in the markets. Secondly, there exists a fast and easily implemented semi-analytical solution for European options.…
Improved SVM classification with interpretable features from scattered data.
Improved numerical solution for BSDEs with reduced boundary errors.
Accelerates Riemannian gradient methods with extrapolation.
The convolution method for the numerical solution of forward-backward stochastic differential equations (FBSDEs), introduced in [21], uses a uniform space grid. In this paper we utilize a tree-like spatial discretization that approximates the BSDE on the tree, so that no spatial interpolation procedure is necessary. In…
This paper examines the problem of pricing spread options under some models with jumps driven by Compound Poisson Processes and stochastic volatilities in the form of Cox-Ingersoll-Ross(CIR) processes. We derive the characteristic function for two market models featuring joint normally distributed jumps, stochastic vol…
Accelerates optimization in asynchronous systems with sparse updates.
We analyze Riemannian accelerated methods using a new framework.
PF-LaCG removes the need for knowing smoothness and strong convexity parameters for locally accelerated CG.
Accelerates coordinate descent methods for machine learning problems.
In a range of fields including the geosciences, molecular biology, robotics and computer vision, one encounters problems that involve random variables on manifolds. Currently, there is a lack of flexible probabilistic models on manifolds that are fast and easy to train. We define an extremely flexible class of exponent…
Continuized Nesterov acceleration accelerates stochastic gradient descent and gossip algorithms.
Accelerated gradient methods play a central role in optimization, achieving optimal rates in many settings. While many generalizations and extensions of Nesterov's original acceleration method have been proposed, it is not yet clear what is the natural scope of the acceleration concept. In this paper, we study accelera…