Flexible SVM framework implementation
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Updated
Dec 18, 2017 - Python
Flexible SVM framework implementation
Support vector machines flexible framework
Julia implementations of the Hilbert-Schmidt Independence Criterion (HSIC)
MMD and HSIC for non-stationary random processes
Harvard Spring 2020 Math 110 Vector Space Methods for ODEs (Christian Brennecke)
My PhD thesis: Regression modelling using priors depending on Fisher information covariance kernels (I-priors)
Python code for kernel inference - optimal estimation of covariance functions
I have implemented a kalman filter from an existing paper that can predict and estimate the behavior of a noisy dynamic system. I have used kernel trick to compute some of the equations.
Additive interaction modelling using I-priors
Originally written to solve systems of integrodifferential equations via collocation method in an arbitrary number of variables. The current implementation of the method is based on a finite-dimensional orthogonal system of functions.
An infinite dimensional vector module.
A Mathematica package for performing calculations involving matrices/vectors in the Dirac notation which is usually used in quantum mechanics/quantum computing.
Source code of 'Deep transfer operator learning for partial differential equations under conditional shift'.
Homemade SVM dual problem solver
QuantaPlus is a light C++ template library for quantum mechanics problems
A Discrete Fourier Transform (DFT), a Fast Wavelet Transform (FWT), and a Wavelet Packet Transform (WPT) algorithm in 1-D, 2-D, and 3-D using normalized orthogonal (orthonormal) Haar, Coiflet, Daubechie, Legendre and normalized biorthognal wavelets in Java.
A refactored port and code rebuilt of JWave - Discrete Fourier Transform (DFT), Fast Wavelet Transform (FWT), Wavelet Packet Transform (WPT), some Shifting Wavelet Transform (SWT) by using orthogonal (orthonormal) wavelets like Haar, Daubechie, Coiflet, and other normalized bi-orthogonal wavelets.
Official code for Continuous-Time Functional Diffusion Processes (NeurIPS 2023).
A light-weight repository to compute Amplitude Phase distance between two functions
Code to the paper: A First Approach to Quantum Logical Shape Classification Framework
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