Implementation of two perturbation functions for Knapsack Problem
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Updated
Dec 17, 2020 - Python
Implementation of two perturbation functions for Knapsack Problem
Dataset of SOSAA model trajectory runs
Mandelbrot Generator based on perturbation and series approximation
A demonstration of perturbation of data
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Presentation given to Drexel XAI seminar on "Interpretable Explanations of Black Boxes by Meaningful Perturbation"
Assessing consciousness by perturbing a dynamic mean-field whole-brain model fitted to empirical neuroimaging data
Second Year project (Module: Artificial Intelligence Methods), focusing on implementing and evaluating AI Optimisation methods.
GUI for rust-fractal-core, a mandelbrot fractal visualizer featuring perturbation based iteration methods, series approximation and multithreading.
Interpretable Explanations of Black Boxes by Meaningful Perturbation Pytorch
Perturbation toolbox to solve DSGE models with Matlab
Single-cell Analysis of Perturbational Effects using Machine Learning
perturbation of coupled model input over a space of input variables
Mandelbrot fractal visualizer featuring perturbation based iteration methods, series approximation and multithreading.
Sophisticated space mission simulator. Calculate n-body trajectories, perform orbital maneuvers, apply various perturbations, plot variables, and more!
scPerturb: A resource and a python/R tool for single-cell perturbation data
Replication code for checking identification in nonlinear pruned DSGE models with Gaussian or Student's t distributed errors
Replication code for simulating and estimation by GMM of DSGE models with higher-order statistics
Macros and functions to work with DSGE models.
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