Popular GitHub Repositories for Polynomial Chaos

Learning to create Machine Learning Algorithms

My journey to learn and grow in the domain of Machine Learning and Artificial Intelligence by performing the #100DaysofMLCode Challenge. Now supported by bright developers adding their learnings :+1:

Official Implementation of the CVPR'20 paper 'Π-nets: Deep Polynomial Neural Networks' and its T-PAMI-21 extension.

Machine Learning Concepts with Concepts

PyInstaCrack: Ultimate Instagram hacking suite. Python-driven, AI-enhanced, brute-force chaos. Stealth ops, ethical only. Slice through defenses like a cyber god! ⚡☠️

Prediction of timeseries using methods of nonlinear dynamics and timeseries analysis

RNN architectures trained with Backpropagation and Reservoir Computing (RC) methods for forecasting high-dimensional chaotic dynamical systems.

jupyter notebooks alternative with touch of notion and chaos

Official PyTorch implementation of the ICCV 2023 paper: From Chaos Comes Order: Ordering Event Representations for Object Recognition and Detection.

An unofficial Python (Numpy and Pytorch) implementation of "Polyblur: Removing mild blur by polynomial reblurring" by Mauricio Delbracio and collaborators.

Intel’s HERACLES accelerator introduces a new set of fundamental instructions, the Polynomial Instructions Set Architecture (P-ISA) that operates directly on polynomials requiring a completely new programming environment. This open-source project aims at developing the building blocks for a compiler toolchain for HERACLES.

High order and sparse layers in pytorch. Lagrange Polynomial, Piecewise Lagrange Polynomial, Piecewise Discontinuous Lagrange Polynomial (Chebyshev nodes) and Fourier Series layers of arbitrary order. Piecewise implementations could be thought of as a 1d grid (for each neuron) where each grid element is Lagrange polynomial. Both full connected and convolutional layers included.

Reliable and efficient agent-based models in Rust

This is a Statistical Learning application which will consist of various Machine Learning algorithms and their implementation in R done by me and their in depth interpretation.Documents and reports related to the below mentioned techniques can be found on my Rpubs profile.

Chaos Reactor makes generative AI accessible to all ?

Reservoir computing for short-and long-term prediction of chaotic systems, with tasks Lorenz and Mackey-Glass systems. Bayesian optimization (hyperparameter optimization algorithm) is used to tune the hyperparameters and improve the performance.

Selected problems and their solutions from the book on "Machine Intelligence in Design Automation"

? A curated list of resources for testing AI agents - frameworks, methodologies, benchmarks, tools, and best practices for ensuring reliable, safe, and effective autonomous AI systems

The official implementation of "ChaosEater: Fully Automating Chaos Engineering with Large Language Models"

Probabilistic Response mOdel Fitting with Interactive Tools