Popular GitHub Repositories for Principle

This is a curated list for Information Bottleneck Principle, in memory of Professor Naftali Tishby.

Code for our nips19 paper: You Only Propagate Once: Accelerating Adversarial Training Via Maximal Principle

A unified end-to-end learning and control framework that is able to learn a (neural) control objective function, dynamics equation, control policy, or/and optimal trajectory in a control system.

An LLM GUI application; enables you to interact with your files, offering dynamic parameters that can modify response behavior during runtime.

Apply modern, deep learning techniques for anomaly detection to identify network intrusions.

A minimalist MVP demonstrating a simple yet profound insight: aligning AI memory with human episodic memory granularity. Shows how this single principle enables simple methods to rival complex memory frameworks for conversational tasks.

PILF: A IPWT-inspired continual learning experiment focus on mitigate catastrophic forgetting with Surprise-gated Mixture of Experts (MoE) model.

RewardAnything: Generalizable Principle-Following Reward Models

In this repository, I have developed a CycleGAN architecture with embedded Self-Attention Layers, that could solve three different complex tasks. Here the same principle Neural Network architecture has been used to solve the three different task. Although truth be told, my model has not exceeded any state of the art performances for the given task, but the architecture was powerful enough to understand the task that has been given to solve and produce considerably good results.

Implementing a diverse reference materials in Go like Zhejiang University's thinking methodologies to comprehend its fundamental operational principles. Include heap, stack, hashtable, etc.

A Novel Approach leveraging Auto-Encoders, LSTM Networks and Maximum Entropy Principle for Video Super-Resolution (Upscaling and Frame Interpolation)

I developed 2 machine learning software that predict and classify ozone day and non-ozone day. The working principle of the two is similar but there are differences. I got the dataset from ics.icu. Each software has a different mathematical model, Gaussian RBF and Linear Kernel, and classifications are visualized in different ways. I would be happy to present the software to you!

Various experiments related to AGI

Generalized Predictive Coding in Torch

'Roiergasias' kubernetes operator is meant to address a fundamental requirement of any data science / machine learning project running their pipelines on Kubernetes - which is to quickly provision a declarative data pipeline (on demand) for their various project needs using simple kubectl commands. Basically, implementing the concept of No Ops. The fundamental principle is to utilise best of docker, kubernetes and programming language features to run a workflow with minimal workflow definition syntax. It is a Go based workflow running on command line or Kubernetes with the help of a custom operator for a quick and automated data pipeline for your machine learning projects (a flavor of MLOps).

PCA(Principle Component Analysis) For Seed Dataset in Machine Learning

This repository is a reproduction of the reinforcement learning algorithm I have learned. It is convenient to quickly understand the implementation and principle of the algorithm in combination with theory.

Source code for brain data processing and analysis in paper <A controller-peripheral architecture and costly energy principle for learning>

Automated pipeline for Multi-Variate Time Series AI in medical longitudinal analysis, following FAIRness principle.

Source code for DNN finetuning pipeline in paper <A controller-peripheral architecture and costly energy principle for learning>