Use stochastic processes to generate samples and use them to train a fully-connected neural network based on Keras

Overview

neural_calibrator

Use stochastic processes to generate samples and use them to train a fully-connected neural network based on Keras which will then be used to generate residuals. I dont think this is great code but i think that is something for people to start with and works for my use case.

currently supports:

  • CoxIngersollRoss Process
  • Diffusion Process
  • Fractional Brownian Motion
  • Geometric Brownian Motion
  • Ornstein Uhlenbeck Process
  • Poisson Process
  • Vasicek Process

what's what:

- data_generation.py -> handles data (sample, config, saving to csv for post analysis, train/test split, ...)
- main.py -> this is where you f5 and follow instructions to get the trained .h5 model to load elsewhere
- network.py -> basically the network we'll train, test and save if needed. kept as clean as possible so you can copy/paste most of this elsewhere.
- network_manager.py -> handles the actual heavy stuff like training/testing/tensorboard/etc. made for extension but not much work put into it.
- start_here.ipynb -> small notebook with examples

comming up next:

  • Markov chains (HMM/Bernoulli chains/etc)
  • Hawkes process
Owner
Federico Lopez
sic transit gloria mundi
Federico Lopez
Continual reinforcement learning baselines: experiment specifications, implementation of existing methods, and common metrics. Easily extensible to new methods.

Continual Reinforcement Learning This repository provides a simple way to run continual reinforcement learning experiments in PyTorch, including evalu

55 Dec 24, 2022
UA-GEC: Grammatical Error Correction and Fluency Corpus for the Ukrainian Language

UA-GEC: Grammatical Error Correction and Fluency Corpus for the Ukrainian Language This repository contains UA-GEC data and an accompanying Python lib

Grammarly 226 Dec 29, 2022
[ICRA 2022] An opensource framework for cooperative detection. Official implementation for OPV2V.

OpenCOOD OpenCOOD is an Open COOperative Detection framework for autonomous driving. It is also the official implementation of the ICRA 2022 paper OPV

Runsheng Xu 322 Dec 23, 2022
Implementation of neural class expression synthesizers

NCES Implementation of neural class expression synthesizers (NCES) Installation Clone this repository: https://github.com/ConceptLengthLearner/NCES.gi

NeuralConceptSynthesis 0 Jan 06, 2022
HCQ: Hybrid Contrastive Quantization for Efficient Cross-View Video Retrieval

HCQ: Hybrid Contrastive Quantization for Efficient Cross-View Video Retrieval [toc] 1. Introduction This repository provides the code for our paper at

13 Dec 08, 2022
Dynamic Token Normalization Improves Vision Transformers

Dynamic Token Normalization Improves Vision Transformers This is the PyTorch implementation of the paper Dynamic Token Normalization Improves Vision T

Wenqi Shao 20 Oct 09, 2022
Learn other languages ​​using artificial intelligence with python.

The main idea of ​​the project is to facilitate the learning of other languages. We created a simple AI that will interact with you. Just ask questions that if she knows, she will answer.

Pedro Rodrigues 2 Jun 07, 2022
Supporting code for short YouTube series Neural Networks Demystified.

Neural Networks Demystified Supporting iPython notebooks for the YouTube Series Neural Networks Demystified. I've included formulas, code, and the tex

Stephen 1.3k Dec 23, 2022
PyTorch Implementation of DSB for Score Based Generative Modeling. Experiments managed using Hydra.

Diffusion Schrödinger Bridge with Applications to Score-Based Generative Modeling This repository contains the implementation for the paper Diffusion

James Thornton 50 Jan 03, 2023
Release of SPLASH: Dataset for semantic parse correction with natural language feedback in the context of text-to-SQL parsing

SPLASH: Semantic Parsing with Language Assistance from Humans SPLASH is dataset for the task of semantic parse correction with natural language feedba

Microsoft Research - Language and Information Technologies (MSR LIT) 35 Oct 31, 2022
Spatio-Temporal Entropy Model (STEM) for end-to-end leaned video compression.

Spatio-Temporal Entropy Model A Pytorch Reproduction of Spatio-Temporal Entropy Model (STEM) for end-to-end leaned video compression. More details can

16 Nov 28, 2022
Pose Transformers: Human Motion Prediction with Non-Autoregressive Transformers

Pose Transformers: Human Motion Prediction with Non-Autoregressive Transformers This is the repo used for human motion prediction with non-autoregress

Idiap Research Institute 26 Dec 14, 2022
From Perceptron model to Deep Neural Network from scratch in Python.

Neural-Network-Basics Aim of this Repository: From Perceptron model to Deep Neural Network (from scratch) in Python. ** Currently working on a basic N

Aditya Kahol 1 Jan 14, 2022
frida工具的缝合怪

fridaUiTools fridaUiTools是一个界面化整理脚本的工具。新人的练手作品。参考项目ZenTracer,觉得既然可以界面化,那么应该可以把功能做的更加完善一些。跨平台支持:win、mac、linux 功能缝合怪。把一些常用的frida的hook脚本简单统一输出方式后,整合进来。并且

diveking 997 Jan 09, 2023
Covid19-Forecasting - An interactive website that tracks, models and predicts COVID-19 Cases

Covid-Tracker This is an interactive website that tracks, models and predicts CO

Adam Lahmadi 1 Feb 01, 2022
Latent Execution for Neural Program Synthesis

Latent Execution for Neural Program Synthesis This repo provides the code to replicate the experiments in the paper Xinyun Chen, Dawn Song, Yuandong T

Xinyun Chen 16 Oct 02, 2022
Orthogonal Over-Parameterized Training

The inductive bias of a neural network is largely determined by the architecture and the training algorithm. To achieve good generalization, how to effectively train a neural network is of great impo

Weiyang Liu 11 Apr 18, 2022
URIE: Universal Image Enhancementfor Visual Recognition in the Wild

URIE: Universal Image Enhancementfor Visual Recognition in the Wild This is the implementation of the paper "URIE: Universal Image Enhancement for Vis

Taeyoung Son 43 Sep 12, 2022
A modification of Daniel Russell's notebook merged with Katherine Crowson's hq-skip-net changes

Edits made to this repo by Katherine Crowson I have added several features to this repository for use in creating higher quality generative art (featu

Paul Fishwick 10 May 07, 2022
Pytorch implementation of Straight Sampling Network For Point Cloud Learning (ICIP2021).

Pytorch code for SS-Net This is a pytorch implementation of Straight Sampling Network For Point Cloud Learning (ICIP2021). Environment Code is tested

Sun Ran 1 May 18, 2022