An open source machine learning library for performing regression tasks using RVM technique.

Overview

neonrvm_logo

GitHub GitHub release (latest SemVer including pre-releases) GitHub stars PyPI - Status

Introduction

neonrvm is an open source machine learning library for performing regression tasks using RVM technique. It is written in C programming language and comes with bindings for the Python programming language.

neonrvm was born during my master's thesis to help reduce training times and required system resources. neonrvm did that by getting rid of multiple middleware layers and optimizing memory usage.

Under the hood neonrvm uses expectation maximization fitting method, and allows basis functions to be fed incrementally to the model. This helps to keep training times and memory requirements significantly lower for large data sets.

neonrvm is not trying to be a full featured machine learning framework, and only provides core training and prediction facilities. You might want to use it in conjunction with higher level scientific programming languages and machine learning tool kits instead.

RVM technique is very sensitive to input data representation and kernel selection. You might consider something else if you are looking for a less challenging solution.


Documentation

Please visit the dedicated users guide page: https://siavashserver.github.io/neonrvm/


License


Future work

  • Investigate methods to make learning process numerically more stable
  • Implement classification
  • Create higher level wrappers and programming language bindings
  • Improve documentation

Reference

  • Tipping, M. E. (2001). Sparse Bayesian learning and the relevance vector machine. Journal of machine learning research, 1(Jun), 211-244.
  • Ben-Shimon, D., & Shmilovici, A. (2006). Accelerating the relevance vector machine via data partitioning. Foundations of Computing and Decision Sciences, 31(1), 27-42.
You might also like...
TorchDistiller - a collection of the open source pytorch code for knowledge distillation, especially for the perception tasks, including semantic segmentation, depth estimation, object detection and instance segmentation.

This project is a collection of the open source pytorch code for knowledge distillation, especially for the perception tasks, including semantic segmentation, depth estimation, object detection and instance segmentation.

A vision library for performing sliced inference on large images/small objects
A vision library for performing sliced inference on large images/small objects

SAHI: Slicing Aided Hyper Inference A vision library for performing sliced inference on large images/small objects Overview Object detection and insta

A library for performing coverage guided fuzzing of neural networks

TensorFuzz: Coverage Guided Fuzzing for Neural Networks This repository contains a library for performing coverage guided fuzzing of neural networks,

Tensorflow Implementation of SMU: SMOOTH ACTIVATION FUNCTION FOR DEEP NETWORKS USING SMOOTHING MAXIMUM TECHNIQUE

SMU A Tensorflow Implementation of SMU: SMOOTH ACTIVATION FUNCTION FOR DEEP NETWORKS USING SMOOTHING MAXIMUM TECHNIQUE arXiv https://arxiv.org/abs/211

Web-interface + rest API for classification and regression (https://jeff1evesque.github.io/machine-learning.docs)
Web-interface + rest API for classification and regression (https://jeff1evesque.github.io/machine-learning.docs)

Machine Learning This project provides a web-interface, as well as a programmatic-api for various machine learning algorithms. Supported algorithms: S

Price-Prediction-For-a-Dream-Home - A machine learning based linear regression trained model for house price prediction.
Price-Prediction-For-a-Dream-Home - A machine learning based linear regression trained model for house price prediction.

Price-Prediction-For-a-Dream-Home ROADMAP TO THIS LINEAR REGRESSION BASED HOUSE PRICE PREDICTION PREDICTION MODEL Import all the dependencies of the p

performing moving objects segmentation using image processing techniques with opencv and numpy
performing moving objects segmentation using image processing techniques with opencv and numpy

Moving Objects Segmentation On this project I tried to perform moving objects segmentation using background subtraction technique. the introduced meth

Python scripts for performing stereo depth estimation using the HITNET Tensorflow model.
Python scripts for performing stereo depth estimation using the HITNET Tensorflow model.

HITNET-Stereo-Depth-estimation Python scripts for performing stereo depth estimation using the HITNET Tensorflow model from Google Research. Stereo de

Python scripts form performing stereo depth estimation using the CoEx model in ONNX.
Python scripts form performing stereo depth estimation using the CoEx model in ONNX.

ONNX-CoEx-Stereo-Depth-estimation Python scripts form performing stereo depth estimation using the CoEx model in ONNX. Stereo depth estimation on the

Releases(v0.2.0)
Graph neural network message passing reframed as a Transformer with local attention

Adjacent Attention Network An implementation of a simple transformer that is equivalent to graph neural network where the message passing is done with

Phil Wang 49 Dec 28, 2022
Merlion: A Machine Learning Framework for Time Series Intelligence

Merlion: A Machine Learning Library for Time Series Table of Contents Introduction Installation Documentation Getting Started Anomaly Detection Foreca

Salesforce 2.8k Dec 30, 2022
Locally Differentially Private Distributed Deep Learning via Knowledge Distillation (LDP-DL)

Locally Differentially Private Distributed Deep Learning via Knowledge Distillation (LDP-DL) A preprint version of our paper: Link here This is a samp

Di Zhuang 3 Jan 08, 2023
OpenIPDM is a MATLAB open-source platform that stands for infrastructures probabilistic deterioration model

Open-Source Toolbox for Infrastructures Probabilistic Deterioration Modelling OpenIPDM is a MATLAB open-source platform that stands for infrastructure

CIVML 0 Jan 20, 2022
An official source code for "Augmentation-Free Self-Supervised Learning on Graphs"

Augmentation-Free Self-Supervised Learning on Graphs An official source code for Augmentation-Free Self-Supervised Learning on Graphs paper, accepted

Namkyeong Lee 59 Dec 01, 2022
TensorFlow tutorials and best practices.

Effective TensorFlow 2 Table of Contents Part I: TensorFlow 2 Fundamentals TensorFlow 2 Basics Broadcasting the good and the ugly Take advantage of th

Vahid Kazemi 8.7k Dec 31, 2022
GPU Programming with Julia - course at the Swiss National Supercomputing Centre (CSCS), ETH Zurich

Course Description The programming language Julia is being more and more adopted in High Performance Computing (HPC) due to its unique way to combine

Samuel Omlin 192 Jan 03, 2023
CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images

CurriculumNet Introduction This repo contains related code and models from the ECCV 2018 CurriculumNet paper. CurriculumNet is a new training strategy

156 Jul 04, 2022
ROS Basics and TurtleSim

Waypoint Follower Anna Garverick This package draws given waypoints, then waits for a service call with a start position to send the turtle to each wa

Anna Garverick 1 Dec 13, 2021
a Lightweight library for sequential learning agents, including reinforcement learning

SaLinA: SaLinA - A Flexible and Simple Library for Learning Sequential Agents (including Reinforcement Learning) TL;DR salina is a lightweight library

Facebook Research 405 Dec 17, 2022
Official PyTorch Implementation of Mask-aware IoU and maYOLACT Detector [BMVC2021]

The official implementation of Mask-aware IoU and maYOLACT detector. Our implementation is based on mmdetection. Mask-aware IoU for Anchor Assignment

Kemal Oksuz 46 Sep 29, 2022
SAGE: Sensitivity-guided Adaptive Learning Rate for Transformers

SAGE: Sensitivity-guided Adaptive Learning Rate for Transformers This repo contains our codes for the paper "No Parameters Left Behind: Sensitivity Gu

Chen Liang 23 Nov 07, 2022
Source for the paper "Universal Activation Function for machine learning"

Universal Activation Function Tensorflow and Pytorch source code for the paper Yuen, Brosnan, Minh Tu Hoang, Xiaodai Dong, and Tao Lu. "Universal acti

4 Dec 03, 2022
Continuous Conditional Random Field Convolution for Point Cloud Segmentation

CRFConv This repository is the implementation of "Continuous Conditional Random Field Convolution for Point Cloud Segmentation" 1. Setup 1) Building c

Fei Yang 8 Dec 08, 2022
This is the official implementation of Elaborative Rehearsal for Zero-shot Action Recognition (ICCV2021)

Elaborative Rehearsal for Zero-shot Action Recognition This is an official implementation of: Shizhe Chen and Dong Huang, Elaborative Rehearsal for Ze

DeLightCMU 26 Sep 24, 2022
Proto-RL: Reinforcement Learning with Prototypical Representations

Proto-RL: Reinforcement Learning with Prototypical Representations This is a PyTorch implementation of Proto-RL from Reinforcement Learning with Proto

Denis Yarats 74 Dec 06, 2022
FedGS: A Federated Group Synchronization Framework Implemented by LEAF-MX.

FedGS: Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoT Preparation For instructions on generating data, plea

Lizonghang 9 Dec 22, 2022
Official code of the paper "Expanding Low-Density Latent Regions for Open-Set Object Detection" (CVPR 2022)

OpenDet Expanding Low-Density Latent Regions for Open-Set Object Detection (CVPR2022) Jiaming Han, Yuqiang Ren, Jian Ding, Xingjia Pan, Ke Yan, Gui-So

csuhan 64 Jan 07, 2023
FlowTorch is a PyTorch library for learning and sampling from complex probability distributions using a class of methods called Normalizing Flows

FlowTorch is a PyTorch library for learning and sampling from complex probability distributions using a class of methods called Normalizing Flows.

Meta Incubator 272 Jan 02, 2023
Keras udrl - Keras implementation of Upside Down Reinforcement Learning

keras_udrl Keras implementation of Upside Down Reinforcement Learning This is me

Eder Santana 7 Jan 24, 2022