Newt - a Gaussian process library in JAX.

Overview

Newt

                      __ \/_
                     (' \`\
                  _\, \ \\/ 
                   /`\/\ \\
                        \ \\    
                         \ \\/\/_
                         /\ \\'\
                       __\ `\\\
                        /|`  `\\
                               \\
                                \\
                                 \\    ,
                                  `---'  

Newt is a Gaussian process library built in JAX (with objax).

Newt currently provides the following models:

  • GPs
  • Sparse GPs
  • Markov GPs
  • Sparse Markov GPs

with the following inference methods:

  • Variational inference (with natural gradients)
  • Power expectation propagation
  • Laplace
  • Posterior linearisation (i.e. classical nonlinear Kalman smoothers)
  • Taylor (i.e. analytical linearisation / extended Kalman smoother)

Installation

In the top directory (Newt), run

pip install -e .

License

This software is provided under the Apache License 2.0. See the accompanying LICENSE file for details.

You might also like...
JAX-based neural network library

Haiku: Sonnet for JAX Overview | Why Haiku? | Quickstart | Installation | Examples | User manual | Documentation | Citing Haiku What is Haiku? Haiku i

Scenic: A Jax Library for Computer Vision and Beyond

Scenic Scenic is a codebase with a focus on research around attention-based models for computer vision. Scenic has been successfully used to develop c

A machine learning library for spiking neural networks. Supports training with both torch and jax pipelines, and deployment to neuromorphic hardware.
A machine learning library for spiking neural networks. Supports training with both torch and jax pipelines, and deployment to neuromorphic hardware.

Rockpool Rockpool is a Python package for developing signal processing applications with spiking neural networks. Rockpool allows you to build network

 JAXDL: JAX (Flax) Deep Learning Library
JAXDL: JAX (Flax) Deep Learning Library

JAXDL: JAX (Flax) Deep Learning Library Simple and clean JAX/Flax deep learning algorithm implementations: Soft-Actor-Critic (arXiv:1812.05905) Transf

A highly efficient and modular implementation of Gaussian Processes in PyTorch

GPyTorch GPyTorch is a Gaussian process library implemented using PyTorch. GPyTorch is designed for creating scalable, flexible, and modular Gaussian

A Python implementation of global optimization with gaussian processes.
A Python implementation of global optimization with gaussian processes.

Bayesian Optimization Pure Python implementation of bayesian global optimization with gaussian processes. PyPI (pip): $ pip install bayesian-optimizat

Supplementary code for the AISTATS 2021 paper
Supplementary code for the AISTATS 2021 paper "Matern Gaussian Processes on Graphs".

Matern Gaussian Processes on Graphs This repo provides an extension for gpflow with Matérn kernels, inducing variables and trainable models implemente

This is code to fit per-pixel environment map with spherical Gaussian lobes, using LBFGS optimization
This is code to fit per-pixel environment map with spherical Gaussian lobes, using LBFGS optimization

Spherical Gaussian Optimization This is code to fit per-pixel environment map with spherical Gaussian lobes, using LBFGS optimization. This code has b

This repository holds the code for the paper "Deep Conditional Gaussian Mixture Model forConstrained Clustering".

Deep Conditional Gaussian Mixture Model for Constrained Clustering. This repository holds the code for the paper Deep Conditional Gaussian Mixture Mod

Releases(v0.0)
Owner
AaltoML
Machine learning group at Aalto University lead by Prof. Solin
AaltoML
Locally Most Powerful Bayesian Test for Out-of-Distribution Detection using Deep Generative Models

LMPBT Supplementary code for the Paper entitled ``Locally Most Powerful Bayesian Test for Out-of-Distribution Detection using Deep Generative Models"

1 Sep 29, 2022
Measuring if attention is explanation with ROAR

NLP ROAR Interpretability Official code for: Evaluating the Faithfulness of Importance Measures in NLP by Recursively Masking Allegedly Important Toke

Andreas Madsen 19 Nov 13, 2022
Includes PyTorch -> Keras model porting code for ConvNeXt family of models with fine-tuning and inference notebooks.

ConvNeXt-TF This repository provides TensorFlow / Keras implementations of different ConvNeXt [1] variants. It also provides the TensorFlow / Keras mo

Sayak Paul 87 Dec 06, 2022
PyTorch implementation of "PatchGame: Learning to Signal Mid-level Patches in Referential Games" to appear in NeurIPS 2021

PatchGame: Learning to Signal Mid-level Patches in Referential Games This repository is the official implementation of the paper - "PatchGame: Learnin

Kamal Gupta 22 Mar 16, 2022
Is RobustBench/AutoAttack a suitable Benchmark for Adversarial Robustness?

Adversrial Machine Learning Benchmarks This code belongs to the papers: Is RobustBench/AutoAttack a suitable Benchmark for Adversarial Robustness? Det

Adversarial Machine Learning 9 Nov 27, 2022
RLMeta is a light-weight flexible framework for Distributed Reinforcement Learning Research.

RLMeta rlmeta - a flexible lightweight research framework for Distributed Reinforcement Learning based on PyTorch and moolib Installation To build fro

Meta Research 281 Dec 22, 2022
本项目是一个带有前端界面的垃圾分类项目,加载了训练好的模型参数,模型为efficientnetb4,暂时为40分类问题。

说明 本项目是一个带有前端界面的垃圾分类项目,加载了训练好的模型参数,模型为efficientnetb4,暂时为40分类问题。 python依赖 tf2.3 、cv2、numpy、pyqt5 pyqt5安装 pip install PyQt5 pip install PyQt5-tools 使用 程

4 May 04, 2022
End-to-end image segmentation kit based on PaddlePaddle.

English | 简体中文 PaddleSeg PaddleSeg has released the new version including the following features: Our team won the 6.2k Jan 02, 2023

Keras-retinanet - Keras implementation of RetinaNet object detection.

Keras RetinaNet Keras implementation of RetinaNet object detection as described in Focal Loss for Dense Object Detection by Tsung-Yi Lin, Priya Goyal,

Fizyr 4.3k Jan 01, 2023
The codes of paper 'Active-LATHE: An Active Learning Algorithm for Boosting the Error exponent for Learning Homogeneous Ising Trees'

Active-LATHE: An Active Learning Algorithm for Boosting the Error exponent for Learning Homogeneous Ising Trees This project contains the codes of pap

0 Apr 20, 2022
Visyerres sgdf woob - Modules Woob pour l'intranet et autres sites Scouts et Guides de France

Vis'Yerres SGDF - Modules Woob Vous avez le sentiment que l'intranet des Scouts

Thomas Touhey (pas un pseudonyme) 3 Dec 24, 2022
I-BERT: Integer-only BERT Quantization

I-BERT: Integer-only BERT Quantization HuggingFace Implementation I-BERT is also available in the master branch of HuggingFace! Visit the following li

Sehoon Kim 139 Dec 27, 2022
Simple sinc interpolation in PyTorch.

Kazane: simple sinc interpolation for 1D signal in PyTorch Kazane utilize FFT based convolution to provide fast sinc interpolation for 1D signal when

Chin-Yun Yu 10 May 03, 2022
Contains code for the paper "Vision Transformers are Robust Learners".

Vision Transformers are Robust Learners This repository contains the code for the paper Vision Transformers are Robust Learners by Sayak Paul* and Pin

Sayak Paul 103 Jan 05, 2023
A computer vision pipeline to identify the "icons" in Christian paintings

Christian-Iconography A computer vision pipeline to identify the "icons" in Christian paintings. A bit about iconography. Iconography is related to id

Rishab Mudliar 3 Jul 30, 2022
CS50x-AI - Artificial Intelligence with Python from Harvard University

CS50x-AI Artificial Intelligence with Python from Harvard University 📖 Table of

Hosein Damavandi 6 Aug 22, 2022
Supervised forecasting of sequential data in Python.

Supervised forecasting of sequential data in Python. Intro Supervised forecasting is the machine learning task of making predictions for sequential da

The Alan Turing Institute 54 Nov 15, 2022
Tutorial: Introduction to Graph Machine Learning, with Jupyter notebooks

GraphMLTutorialNLDL22 Tutorial NLDL22: Introduction to Graph Machine Learning, with Jupyter notebooks This tutorial takes place during the conference

UiT Machine Learning Group 3 Jan 10, 2022
Code in PyTorch for the convex combination linear IAF and the Householder Flow, J.M. Tomczak & M. Welling

VAE with Volume-Preserving Flows This is a PyTorch implementation of two volume-preserving flows as described in the following papers: Tomczak, J. M.,

Jakub Tomczak 87 Dec 26, 2022
[CVPR 2022 Oral] TubeDETR: Spatio-Temporal Video Grounding with Transformers

TubeDETR: Spatio-Temporal Video Grounding with Transformers Website • STVG Demo • Paper This repository provides the code for our paper. This includes

Antoine Yang 108 Dec 27, 2022