Codes for 'Dual Parameterization of Sparse Variational Gaussian Processes'

Related tags

Deep Learningt-SVGP
Overview

Dual Parameterization of Sparse Variational Gaussian Processes

Quality checks and Tests Docs build

Documentation | Notebooks | API reference

Introduction

This repository is the official implementation of the methods in the publication:

  • V. Adam, P.E. Chang, M.E. Khan, and A. Solin (2021). Dual Parameterization of Sparse Variational Gaussian Processes. In Advances in Neural Information Processing Systems (NeurIPS). [arXiv]

The paper's main result shows that an alternative (dual) parameterization for SVGP models leads to a better objective for learning and allows for faster inference via natural gradient descent.

Repository structure

The repository has the following folder structure:

  • scr contains the source code
  • experiments contains scripts to reproduce some of the experiments presented in the paper
  • docs contains documentation in the form of notebooks and an api reference.
  • tests contains unit and integration tests for the source code

Installation

We recommend using Python version 3.7.3 and pip version 20.1.1. To install the package, run:

pip install -e .

To run the tests, notebooks, build the docs or run the experiments, install the dependencies:

pip install \
  -r tests_requirements.txt \
  -r notebook_requirements.txt \
  -r docs/docs_requirements.txt \
  -e .

Notebooks

To build the notebooks from source, use jupytext:

jupytext --to notebook [filename].py

Citation

If you use the code in this repository for your research, please cite the paper as follows:

@inproceedings{adam2021dual,
  title={Dual Parameterization of Sparse Variational {G}aussian Processes},
  author={Adam, Vincent and Chang, Paul Edmund and Khan, Mohammad Emtiyaz and Solin, Arno},
  booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
  year={2021}
}

Contributing

For all correspondence, please contact [email protected].

License

This software is provided under the MIT license.

Owner
AaltoML
Machine learning group at Aalto University lead by Prof. Solin
AaltoML
OpenMMLab Image Classification Toolbox and Benchmark

Introduction English | 简体中文 MMClassification is an open source image classification toolbox based on PyTorch. It is a part of the OpenMMLab project. D

OpenMMLab 1.8k Jan 03, 2023
This repository contains the needed resources to build the HIRID-ICU-Benchmark dataset

HiRID-ICU-Benchmark This repository contains the needed resources to build the HIRID-ICU-Benchmark dataset for which the manuscript can be found here.

Biomedical Informatics at ETH Zurich 30 Dec 16, 2022
Implementation of Axial attention - attending to multi-dimensional data efficiently

Axial Attention Implementation of Axial attention in Pytorch. A simple but powerful technique to attend to multi-dimensional data efficiently. It has

Phil Wang 250 Dec 25, 2022
The code repository for EMNLP 2021 paper "Vision Guided Generative Pre-trained Language Models for Multimodal Abstractive Summarization".

Vision Guided Generative Pre-trained Language Models for Multimodal Abstractive Summarization [Paper] accepted at the EMNLP 2021: Vision Guided Genera

CAiRE 42 Jan 07, 2023
Neighborhood Reconstructing Autoencoders

Neighborhood Reconstructing Autoencoders The official repository for Neighborhood Reconstructing Autoencoders (Lee, Kwon, and Park, NeurIPS 2021). T

Yonghyeon Lee 24 Dec 14, 2022
Geometric Vector Perceptron --- a rotation-equivariant GNN for learning from biomolecular structure

Geometric Vector Perceptron Code to accompany Learning from Protein Structure with Geometric Vector Perceptrons by B Jing, S Eismann, P Suriana, RJL T

Dror Lab 85 Dec 29, 2022
[ICCV2021] Safety-aware Motion Prediction with Unseen Vehicles for Autonomous Driving

Safety-aware Motion Prediction with Unseen Vehicles for Autonomous Driving Safety-aware Motion Prediction with Unseen Vehicles for Autonomous Driving

Xuanchi Ren 44 Dec 03, 2022
Reference implementation for Deep Unsupervised Learning using Nonequilibrium Thermodynamics

Diffusion Probabilistic Models This repository provides a reference implementation of the method described in the paper: Deep Unsupervised Learning us

Jascha Sohl-Dickstein 238 Jan 02, 2023
Official Implementation of "Tracking Grow-Finish Pigs Across Large Pens Using Multiple Cameras"

Multi Camera Pig Tracking Official Implementation of Tracking Grow-Finish Pigs Across Large Pens Using Multiple Cameras CVPR2021 CV4Animals Workshop P

44 Jan 06, 2023
Barlow Twins and HSIC

Barlow Twins and HSIC Unofficial Pytorch implementation for Barlow Twins and HSIC_SSL on small datasets (CIFAR10, STL10, and Tiny ImageNet). Correspon

Yao-Hung Hubert Tsai 49 Nov 24, 2022
Mind the Trade-off: Debiasing NLU Models without Degrading the In-distribution Performance

Models for natural language understanding (NLU) tasks often rely on the idiosyncratic biases of the dataset, which make them brittle against test cases outside the training distribution.

Ubiquitous Knowledge Processing Lab 22 Jan 02, 2023
Efficient 3D Backbone Network for Temporal Modeling

VoV3D is an efficient and effective 3D backbone network for temporal modeling implemented on top of PySlowFast. Diverse Temporal Aggregation and

102 Dec 06, 2022
Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm

Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetu

3 Dec 05, 2022
LUKE -- Language Understanding with Knowledge-based Embeddings

LUKE (Language Understanding with Knowledge-based Embeddings) is a new pre-trained contextualized representation of words and entities based on transf

Studio Ousia 587 Dec 30, 2022
PyTorch 1.0 inference in C++ on Windows10 platforms

Serving PyTorch Models in C++ on Windows10 platforms How to use Prepare Data examples/data/train/ - 0 - 1 . . . - n examples/data/test/

Henson 88 Oct 15, 2022
Implementation of the GVP-Transformer, which was used in the paper "Learning inverse folding from millions of predicted structures" for de novo protein design alongside Alphafold2

GVP Transformer (wip) Implementation of the GVP-Transformer, which was used in the paper Learning inverse folding from millions of predicted structure

Phil Wang 19 May 06, 2022
Official implementation for ICDAR 2021 paper "Handwritten Mathematical Expression Recognition with Bidirectionally Trained Transformer"

Handwritten Mathematical Expression Recognition with Bidirectionally Trained Transformer Description Convert offline handwritten mathematical expressi

Wenqi Zhao 87 Dec 27, 2022
Code for Fully Context-Aware Image Inpainting with a Learned Semantic Pyramid

SPN: Fully Context-Aware Image Inpainting with a Learned Semantic Pyramid Code for Fully Context-Aware Image Inpainting with a Learned Semantic Pyrami

12 Jun 27, 2022
Train emoji embeddings based on emoji descriptions.

emoji2vec This is my attempt to train, visualize and evaluate emoji embeddings as presented by Ben Eisner, Tim Rocktäschel, Isabelle Augenstein, Matko

Miruna Pislar 17 Sep 03, 2022
Python code for the paper How to scale hyperparameters for quickshift image segmentation

How to scale hyperparameters for quickshift image segmentation Python code for the paper How to scale hyperparameters for quickshift image segmentatio

0 Jan 25, 2022