ViViT: Curvature access through the generalized Gauss-Newton's low-rank structure

Related tags

Deep Learningvivit
Overview

[ πŸ‘· πŸ— πŸ‘· πŸ— Coming soon! Official release with improved docs. Stay tuned. πŸ‘· πŸ— πŸ‘· πŸ— ]

ViViT: Curvature access through the generalized Gauss-Newton's low-rank structure

Python 3.7+ [tests]

ViViT is a collection of numerical tricks to efficiently access curvature from the generalized Gauss-Newton (GGN) matrix based on its low-rank structure. Provided functionality includes computing

  • GGN eigenvalues
  • GGN eigenpairs (eigenvalues + eigenvector)
  • 1Λ’α΅—- and 2ⁿᡈ-order directional derivatives along GGN eigenvectors
  • Newton steps

These operations can also further approximate the GGN to reduce cost via sub-sampling, Monte-Carlo approximation, and block-diagonal approximation.

How does it work? ViViT uses and extends BackPACK for PyTorch. The described functionality is realized through a combination of existing and new BackPACK extensions and hooks into its backpropagation.

Installation

πŸ‘· πŸ— πŸ‘· πŸ— The PyPI release is coming soon. πŸ‘· πŸ— πŸ‘· πŸ—

For now, you need to install from GitHub via

pip install vivit-for-pytorch@git+https://github.com/f-dangel/vivit.git#egg=vivit-for-pytorch

Examples

πŸ‘· πŸ— πŸ‘· πŸ— Coming soon! πŸ‘· πŸ— πŸ‘· πŸ—

How to cite

If you are using ViViT, consider citing the paper

@misc{dangel2022vivit,
      title={{ViViT}: Curvature access through the generalized Gauss-Newton's low-rank structure},
      author={Felix Dangel and Lukas Tatzel and Philipp Hennig},
      year={2022},
      eprint={2106.02624},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
Comments
  • [ADD] Warn about instabilities if eigenvalues are small

    [ADD] Warn about instabilities if eigenvalues are small

    The directional gradient computation and transformation of the Newton step from Gram space into parameter space require division by the square root of the direction's eigenvalue. This is unstable if the eigenvalue is close to zero.

    opened by f-dangel 1
  • [ADD] Clean `DirectionalDampedNewtonComputation`

    [ADD] Clean `DirectionalDampedNewtonComputation`

    Adds directionally damped Newton step computation with cleaned up API.

    • Fixes a bug in the eigenvalue criterion in the tests. It always picked one more eigenvalue than specified.
    opened by f-dangel 1
  • [DOC] Add NTK example

    [DOC] Add NTK example

    Adds an example inspired by the functorch tutorial on NTKs. It demonstrates how to use vivit to compute empirical NTK matrices and makes a comparison with the functorch implementation.

    opened by f-dangel 1
  • [ADD] Simplify `DirectionalDerivatives` API

    [ADD] Simplify `DirectionalDerivatives` API

    Exotic features, like using different GGNs to compute directions and directional curvatures, as well as full control of which intermediate buffers to keep, have been deprecated in favor of a simpler API.

    • Remove Newton step computation for now as it was internally relying on DirectionalDerivatives
    • Remove many utilities and associated tests from the exotic features
    • Forbid duplicate indices in subsampling
    • Always delete intermediate buffers other than the target quantities
    opened by f-dangel 1
  • [DOC] Set up `sphinx` and RTD

    [DOC] Set up `sphinx` and RTD

    This PR adds a scaffold for the doc at https://vivit.readthedocs.io/en/latest/. Code examples are integrated via sphinx-gallery (I added a preliminary logo). Pull requests are built by the CI.

    To build the docs, run make docs. You need to install the dependencies first, for example using pip install -e .[docs].

    opened by f-dangel 1
  • Calculate Parameter Space Values of GGN Eigenvectors

    Calculate Parameter Space Values of GGN Eigenvectors

    The docs show how to calculate the gram matrix eigenvectors and the paper articulates that to translate from 'gram space' to parameter space we just need to multiply by the 'V' matrix.

    What's the easiest way of implementing this?

    question 
    opened by lk-wq 1
  • Detect loss function's `reduction`, error if unsupported

    Detect loss function's `reduction`, error if unsupported

    For now, the library only supports reduction='mean'. We rely on the user to use this reduction and raise awareness about this point in the documentation. It would be better to automatically have the library detect the reduction and error if it is unsupported.

    This can be done via a hook into BackPACK.

    • [ ] Implement hook that determines the loss function reduction during backpropagation
    • [ ] Integrate the above hook into the *Computation and raise an exception if the reduction is not supported
    • [ ] Remove the comments about supported reductions in the documentation
    enhancement 
    opened by f-dangel 0
Releases(1.0.0)
Owner
Felix Dangel
Machine Learning PhD student at the University of TΓΌbingen and the Max Planck Institute for Intelligent Systems.
Felix Dangel
GitHub Repository https://arxiv.org/abs/2106.02624
Pipeline code for Sequential-GAM(Genome Architecture Mapping).

Sequential-GAM Pipeline code for Sequential-GAM(Genome Architecture Mapping). mapping whole_preprocess.sh include the whole processing of mapping. usa

3 Nov 03, 2022
Out-of-boundary View Synthesis towards Full-frame Video Stabilization

Out-of-boundary View Synthesis towards Full-frame Video Stabilization Introduction | Update | Results Demo | Introduction This repository contains the

25 Oct 10, 2022
The official implementation of EIGNN: Efficient Infinite-Depth Graph Neural Networks (NeurIPS 2021)

EIGNN: Efficient Infinite-Depth Graph Neural Networks The official implementation of EIGNN: Efficient Infinite-Depth Graph Neural Networks (NeurIPS 20

Juncheng Liu 14 Nov 22, 2022
Official implementation of Meta-StyleSpeech and StyleSpeech

Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation Dongchan Min, Dong Bok Lee, Eunho Yang, and Sung Ju Hwang This is an official code

min95 168 Dec 28, 2022
Hierarchical Metadata-Aware Document Categorization under Weak Supervision (WSDM'21)

Hierarchical Metadata-Aware Document Categorization under Weak Supervision This project provides a weakly supervised framework for hierarchical metada

Yu Zhang 53 Sep 17, 2022
Final term project for Bayesian Machine Learning Lecture (XAI-623)

Mixquality_AL Final Term Project For Bayesian Machine Learning Lecture (XAI-623) Youtube Link The presentation is given in YoutubeLink Problem Formula

JeongEun Park 3 Jan 18, 2022
Learning from graph data using Keras

Steps to run = Download the cora dataset from this link : https://linqs.soe.ucsc.edu/data unzip the files in the folder input/cora cd code python eda

Mansar Youness 64 Nov 16, 2022
BMVC 2021: This is the github repository for "Few Shot Temporal Action Localization using Query Adaptive Transformers" accepted in British Machine Vision Conference (BMVC) 2021, Virtual

FS-QAT: Few Shot Temporal Action Localization using Query Adaptive Transformer Accepted as Poster in BMVC 2021 This is an official implementation in P

Sauradip Nag 14 Dec 09, 2022
A minimal yet resourceful implementation of diffusion models (along with pretrained models + synthetic images for nine datasets)

A minimal yet resourceful implementation of diffusion models (along with pretrained models + synthetic images for nine datasets)

Vikash Sehwag 65 Dec 19, 2022
Project for tracking occupancy in Tel-Aviv parking lots.

Ahuzat Dibuk - Tracking occupancy in Tel-Aviv parking lots main.py This module was set-up to be executed on Google Cloud Platform. I run it every 15 m

Geva Kipper 35 Nov 22, 2022
Statistical and Algorithmic Investing Strategies for Everyone

Eiten - Algorithmic Investing Strategies for Everyone Eiten is an open source toolkit by Tradytics that implements various statistical and algorithmic

Tradytics 2.5k Jan 02, 2023
The official implementation of CircleNet: Anchor-free Detection with Circle Representation, MICCAI 2030

CircleNet: Anchor-free Detection with Circle Representation The official implementation of CircleNet, MICCAI 2020 [PyTorch] [project page] [MICCAI pap

The Biomedical Data Representation and Learning Lab 45 Nov 18, 2022
Evaluation toolkit of the informative tracking benchmark comprising 9 scenarios, 180 diverse videos, and new challenges.

Informative-tracking-benchmark Informative tracking benchmark (ITB) higher diversity. It contains 9 representative scenarios and 180 diverse videos. m

Xin Li 15 Nov 26, 2022
Tracking code for the winner of track 1 in the MMP-Tracking Challenge at ICCV 2021 Workshop.

Tracking Code for the winner of track1 in MMP-Trakcing challenge This repository contains our tracking code for the Multi-camera Multiple People Track

DamoCV 29 Nov 13, 2022
Clean and readable code for Decision Transformer: Reinforcement Learning via Sequence Modeling

Minimal implementation of Decision Transformer: Reinforcement Learning via Sequence Modeling in PyTorch for mujoco control tasks in OpenAI gym

Nikhil Barhate 104 Jan 06, 2023
Official Code Implementation of the paper : XAI for Transformers: Better Explanations through Conservative Propagation

Official Code Implementation of The Paper : XAI for Transformers: Better Explanations through Conservative Propagation For the SST-2 and IMDB expermin

Ameen Ali 23 Dec 30, 2022
Source code of our BMVC 2021 paper: AniFormer: Data-driven 3D Animation with Transformer

AniFormer This is the PyTorch implementation of our BMVC 2021 paper AniFormer: Data-driven 3D Animation with Transformer. Haoyu Chen, Hao Tang, Nicu S

24 Nov 02, 2022
FeTaQA: Free-form Table Question Answering

FeTaQA: Free-form Table Question Answering FeTaQA is a Free-form Table Question Answering dataset with 10K Wikipedia-based {table, question, free-form

Language, Information, and Learning at Yale 40 Dec 13, 2022
Model Zoo for AI Model Efficiency Toolkit

We provide a collection of popular neural network models and compare their floating point and quantized performance.

Qualcomm Innovation Center 137 Jan 03, 2023
The pytorch implementation of the paper "text-guided neural image inpainting" at MM'2020

TDANet: Text-Guided Neural Image Inpainting, MM'2020 (Oral) MM | ArXiv This repository implements the paper "Text-Guided Neural Image Inpainting" by L

LisaiZhang 75 Dec 22, 2022