ZIB-IOL/fw-rde ZIB-IOL/fw-rde

  • Stars
    star
    4
  • Rank 3,304,323 (Top 66 %)
  • Language
    Python
  • License
    MIT License
  • Created about 3 years ago
  • Updated about 1 year ago
Twitter logo Share LinkedIn logo Share Facebook logo Share
ZIB-IOL/fw-rde
github

ZIB-IOL

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

Official implementation of the paper "Interpretable Neural Networks with Frank-Wolfe: Sparse Relevance Maps and Relevance Orderings" by J. Macdonald, M. Besançon, and S. Pokutta (2021).

More Repositories

1

FrankWolfe.jl

Julia implementation for various Frank-Wolfe and Conditional Gradient variants
Julia
92
star
2

Boscia.jl

Mixed-Integer Convex Programming: Branch-and-bound with Frank-Wolfe-based convex relaxations
Julia
22
star
3

CINDy

CINDy: Conditional gradient-based Identification of Non-linear Dynamics – Noise-robust recovery
Python
8
star
4

StochasticFrankWolfe

Python
7
star
5

FrankWolfe-book-code

Python implementation of Frank-Wolfe and Conditional Gradient algorithms
Python
2
star
6

AAPPR.jl

Accelerated and Sparse Algorithms for Approximate Personalized PageRank and Beyond
Julia
1
star
7

KernelHerding.jl

A package demonstrating Kernel Herding with Frank-Wolfe algorithms
Julia
1
star
8

AVI

approximate vanishing ideal computations
Julia
1
star
9

merlin-arthur-classifiers

Implementation of Merlin-Arthur-Classifier Framework presented at AISTATS24.
Python
1
star
10

cgavi

Code for the paper: Wirth, E.S. and Pokutta, S., 2022, May. Conditional gradients for the approximately vanishing ideal. In International Conference on Artificial Intelligence and Statistics (pp. 2191-2209). PMLR.
Python
1
star
11

IMP

Back to Basics: Efficient Network Compression via IMP
Python
1
star
12

open_loop_fw

Code for the paper: Wirth, E., Pokutta, S., and Kerdreux, T. (2023). Acceleration of Frank-Wolfe Algorithms with Open-Loop Step-Sizes. To Appear in Proceedings of AISTATS.
Python
1
star