Search Results for author: Julian Stier

Found 6 papers, 3 papers with code

GRAN is superior to GraphRNN: node orderings, kernel- and graph embeddings-based metrics for graph generators

1 code implementation13 Jul 2023 Ousmane Touat, Julian Stier, Pierre-Edouard Portier, Michael Granitzer

We use these metrics to compare GraphRNN and GRAN, two well-known generative models for graphs, and unveil the influence of node orderings.

Drug Discovery Graph Embedding +2

deepstruct -- linking deep learning and graph theory

no code implementations12 Nov 2021 Julian Stier, Michael Granitzer

deepstruct connects deep learning models and graph theory such that different graph structures can be imposed on neural networks or graph structures can be extracted from trained neural network models.

Neural Architecture Search

Experiments on Properties of Hidden Structures of Sparse Neural Networks

1 code implementation27 Jul 2021 Julian Stier, Harshil Darji, Michael Granitzer

Sparsity in the structure of Neural Networks can lead to less energy consumption, less memory usage, faster computation times on convenient hardware, and automated machine learning.

Neural Architecture Search

DeepGG: a Deep Graph Generator

1 code implementation7 Jun 2020 Julian Stier, Michael Granitzer

Learning distributions of graphs can be used for automatic drug discovery, molecular design, complex network analysis, and much more.

Drug Discovery Graph Embedding

Structural Analysis of Sparse Neural Networks

no code implementations16 Oct 2019 Julian Stier, Michael Granitzer

Sparse Neural Networks regained attention due to their potential for mathematical and computational advantages.

Neural Architecture Search

Analysing Neural Network Topologies: a Game Theoretic Approach

no code implementations17 Apr 2019 Julian Stier, Gabriele Gianini, Michael Granitzer, Konstantin Ziegler

In previous work, heuristics based on using the weight distribution of a neuron as contribution measure have shown some success, but do not provide a proper theoretical understanding.

Network Pruning

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