Search Results for author:
Found 9 papers, 7 papers with code
Sparsified Model Zoo Twins: Investigating Populations of Sparsified Neural Network Models
With this paper, we address that gap by applying two popular sparsification methods on populations of models (so called model zoos) to create sparsified versions of the original zoos.
Towards dynamic stability analysis of sustainable power grids using graph neural networks
To mitigate climate change, the share of renewable needs to be increased.
Hyper-Representations as Generative Models: Sampling Unseen Neural Network Weights
Learning representations of neural network weights given a model zoo is an emerging and challenging area with many potential applications from model inspection, to neural architecture search or knowledge distillation.
Model Zoos: A Dataset of Diverse Populations of Neural Network Models
With this work, we publish a novel dataset of model zoos containing systematically generated and diverse populations of NN models for further research.
Hyper-Representations for Pre-Training and Transfer Learning
Learning representations of neural network weights given a model zoo is an emerging and challenging area with many potential applications from model inspection, to neural architecture search or knowledge distillation.
Toward Dynamic Stability Assessment of Power Grid Topologies using Graph Neural Networks
As a testbed for GNN models, we generate new, large datasets of dynamic stability of synthetic power grids, and provide them as an open-source resource to the research community.
Hyper-Representations: Self-Supervised Representation Learning on Neural Network Weights for Model Characteristic Prediction
Self-Supervised Learning (SSL) has been shown to learn useful and information-preserving representations.
Predicting Basin Stability of Power Grids using Graph Neural Networks
We investigate the feasibility of applying graph neural networks (GNN) to predict dynamic stability of synchronisation in complex power grids using the single-node basin stability (SNBS) as a measure.
An Investigation of the Weight Space to Monitor the Training Progress of Neural Networks
We show that the model trajectories can be separated and the order of checkpoints on the trajectories recovered, which may serve as a first step towards DNN model versioning.
