Search Results for author:
Found 9 papers, 3 papers with code
Solving the Tree Containment Problem Using Graph Neural Networks
In particular, we propose to combine the given network and the tree and apply a Graph Neural Network to this network-tree graph.
Shrink-Perturb Improves Architecture Mixing during Population Based Training for Neural Architecture Search
In this work, we show that simultaneously training and mixing neural networks is a promising way to conduct Neural Architecture Search (NAS).
Multi-Objective Population Based Training
Population Based Training (PBT) is an efficient hyperparameter optimization algorithm.
Data variation-aware medical image segmentation
In experiments with a real clinical dataset of CT scans with prostate segmentations, our approach provides an improvement of several percentage points in terms of Dice and surface Dice coefficients compared to when all network paths are trained on all training data.
Mixed-Block Neural Architecture Search for Medical Image Segmentation
The design of the best possible medical image segmentation DNNs, however, is task-specific.
Heed the Noise in Performance Evaluations in Neural Architecture Search
Due to stochastic factors in neural network initialization, training, and the chosen train/validation dataset split, the performance evaluation of a neural network architecture, which is often based on a single learning run, is also stochastic.
Parameterless Gene-pool Optimal Mixing Evolutionary Algorithms
When it comes to solving optimization problems with evolutionary algorithms (EAs) in a reliable and scalable manner, detecting and exploiting linkage information, i. e., dependencies between variables, can be key.
A Novel Surrogate-assisted Evolutionary Algorithm Applied to Partition-based Ensemble Learning
We propose a novel surrogate-assisted Evolutionary Algorithm for solving expensive combinatorial optimization problems.
Observer variation-aware medical image segmentation by combining deep learning and surrogate-assisted genetic algorithms
There has recently been great progress in automatic segmentation of medical images with deep learning algorithms.
