Search Results for author:
Found 17 papers, 9 papers with code
Reducing Annotation Need in Self-Explanatory Models for Lung Nodule Diagnosis
Visualisation of the learned space further indicates that the correlation between the clustering of malignancy and nodule attributes coincides with clinical knowledge.
Learning Coulomb Diamonds in Large Quantum Dot Arrays
We introduce an algorithm that is able to find the facets of Coulomb diamonds in quantum dot arrays.
Estimation of Convex Polytopes for Automatic Discovery of Charge State Transitions in Quantum Dot Arrays
In spin based quantum dot arrays, material or fabrication imprecisions affect the behaviour of the device, which must be taken into account when controlling it.
Spot the Difference: Detection of Topological Changes via Geometric Alignment
As a first step towards solving such alignment problems, we propose an unsupervised algorithm for the detection of changes in image topology.
Quantifying Topology In Pancreatic Tubular Networks From Live Imaging 3D Microscopy
b) We provide a full deep-learning methodology for this difficult noisy task on time-series image data.
Semantic similarity metrics for learned image registration
We propose a semantic similarity metric for image registration.
Is segmentation uncertainty useful?
Probabilistic image segmentation encodes varying prediction confidence and inherent ambiguity in the segmentation problem.
Multimodal Variational Autoencoders for Semi-Supervised Learning: In Defense of Product-of-Experts
Multimodal generative models should be able to learn a meaningful latent representation that enables a coherent joint generation of all modalities (e. g., images and text).
A Loss Function for Generative Neural Networks Based on Watson’s Perceptual Model
To train Variational Autoencoders (VAEs) to generate realistic imagery requires a loss function that reflects human perception of image similarity.
DeepSim: Semantic similarity metrics for learned image registration
We propose a semantic similarity metric for image registration.
Convergence Analysis of the Hessian Estimation Evolution Strategy
The class of algorithms called Hessian Estimation Evolution Strategies (HE-ESs) update the covariance matrix of their sampling distribution by directly estimating the curvature of the objective function.
A Loss Function for Generative Neural Networks Based on Watson's Perceptual Model
To train Variational Autoencoders (VAEs) to generate realistic imagery requires a loss function that reflects human perception of image similarity.
The Hessian Estimation Evolution Strategy
We demonstrate that our approach to covariance matrix adaptation is efficient by evaluation it on the BBOB/COCO testbed.
Unsupervised-Learning of time-varying features
We present an architecture based on the conditional Variational Autoencoder to learn a representation of transformations in time-sequence data.
Convolutional neural networks for segmentation and object detection of human semen
We compare a set of convolutional neural network (CNN) architectures for the task of segmenting and detecting human sperm cells in an image taken from a semen sample.
CMA-ES with Optimal Covariance Update and Storage Complexity
The covariance matrix adaptation evolution strategy (CMA-ES) is arguably one of the most powerful real-valued derivative-free optimization algorithms, finding many applications in machine learning.
Population-Contrastive-Divergence: Does Consistency help with RBM training?
Compared to CD, it leads to a consistent estimate and may have a significantly lower bias.
