Jan Macdonald
YOU?
Author Swipe
View article: The EuroCropsML time series benchmark dataset for few-shot crop type classification in Europe
The EuroCropsML time series benchmark dataset for few-shot crop type classification in Europe Open
We introduce EuroCropsML , an analysis-ready remote sensing dataset based on the open-source EuroCrops collection, for machine learning (ML) benchmarking of time series crop type classification in Europe. It is the first time-resolved remo…
View article: Benchmarking for Practice: Few-Shot Time-Series Crop-Type Classification on the EuroCropsML Dataset
Benchmarking for Practice: Few-Shot Time-Series Crop-Type Classification on the EuroCropsML Dataset Open
Accurate crop-type classification from satellite time series is essential for agricultural monitoring. While various machine learning algorithms have been developed to enhance performance on data-scarce tasks, their evaluation often lacks …
Let's enhance: A deep learning approach to extreme deblurring of text images Open
This work presents a novel deep-learning-based pipeline for the inverse problem of image deblurring, leveraging augmentation and pre-training with synthetic data. Our results build on our winning submission to the recent Helsinki Deblur Ch…
Let's Enhance: A Deep Learning Approach to Extreme Deblurring of Text Images Open
This work presents a novel deep-learning-based pipeline for the inverse problem of image deblurring, leveraging augmentation and pre-training with synthetic data. Our results build on our winning submission to the recent Helsinki Deblur Ch…
Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning Open
This work is concerned with the following fundamental question in scientific machine learning: Can deep-learning-based methods solve noise-free inverse problems to near-perfect accuracy? Positive evidence is provided for the first time, fo…
Solving Inverse Problems With Deep Neural Networks – Robustness Included? Open
In the past five years, deep learning methods have become state-of-the-art in solving various inverse problems. Before such approaches can find application in safety-critical fields, a verification of their reliability appears mandatory. R…
Interpretable Neural Networks with Frank-Wolfe: Sparse Relevance Maps and Relevance Orderings Open
This repository provides the 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). We use a constrained optimi…
Interpretable Neural Networks with Frank-Wolfe: Sparse Relevance Maps\n and Relevance Orderings Open
We study the effects of constrained optimization formulations and Frank-Wolfe\nalgorithms for obtaining interpretable neural network predictions.\nReformulating the Rate-Distortion Explanations (RDE) method for relevance\nattribution as a …
AAPM DL-Sparse-View CT Challenge Submission Report: Designing an Iterative Network for Fanbeam-CT with Unknown Geometry Open
This report is dedicated to a short motivation and description of our contribution to the AAPM DL-Sparse-View CT Challenge (team name: "robust-and-stable"). The task is to recover breast model phantom images from limited view fanbeam measu…
The Computational Complexity of Understanding Binary Classifier Decisions Open
For a d-ary Boolean function Φ: {0, 1}d → {0, 1} and an assignment to its variables x = (x1, x2, . . . , xd) we consider the problem of finding those subsets of the variables that are sufficient to determine the function value with a given…
Solving Inverse Problems With Deep Neural Networks -- Robustness Included? Open
In the past five years, deep learning methods have become state-of-the-art in solving various inverse problems. Before such approaches can find application in safety-critical fields, a verification of their reliability appears mandatory. R…
Interval Neural Networks as Instability Detectors for Image\n Reconstructions Open
This work investigates the detection of instabilities that may occur when\nutilizing deep learning models for image reconstruction tasks. Although neural\nnetworks often empirically outperform traditional reconstruction methods, their\nusa…
Interval Neural Networks: Uncertainty Scores Open
We propose a fast, non-Bayesian method for producing uncertainty scores in the output of pre-trained deep neural networks (DNNs) using a data-driven interval propagating network. This interval neural network (INN) has interval valued param…
A Rate-Distortion Framework for Explaining Neural Network Decisions Open
We formalise the widespread idea of interpreting neural network decisions as an explicit optimisation problem in a rate-distortion framework. A set of input features is deemed relevant for a classification decision if the expected classifi…
The Computational Complexity of Understanding Network Decisions Open
For a Boolean function $Φ\colon\{0,1\}^d\to\{0,1\}$ and an assignment to its variables $\mathbf{x}=(x_1, x_2, \dots, x_d)$ we consider the problem of finding the subsets of the variables that are sufficient to determine the function value …
The Oracle of DLphi Open
We present a novel technique based on deep learning and set theory which yields exceptional classification and prediction results. Having access to a sufficiently large amount of labelled training data, our methodology is capable of predic…
Efficient Numerical Optimization For Susceptibility Artifact Correction Of EPI-MRI Open
We present two efficient numerical methods for susceptibility artifact correction applicable in Echo Planar Imaging (EPI), an ultra fast Magnetic Resonance Imaging (MRI) technique widely used in clinical applications. Both methods address …