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J. Macdonald

6 papers hereh-index 8380 citations21 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author2
  • middle author4

Across the 6 of 6 papers where every author was matched, so the position is known.

fields
  • cs.LG4
  • cs.CC1
  • eess.IV1
same name
  • J. MacDonald — 13 papers, h 27
  • J. Macdonald — 8 papers, h 6
  • J. Macdonald — 6 papers, h 24
  • J. MacDonald — 2 papers, h 5
  • J. Macdonald — 2 papers, h 41
  • J. Macdonald — 2 papers, h 20

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20192021
most citedA Rate-Distortion Framework for Explaining Neural Network Decisions

15 citations · 17 across the 4 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2021

AAPM DL-Sparse-View CT Challenge Submission Report: Designing an Iterative Network for Fanbeam-CT with Unknown Geometry

Martin Genzel, Jan Macdonald, Maximilian März

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…

cs.LG2020

Interval Neural Networks: Uncertainty Scores

Luis Oala, Cosmas Heiß, Jan Macdonald +3

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. T…

cs.LG2019★ 15 cited

A Rate-Distortion Framework for Explaining Neural Network Decisions

Jan Macdonald, Stephan Wäldchen, Sascha Hauch +1

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 rele…

cs.LG2019

The Oracle of DLphi

Dominik Alfke, Weston Baines, Jan Blechschmidt +24

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 l…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.