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Cordelia Schmid

108 papers hereh-index 153124k citations466 works total

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

author position
  • middle author49
  • last author58

Across the 107 of 108 papers where every author was matched, so the position is known.

fields
  • cs.CV92
  • cs.LG6
  • cs.RO5
  • stat.ML4
  • cs.CL1
same name
  • Cordelia Schmid — 12 papers, h 7
  • Cordelia Schmid — 11 papers
  • Cordelia Schmid — 11 papers, h 7
  • Cordelia Schmid — 9 papers, h 7
  • Cordelia Schmid — 6 papers, h 2
  • Cordelia Schmid — 5 papers, h 2

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
20152024
most citedTNT: Target-driveN Trajectory Prediction

211 citations · 567 across the 48 of their papers we have counts for

collaborators
Showing stat.MLShow all

4 papers · 1 filter

stat.ML2020★ 33 cited

Radioactive data: tracing through training

Alexandre Sablayrolles, Matthijs Douze, Cordelia Schmid +1

We want to detect whether a particular image dataset has been used to train a model. We propose a new technique, \emph{radioactive data}, that makes imperceptible changes to this d…

stat.ML2019

White-box vs Black-box: Bayes Optimal Strategies for Membership Inference

Alexandre Sablayrolles, Matthijs Douze, Yann Ollivier +2

Membership inference determines, given a sample and trained parameters of a machine learning model, whether the sample was part of the training set. In this paper, we derive the op…

stat.ML2018

Spreading vectors for similarity search

Alexandre Sablayrolles, Matthijs Douze, Cordelia Schmid +1

Discretizing multi-dimensional data distributions is a fundamental step of modern indexing methods. State-of-the-art techniques learn parameters of quantizers on training data for…

stat.ML2018

Unsupervised Learning of Artistic Styles with Archetypal Style Analysis

Daan Wynen, Cordelia Schmid, Julien Mairal

In this paper, we introduce an unsupervised learning approach to automatically discover, summarize, and manipulate artistic styles from large collections of paintings. Our method i…

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