10.9k citations
- Max Planck SocietyDE155 papers
- Heidelberg UniversityDE52 papers
- Centre National de la Recherche ScientifiqueFR46 papers
- Jagiellonian UniversityPL44 papers
- Charles UniversityCZ40 papers
- Polish Academy of SciencesPL40 papers
- Humboldt-Universität zu BerlinDE39 papers
- European Organization for Nuclear ResearchCH38 papers
- Institut National de Physique Nucléaire et de Physique des ParticulesFR37 papers
- University of BirminghamGB37 papers
- University of OxfordGB36 papers
- Rutherford Appleton LaboratoryGB35 papers
6 papers · 1 filter
More Powerful Selective Kernel Tests for Feature Selection
Jen Ning Lim, Makoto Yamada, Wittawat Jitkrittum +3
Refining one's hypotheses in the light of data is a common scientific practice; however, the dependency on the data introduces selection bias and can lead to specious statistical a…
Uncertainty Based Detection and Relabeling of Noisy Image Labels
Jan M. Köhler, Maximilian Autenrieth, William H. Beluch
Deep neural networks (DNNs) are powerful tools in computer vision tasks. However, in many realistic scenarios label noise is prevalent in the training images, and overfitting to th…
CITE: A Corpus of Image-Text Discourse Relations
Malihe Alikhani, Sreyasi Nag Chowdhury, Gerard de Melo +1
This paper presents a novel crowd-sourced resource for multimodal discourse: our resource characterizes inferences in image-text contexts in the domain of cooking recipes in the fo…
Evaluating Style Transfer for Text
Remi Mir, Bjarke Felbo, Nick Obradovich +1
Research in the area of style transfer for text is currently bottlenecked by a lack of standard evaluation practices. This paper aims to alleviate this issue by experimentally iden…
4MOST: Project overview and information for the First Call for Proposals
R. S. de Jong, O. Agertz, A. Agudo Berbel +335
We introduce the 4-metre Multi-Object Spectroscopic Telescope (4MOST), a new high-multiplex, wide-field spectroscopic survey facility under development for the four-metre-class Vis…
Noise2Self: Blind Denoising by Self-Supervision
Joshua Batson, Loic Royer
We propose a general framework for denoising high-dimensional measurements which requires no prior on the signal, no estimate of the noise, and no clean training data. The only ass…