◍wovepaper
SearchResearchersInstitutions
Sign in
researcher

Markus H. Gross

13 papers hereh-index 243.1k citations59 works total

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

author position
  • middle author5
  • last author8

Across the 13 of 13 papers where every author was matched, so the position is known.

fields
  • cs.CV6
  • cs.LG3
  • eess.IV2
  • cs.CY1
  • stat.ML1
same name
  • Markus H. Gross — 1 paper, h 3
  • Markus H. Gross — 1 paper, h 5
  • Markus H. Gross — 1 paper, h 2
  • Markus H. Gross — 1 paper, h 5

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
20172023
most citedExplaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Values Approximation

48 citations · 109 across the 9 of their papers we have counts for

collaborators
Showing 2020Show all

4 papers · 1 filter

eess.IV2020★ 1 cited

Blind Image Restoration with Flow Based Priors

Leonhard Helminger, Michael Bernasconi, Abdelaziz Djelouah +2

Image restoration has seen great progress in the last years thanks to the advances in deep neural networks. Most of these existing techniques are trained using full supervision wit…

cs.CV2020★ 22 cited

Lossy Image Compression with Normalizing Flows

Leonhard Helminger, Abdelaziz Djelouah, Markus Gross +1

Deep learning based image compression has recently witnessed exciting progress and in some cases even managed to surpass transform coding based approaches that have been establishe…

cs.CV2020

Enriching Video Captions With Contextual Text

Philipp Rimle, Pelin Dogan, Markus Gross

Understanding video content and generating caption with context is an important and challenging task. Unlike prior methods that typically attempt to generate generic video captions…

cs.LG2020★ 6 cited

Shapley Value as Principled Metric for Structured Network Pruning

Marco Ancona, Cengiz Öztireli, Markus Gross

Structured pruning is a well-known technique to reduce the storage size and inference cost of neural networks. The usual pruning pipeline consists of ranking the network internal f…

◍wovepaper

Papers, researchers and institutions, woven together.

Explore
  • Search
  • Researchers
  • Institutions
Account
  • Library
  • Chat
Data
  • arXiv.org
  • Semantic Scholar
  • OpenAlex
  • Latest RSS
AboutContactPrivacyDevelopersllms.txtopenapi.json
Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.