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Marcus Rohrbach

TU Darmstadt

66 papers hereh-index 6739k citations114 works total

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

author position
  • sole author1
  • first author1
  • middle author47
  • last author17

Across the 66 of 66 papers where every author was matched, so the position is known.

fields
  • cs.CV57
  • cs.LG5
  • cs.CL2
  • cs.AI1
  • stat.ML1
affiliations
  • TU Darmstadt
Homepage
same name
  • Marcus Rohrbach — 17 papers, h 4
  • Marcus Rohrbach — 8 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
20142023
most citedEfficient Lifelong Learning with A-GEM

580 citations · 3.8k across the 56 of their papers we have counts for

collaborators
Showing 2019 · cs.LGShow all

4 papers · 2 filters

cs.LG2019★ 26 cited

Uncertainty-guided Continual Learning with Bayesian Neural Networks

Sayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell +1

Continual learning aims to learn new tasks without forgetting previously learned ones. This is especially challenging when one cannot access data from previous tasks and when the m…

cs.LG2019★ 327 cited

On Tiny Episodic Memories in Continual Learning

Arslan Chaudhry, Marcus Rohrbach, Mohamed Elhoseiny +4

In continual learning (CL), an agent learns from a stream of tasks leveraging prior experience to transfer knowledge to future tasks. It is an ideal framework to decrease the amoun…

cs.LG2019★ 33 cited

Probabilistic Neural-symbolic Models for Interpretable Visual Question Answering

Ramakrishna Vedantam, Karan Desai, Stefan Lee +3

We propose a new class of probabilistic neural-symbolic models, that have symbolic functional programs as a latent, stochastic variable. Instantiated in the context of visual quest…

cs.LG2019★ 580 cited

Efficient Lifelong Learning with A-GEM

Arslan Chaudhry, Marc'Aurelio Ranzato, Marcus Rohrbach +1

In lifelong learning, the learner is presented with a sequence of tasks, incrementally building a data-driven prior which may be leveraged to speed up learning of a new task. In th…

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