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Róbert Ormándi

Researcher at University of Szeged

Publications -  27
Citations -  770

Róbert Ormándi is an academic researcher from University of Szeged. The author has contributed to research in topics: Stochastic gradient descent & Online machine learning. The author has an hindex of 10, co-authored 27 publications receiving 610 citations. Previous affiliations of Róbert Ormándi include Google & Hungarian Academy of Sciences.

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Large scale distributed neural network training through online distillation

Rohan Anil, +5 more
- 09 Apr 2018 - 
TL;DR: This paper claims that online distillation is a cost-effective way to make the exact predictions of a model dramatically more reproducible and can still speed up training even after the authors have already reached the point at which additional parallelism provides no benefit for synchronous or asynchronous stochastic gradient descent.
Journal ArticleDOI

Gossip learning with linear models on fully distributed data

Róbert Ormándi, +2 more
- 01 Feb 2013 - 
TL;DR: This work proposes gossip learning, a generic approach that is based on multiple models taking random walks over the network in parallel, while applying an online learning algorithm to improve themselves, and getting combined via ensemble learning methods.
Proceedings Article

Gossip-based distributed stochastic bandit algorithms

Balázs Szörényi, +7 more
TL;DR: This work shows that the probability of playing a suboptimal arm at a peer in iteration t = Ω(log N) is proportional to 1/(Nt) where N denotes the number of peers participating in the network.
Proceedings Article

Large scale distributed neural network training through online distillation

Rohan Anil, +5 more
TL;DR: In this paper, the authors explore a variant of distillation which is relatively straightforward to use as it does not require a complicated multi-stage setup or many new hyperparameters.
Journal ArticleDOI

Semi-automated Construction of Decision Rules to Predict Morbidities from Clinical Texts

Richárd Farkas, +8 more
- 01 Jul 2009 - 
TL;DR: The results demonstrate the feasibility of the authors approach and show that even very simple systems with a shallow linguistic analysis can achieve remarkable accuracy scores for classifying clinical records on a limited set of concepts.