2 citations · 6 across the 7 of their papers we have counts for
8 papers · 1 filter
FAM: Relative Flatness Aware Minimization
Linara Adilova, Amr Abourayya, Jianning Li +5
Flatness of the loss curve around a model at hand has been shown to empirically correlate with its generalization ability. Optimizing for flatness has been proposed as early as 199…
Novelty Detection in Sequential Data by Informed Clustering and Modeling
Linara Adilova, Siming Chen, Michael Kamp
Novelty detection in discrete sequences is a challenging task, since deviations from the process generating the normal data are often small or intentionally hidden. Novelties can b…
Resource-Constrained On-Device Learning by Dynamic Averaging
Lukas Heppe, Michael Kamp, Linara Adilova +3
The communication between data-generating devices is partially responsible for a growing portion of the world's power consumption. Thus reducing communication is vital, both, from…
A Reparameterization-Invariant Flatness Measure for Deep Neural Networks
Henning Petzka, Linara Adilova, Michael Kamp +1
The performance of deep neural networks is often attributed to their automated, task-related feature construction. It remains an open question, though, why this leads to solutions…
Information-Theoretic Perspective of Federated Learning
Linara Adilova, Julia Rosenzweig, Michael Kamp
An approach to distributed machine learning is to train models on local datasets and aggregate these models into a single, stronger model. A popular instance of this form of parall…
Introducing Noise in Decentralized Training of Neural Networks
Linara Adilova, Nathalie Paul, Peter Schlicht
It has been shown that injecting noise into the neural network weights during the training process leads to a better generalization of the resulting model. Noise injection in the d…
Making Efficient Use of a Domain Expert's Time in Relation Extraction
Linara Adilova, Sven Giesselbach, Stefan Rüping
Scarcity of labeled data is one of the most frequent problems faced in machine learning. This is particularly true in relation extraction in text mining, where large corpora of tex…
Efficient Decentralized Deep Learning by Dynamic Model Averaging
Michael Kamp, Linara Adilova, Joachim Sicking +4
We propose an efficient protocol for decentralized training of deep neural networks from distributed data sources. The proposed protocol allows to handle different phases of model…