38 citations · 57 across the 8 of their papers we have counts for
8 papers
Uncertainty-Aware Decomposed Hybrid Networks
Sina Ditzel, Achref Jaziri, Iuliia Pliushch +1
The robustness of image recognition algorithms remains a critical challenge, as current models often depend on large quantities of labeled data. In this paper, we propose a hybrid…
Representation Learning in a Decomposed Encoder Design for Bio-inspired Hebbian Learning
Achref Jaziri, Sina Ditzel, Iuliia Pliushch +1
Modern data-driven machine learning system designs exploit inductive biases in architectural structure, invariance and equivariance requirements, task-specific loss functions, and…
A Procedural World Generation Framework for Systematic Evaluation of Continual Learning
Timm Hess, Martin Mundt, Iuliia Pliushch +1
Several families of continual learning techniques have been proposed to alleviate catastrophic interference in deep neural network training on non-stationary data. However, a compr…
When Deep Classifiers Agree: Analyzing Correlations between Learning Order and Image Statistics
Iuliia Pliushch, Martin Mundt, Nicolas Lupp +1
Although a plethora of architectural variants for deep classification has been introduced over time, recent works have found empirical evidence towards similarities in their traini…
Neural Architecture Search of Deep Priors: Towards Continual Learning without Catastrophic Interference
Martin Mundt, Iuliia Pliushch, Visvanathan Ramesh
In this paper we analyze the classification performance of neural network structures without parametric inference. Making use of neural architecture search, we empirically demonstr…
A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World Learning
Martin Mundt, Yongwon Hong, Iuliia Pliushch +1
Current deep learning methods are regarded as favorable if they empirically perform well on dedicated test sets. This mentality is seamlessly reflected in the resurfacing area of c…