2 citations · 3 across the 7 of their papers we have counts for
7 papers
Data Pruning: Redundant, Problematic, and Interdependent Samples
Leon Freese, Marthinus W. Theunissen
The performance of deep learning models is affected by not only data quantity but also data quality. Data pruning is a process by which practitioners can reduce the size of a datas…
KnowIt: Deep Time Series Modeling and Interpretation
M. W. Theunissen, R. Rabe, H. L. Potgieter +1
KnowIt (Knowledge discovery in time series data) is a flexible framework for building deep time series models and interpreting them. It is implemented as a Python toolkit, with sou…
Is network fragmentation a useful complexity measure?
Coenraad Mouton, Randle Rabe, Daniël G. Haasbroek +3
It has been observed that the input space of deep neural network classifiers can exhibit `fragmentation', where the model function rapidly changes class as the input space is trave…
Input margins can predict generalization too
Coenraad Mouton, Marthinus W. Theunissen, Marelie H. Davel
Understanding generalization in deep neural networks is an active area of research. A promising avenue of exploration has been that of margin measurements: the shortest distance to…
The Missing Margin: How Sample Corruption Affects Distance to the Boundary in ANNs
Marthinus W. Theunissen, Coenraad Mouton, Marelie H. Davel
Classification margins are commonly used to estimate the generalization ability of machine learning models. We present an empirical study of these margins in artificial neural netw…
Pre-interpolation loss behaviour in neural networks
Arthur E. W. Venter, Marthinus W. Theunissen, Marelie H. Davel
When training neural networks as classifiers, it is common to observe an increase in average test loss while still maintaining or improving the overall classification accuracy on t…
DNNs as Layers of Cooperating Classifiers
Marelie H. Davel, Marthinus W. Theunissen, Arnold M. Pretorius +1
A robust theoretical framework that can describe and predict the generalization ability of deep neural networks (DNNs) in general circumstances remains elusive. Classical attempts…