Deep Learning with Ensembles of Neural Networks
Typ
Examensarbete för masterexamen
Program
Complex adaptive systems (MPCAS), MSc
Publicerad
2020
Författare
Yang, Henry
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Deep neural networks are powerful machine-learning models that excel at a large array
of machine-learning tasks. A major challenge in machine-learning is the problem
of assessing the uncertainty of deep-learning predictors. Coupled with this problem
is that deep neural networks tend to make overconfident predictions, which can
lead to incorrect decision making where errors go unnoticed. A number of schemes
for uncertainty detection have been proposed in recent years ranging from using
Bayesian methodology and Monte Carlo simulations to reading neuron states in the
upstream layers and letting the neural-networks learn to recognize its certainty. In
this thesis, we analyze one of the proposed methods that estimates the predictive
uncertainty of deep learning algorithms using an ensemble of deep neural networks.
Using classification of handwritten digits as the reference problem, we demonstrate
that this method is effective at assessing predictive uncertainty when faced with outof-
distribution inputs and inputs that are distorted by deformation and noise. Our
results demonstrated that the distribution of the estimated predictive uncertainty
differs substantially between correctly and incorrectly classified inputs, indicating
that this method can be used to predict incorrect decision making.
Beskrivning
Ämne/nyckelord
Deep Learning , Neural Networks , Ensemble Learning , Machine Learning , Entropy , Predictive Uncertainty , Classification , CNN , Perceptrons