An up-to-date list is available on Google Scholar.
refereed articles
2021
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On the Importance of Regularisation and Auxiliary
Information in OOD Detection
In International Conference of Neural Information
Processing, (ICONIP), 2021
Neural networks are often utilised in critical
domain applications (e.g. self-driving cars,
financial markets, and aerospace engineering), even
though they exhibit overconfident predictions for
ambiguous inputs. This deficiency demonstrates a
fundamental flaw indicating that neural networks
often overfit on spurious correlations. To address
this problem in this work we present two novel
objectives that improve the ability of a network to
detect out-of-distribution samples and therefore
avoid overconfident predictions for ambiguous
inputs. We empirically demonstrate that our methods
outperform the baseline and perform better than the
majority of existing approaches, while performing
competitively those that they don’t
outperform. Additionally, we empirically demonstrate
the robustness of our approach against common
corruptions and demonstrate the importance of
regularisation and auxiliary information in
out-of-distribution detection.
2020
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Ramifications of Approximate Posterior Inference for
Bayesian Deep Learning in Adversarial and
Out-of-Distribution Settings
In Proceedings of Computer Vision - (ECCV) Workshops, 2020
Deep neural networks have been successful in diverse
discriminative classification tasks, although, they
are poorly calibrated often assigning high
probability to misclassified predictions. Potential
consequences could lead to trustworthiness and
accountability of the models when deployed in real
applications, where predictions are evaluated based
on their confidence scores. Existing solutions
suggest the benefits attained by combining deep
neural networks and Bayesian inference to quantify
uncertainty over the models’ predictions for
ambiguous datapoints. In this work we propose to
validate and test the efficacy of likelihood based
models in the task of out of distribution detection
(OoD). Across different datasets and metrics we show
that Bayesian deep learning models on certain
occasions marginally outperform conventional neural
networks and in the event of minimal overlap between
in/out distribution classes, even the best models
exhibit a reduction in AUC scores in detecting OoD
data. Preliminary investigations indicate the
potential inherent role of bias due to choices of
initialisation, architecture or activation
functions. We hypothesise that the sensitivity of
neural networks to unseen inputs could be a
multi-factor phenomenon arising from the different
architectural design choices often amplified by the
curse of dimensionality. Furthermore, we perform a
study to find the effect of the adversarial noise
resistance methods on in and out-of-distribution
performance, as well as, also investigate
adversarial noise robustness of Bayesian deep
learners.
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A Comparison of Bayesian Deep Learning for Out of
Distribution Detection and Uncertainty Estimation
In Proceedings of the 37th International Conference of
Machine Learning - (ICML) Workshops, 2020
2019
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On the Validity of Bayesian Neural Networks for
Uncertainty Estimation
In Proceedings of the 27th Irish Conference on
Artificial Intelligence and Cognitive Science -
(AICS), 2019
Deep neural networks (DNN) are versatile parametric
models utilised successfully in a diverse number of
tasks and domains. However, they have
limitations—particularly from their lack of
robustness and over-sensitivity to out of
distribution samples. Bayesian Neural Networks, due
to their formulation under the Bayesian framework,
provide a principled approach to building neural
networks that address these limitations. This paper
describes a study that empirically evaluates and
compares Bayesian Neural Networks to their
equivalent point estimate Deep Neural Networks to
quantify the predictive uncertainty induced by their
parameters, as well as their performance in view of
this uncertainty. In this study, we evaluated and
compared three point estimate deep neural networks
against comparable Bayesian neural network
alternatives using two well-known benchmark image
classification datasets (CIFAR-10 and SVHN).
-
A Categorisation of Post-hoc Explanations for
Predictive Models
In Association for the Advancement of Artificial
Intelligence - (AAAI) Spring Symposia on
Story-Enabled Intelligence, 2019
The ubiquity of machine learning based predictive
models in modern society naturally leads people to
ask how trustworthy those models are? In predictive
modeling, it is quite common to induce a trade-off
between accuracy and interpretability. For instance,
doctors would like to know how effective some
treatment will be for a patient or why the model
suggested a particular medication for a patient
exhibiting those symptoms? We acknowledge that the
necessity for interpretability is a consequence of
an incomplete formalisation of the problem, or more
precisely of multiple meanings adhered to a
particular concept. For certain problems, it is not
enough to get the answer (what), the model also has
to provide an explanation of how it came to that
conclusion (why), because a correct prediction, only
partially solves the original problem. In this
article we extend existing categorisation of
techniques to aid model interpretability and test
this categorisation.
2018
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Denoising Dictionary Learning Against Perturbations
Mitros, John, Bridge, Derek, and Prestwich, Steven
In Proceedings of the 32nd Association for the
Advancement of Artificial Intelligence - (AAAI)
Workshops, 2018
We propose denoising dictionary learning (DDL), a
simple yet effective technique as a protection
measure against adversarial perturbations. We
examined denoising dictionary learning on MNIST and
CIFAR10 perturbed under two different perturbation
techniques, fast gradient sign (FGSM) and jacobian
saliency maps (JSMA). We evaluated it against five
different deep neural networks (DNN) representing
the building blocks of most recent architectures
indicating a successive progression of model
complexity of each other. We show that each model
tends to capture different representations based on
their architecture. For each model we recorded its
accuracy both on the perturbed test data previously
misclassified with high confidence and on the
denoised one after the reconstruction using
dictionary learning. The reconstruction quality of
each data point is assessed by means of PSNR (Peak
Signal to Noise Ratio) and Structure Similarity
Index (SSI). We show that after applying (DDL) the
reconstruction of the original data point from a
noisy sample results in a correct prediction with
high confidence.
preprints
2016
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Content-based image retrieval tutorial
Mitros, John
2016
This paper functions as a tutorial for individuals
interested to enter the field of information
retrieval but wouldn’t know where to begin from. It
describes two fundamental yet efficient image
retrieval techniques, the first being k - nearest
neighbors (knn) and the second support vector
machines(svm). The goal is to provide the reader
with both the theoretical and practical aspects in
order to acquire a better understanding. Along with
this tutorial we have also developed the equivalent
software1 using the MATLAB environment in order to
illustrate the techniques, so that the reader can
have a hands-on experience.
theses
2022
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Mitros, J. (2022). Bayesian Neural Networks for Out-of-Distribution Detection. University College Dublin.
2014
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Mitros, J. (2014). Discovering Latent Factors in Lyrics of Greek Folk
Songs. [In Greek]. Aristotle University.