Automatic diagnosis of covid-19 from ct images using cyclegan and transfer learning
The outbreak of the corona virus disease (COVID-19) has changed the lives of most people
on Earth. Given the high prevalence of this disease, its correct diagnosis in order to …
on Earth. Given the high prevalence of this disease, its correct diagnosis in order to …
[HTML][HTML] Pipeline corrosion prediction and uncertainty analysis with an ensemble Bayesian neural network approach
This study developed an ensemble Bayesian Neural Network (BNN) model for pipeline
corrosion prediction incorporating uncertainty analysis. The performance of the proposed …
corrosion prediction incorporating uncertainty analysis. The performance of the proposed …
Extracting or guessing? improving faithfulness of event temporal relation extraction
In this paper, we seek to improve the faithfulness of TempRel extraction models from two
perspectives. The first perspective is to extract genuinely based on contextual description …
perspectives. The first perspective is to extract genuinely based on contextual description …
[HTML][HTML] Uncertain-cam: Uncertainty-based ensemble machine voting for improved covid-19 cxr classification and explainability
The ongoing coronavirus disease 2019 (COVID-19) pandemic has had a significant impact
on patients and healthcare systems across the world. Distinguishing non-COVID-19 patients …
on patients and healthcare systems across the world. Distinguishing non-COVID-19 patients …
A novel dual layer cascade reliability framework for an informed and intuitive clinician-AI interaction in diagnosis of colorectal cancer polyps
We present a novel Cascade Reliability Framework (CRF) that integrates two independent
cascade layers of reliability (ie, variational temperature scaling and conformal prediction) …
cascade layers of reliability (ie, variational temperature scaling and conformal prediction) …
Cold posteriors through PAC-Bayes
We investigate the cold posterior effect through the lens of PAC-Bayes generalization
bounds. We argue that in the non-asymptotic setting, when the number of training samples is …
bounds. We argue that in the non-asymptotic setting, when the number of training samples is …
Beauty in the Eyes of Machine: A Novel Intelligent Signal Processing-Based Approach to Explain the Brain Cognition and Perception of Beauty Using Uncertainty …
The most mysterious question humans have ever attempted to answer for centuries is,“What
is beauty, and how does the brain decide what beauty is?”. The main problem is that beauty …
is beauty, and how does the brain decide what beauty is?”. The main problem is that beauty …
[PDF][PDF] The Fine Print on Tempered Posteriors-Supplementary
K Pitas, J Arbel - Proceedings of Machine Learning …, 2023 - proceedings.mlr.press
Our case can be seen as a greatly simplified case of Online Variational Inference Chérief-
Abdellatif et al.(2019) for the set Zvalidation∪ Ztrain. In fact, in a truly Bayesian approach we …
Abdellatif et al.(2019) for the set Zvalidation∪ Ztrain. In fact, in a truly Bayesian approach we …
Uncertainty Calibration and its Application to Object Detection
F Küppers - arXiv preprint arXiv:2302.02622, 2023 - arxiv.org
Image-based environment perception is an important component especially for driver
assistance systems or autonomous driving. In this scope, modern neuronal networks are …
assistance systems or autonomous driving. In this scope, modern neuronal networks are …
Probabilistic calibration of a binary classifier, applied to detecting sleeping state in a car drive
P Giudici, G Villone - 2024 - advance.sagepub.com
Probabilistic calibration of a binary classifier, applied to detecting sleeping state in a car drive
Page 1 P osted on 26 Apr 2024 — The cop yrigh t holder is the author/funder. All righ ts reserv …
Page 1 P osted on 26 Apr 2024 — The cop yrigh t holder is the author/funder. All righ ts reserv …