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Informed MCMC with Bayesian Neural Networks for Facial Image Analysis [article]

Adam Kortylewski, Mario Wieser, Andreas Morel-Forster, Aleksander Wieczorek, Sonali Parbhoo, Volker Roth, Thomas Vetter
2018 arXiv   pre-print
In this work, we propose to use a Bayesian Neural Network for estimating an image dependent proposal distribution.  ...  In this way, we can significantly reduce the number of samples needed to perform facial image analysis.  ...  Discussion We have presented a novel approach to inform MCMC sampling with Bayesian Neural Networks.  ... 
arXiv:1811.07969v2 fatcat:reqbicvrnnhgbaeb5ck6fbjlxq

Confident Classification using a Hybrid between Deterministic and Probabilistic Convolutional Neural Networks

Muhammad Naseer Bajwa, Suleman Khurram, Mohsin Munir, Shoaib Ahmed Siddiqui, Muhammad Imran Malik, Andreas Dengel, Sheraz Ahmed
2020 IEEE Access  
This paper proposes a hybrid convolutional neural network which combines high accuracy of deterministic models with posterior distribution approximation of Bayesian neural networks.  ...  INDEX TERMS Bayesian estimation, convolutional neural networks, hybrid neural networks, image classification, time-series classification, uncertainty estimation. 115476 This work is licensed under a Creative  ...  Similarly, other neural networks with partially Bayesian parameters have been proposed for regression tasks as alternative to Gaussian Processes [14] , [28] , which do not scale well with the number  ... 
doi:10.1109/access.2020.3004409 fatcat:dvpcs3c57rg2tpp74xwjwky5wa

A Survey of Uncertainty in Deep Neural Networks [article]

Jakob Gawlikowski, Cedrique Rovile Njieutcheu Tassi, Mohsin Ali, Jongseok Lee, Matthias Humt, Jianxiang Feng, Anna Kruspe, Rudolph Triebel, Peter Jung, Ribana Roscher, Muhammad Shahzad, Wen Yang (+2 others)
2022 arXiv   pre-print
The modeling of these uncertainties based on deterministic neural networks, Bayesian neural networks, ensemble of neural networks, and test-time data augmentation approaches is introduced and different  ...  For a practical application, we discuss different measures of uncertainty, approaches for the calibration of neural networks and give an overview of existing baselines and implementations.  ...  “Sensitivity analysis for predictive uncertainty in bayesian neural [159] R. M.  ... 
arXiv:2107.03342v3 fatcat:cex5j3xq5fdijjdtdbt2ixralm

Fusiform Gyrus Dysfunction is Associated with Perceptual Processing Efficiency to Emotional Faces in Adolescent Depression: A Model-Based Approach

Tiffany C. Ho, Shunan Zhang, Matthew D. Sacchet, Helen Weng, Colm G. Connolly, Eva Henje Blom, Laura K. M. Han, Nisreen O. Mobayed, Tony T. Yang
2016 Frontiers in Psychology  
responsible for the early processing of visual information.  ...  magnetic resonance imaging (fMRI).  ...  Hahn for assistance with data collection.  ... 
doi:10.3389/fpsyg.2016.00040 pmid:26869950 pmcid:PMC4740953 fatcat:rdcprirjtzbsvab2v7hrshiu7y

Efficient inverse graphics in biological face processing

Ilker Yildirim, Mario Belledonne, Winrich Freiwald, Josh Tenenbaum
2020 Science Advances  
Inverting generative models, or "analysis-by-synthesis", presents a possible solution, but its mechanistic implementations have typically been too slow for online perception, and their mapping to neural  ...  The model is based on a deep neural network that learns to invert a three-dimensional face graphics program in a single fast feedforward pass.  ...  We took a recent convolutional neural network with an hourglass architecture that is trained for volumetric 3D segmentation of faces from images (27) .  ... 
doi:10.1126/sciadv.aax5979 pmid:32181338 pmcid:PMC7056304 fatcat:zbec5hkntjb2hpc4q3tdwwrf2a

Learning probabilistic classifiers for human–computer interaction applications

Nicu Sebe, Ira Cohen, Fabio G. Cozman, Theo Gevers, Thomas S. Huang
2005 Multimedia Systems  
type of probabilistic classifiers, Bayesian networks.  ...  In this paper, we discuss training probabilistic classifiers with labeled and unlabeled data for HCI applications.  ...  Bayesian networks Bayesian networks [47] are tools for modeling and classification.  ... 
doi:10.1007/s00530-005-0177-4 fatcat:elku2ivr2bczvkbeuvxkyhvsei

Hierarchical Representations Feature Deep Learning for Face Recognition

Haijun Zhang, Yinghui Chen
2020 Journal of Data Analysis and Information Processing  
learning has better effect than all supervised learning algorithms; third, hybrid neural networks have better effect than single model neural network; fourth, the average recognition rate and variance  ...  In this paper, we propose a novel deep learning algorithm combining unsupervised and supervised learning named deep belief network embedded with Softmax regress (DBNESR) as a natural source for obtaining  ...  Journal of Data Analysis and Information Processing Figure 4 . 4 Hybrid RBF neural networks (HRBFNNs). of positive example is +1 and negative example is −1.  ... 
doi:10.4236/jdaip.2020.83012 fatcat:ldiygwlqj5b73gyeqiqev5wp5e

A Rational Analysis of the Acquisition of Multisensory Representations

Ilker Yildirim, Robert A. Jacobs
2011 Cognitive Science  
This analysis makes use of a Bayesian nonparametric model that acquires latent multisensory features that optimally explain the unisensory features arising in individual sensory modalities.  ...  The model qualitatively accounts for several important aspects of multisensory perception: (a) it integrates information from multiple sensory sources in such a way that it leads to superior performances  ...  Bülthoff for sharing their visual-haptic experimental data with us, and the anonymous reviewers and M. Lee for their helpful comments on an earlier version of this manuscript.  ... 
doi:10.1111/j.1551-6709.2011.01216.x pmid:22141921 fatcat:2nhzgxmolnerxib4mf6i7h3qha

The Future of Data Analysis in the Neurosciences [article]

Danilo Bzdok, B. T. Thomas Yeo
2016 arXiv   pre-print
We believe that large-scale data analysis will use more models that are non-parametric, generative, mixing frequentist and Bayesian aspects, and grounded in different statistical inferences.  ...  While growing data availability and information granularity have been amply discussed, we direct attention to a routinely neglected question: How will the unprecedented data richness shape data analysis  ...  This includes Bayesian time-series analysis [51] , model selection for group analysis [50] and mixed-effects classification for imbalanced groups [52] .  ... 
arXiv:1608.03465v1 fatcat:roen4d2axncufftj3ifjjimqpe

Deep learning from 21-cm tomography of the Cosmic Dawn and Reionization

Nicolas Gillet, Andrei Mesinger, Bradley Greig, Adrian Liu, Graziano Ucci
2019 Monthly notices of the Royal Astronomical Society  
Here we showcase astrophysical parameter recovery directly from 21-cm images, using deep learning with convolutional neural networks (CNN).  ...  Therefore there is additional information which is wasted if only the PS is used for parameter recovery.  ...  for Research in Astronomy, Inc., for NASA, under contract NAS5-26555.  ... 
doi:10.1093/mnras/stz010 fatcat:wfb5huk6nbfdvlh63jm6hastpa

Semisupervised learning of classifiers: theory, algorithms, and their application to human-computer interaction

I. Cohen, F.G. Cozman, N. Sebe, M.C. Cirelo, T.S. Huang
2004 IEEE Transactions on Pattern Analysis and Machine Intelligence  
Index Terms Semi-supervised learning, generative models, facial expression recognition, face detection, unlabeled data, Bayesian network classifiers.  ...  We discuss the implications of this analysis to a specific type of probabilistic classifiers, Bayesian networks, and propose a new structure learning algorithm that can utilize unlabeled data to improve  ...  For the labeled only case, we also compare results with training of an artificial Neural network (ANN) so as to test how Bayesian network classifiers compare with a different kind of classifier for this  ... 
doi:10.1109/tpami.2004.127 pmid:15573817 fatcat:6kzguo4f5vhcnndy3kgki6skwq

2020 Index IEEE Signal Processing Letters Vol. 27

2020 IEEE Signal Processing Letters  
Brain MR Images Using Cross-Domain Neural Networks.  ...  ., +, LSP 2020 201-205 Multiple Norms and Boundary Constraint Enforced Image Deblurring via Efficient MCMC Algorithm.  ... 
doi:10.1109/lsp.2021.3055468 fatcat:wfdtkv6fmngihjdqultujzv4by

Long-Term Online Multiface Tracking using Kalman Filter

Deepthi S, Dr. Dinakar Das C. N
2015 International Journal of Engineering Research and  
Tracking of the face movement in the input frame of the video is the key process for various real time applications such as videoconferencing, human robotics or human computer interface or in the analysis  ...  This paper gives a brief analysis of recent long-term online multi-face tracking algorithms based on Markov model and Kalman filtering.  ...  Face detection using artificial neural networks was done by Rowley [1] . It is robust but computationally complex as the whole image has to be scanned at different scales and orientations.  ... 
doi:10.17577/ijertv4is090488 fatcat:x7nquobte5c4tpkdpwkymh6cvm

Bayesian Brains without Probabilities

Adam N. Sanborn, Nick Chater
2016 Trends in Cognitive Sciences  
In this paper, we argue for the opposite view: that the brain implements Bayesian inference and that systematic probability reasoning errors actually follow from a Bayesian approach.  ...  Yet Bayesian computational models often must represent vast data spaces, such as the space of possible images or speech waves; and effectively infinite hypothesis spaces, such as the space of possible  ...  Supplemental Information Supplemental information associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.tics. 2016.10.003.  ... 
doi:10.1016/j.tics.2016.10.003 pmid:28327290 fatcat:sm2tpkuz6bblteqwk4mhuwxdr4

Graph-based Facial Affect Analysis: A Review [article]

Yang Liu, Xingming Zhang, Yante Li, Jinzhao Zhou, Xin Li, Guoying Zhao
2022 arXiv   pre-print
As one of the most important affective signals, facial affect analysis (FAA) is essential for developing human-computer interaction systems.  ...  Early methods focus on extracting appearance and geometry features associated with human affects while ignoring the latent semantic information among individual facial changes, leading to limited performance  ...  ACKNOWLEDGMENTS The authors would like to thank Muzammil Behzad and Tuomas Varanka for providing materials and suggestions for the figures used in this paper.  ... 
arXiv:2103.15599v6 fatcat:o2r6wi7qtzdcnbhicm45yvclzy
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