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Bayesian Learning Neural Network Techniques to Forecast Mobile Phone User Location

2019 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
The consequence of training Bayesian learning is a subsequent dissemination through network weights.  ...  The Neural Networks of Bayesian learning foresees together mobile Network location and also enhanced provision than typical neural network methods meanwhile this one routines fine originated probability  ...  NEURAL NETWORKS TECHNIQUES IN Bayesian Learning Neural Network Techniques BAYESIAN LEARNING to Forecast Mobile Phone User Location The speculation ability of a factual model, established or Bayesian is  ... 
doi:10.35940/ijitee.i8023.078919 fatcat:ultea725unhw5bscotypwr6b7a

Introduction to Bayesian learning

Aaron Hertzmann
2004 Proceedings of the conference on SIGGRAPH 2004 course notes - GRAPH '04  
A weight decay prior can be used to prevent overfitting, to a limited degree. Neural networks become much more useful with Bayesian prediction (Section ??) .  ...  Neural networks Neural networks are widely used for solving regression problems.  ...  The first two chapters provide an excellent description of the philosophical background of the Bayesian worldview. • Neural Networks for Pattern Recognition, by Christopher M. Bishop, [1995] .  ... 
doi:10.1145/1103900.1103922 dblp:conf/siggraph/Hertzmann04 fatcat:5e4clziplzesdhsuluy2ygqmam

Priors in Bayesian Deep Learning: A Review [article]

Vincent Fortuin
2022 arXiv   pre-print
, and Bayesian neural networks.  ...  We hope to motivate practitioners in Bayesian deep learning to think more carefully about the prior specification for their models and to provide them with some inspiration in this regard.  ...  We thank Alex Immer, Adrià Garriga-Alonso, and Claire Vernade for helpful feedback on the draft and Arnold Weber for constant inspiration.  ... 
arXiv:2105.06868v3 fatcat:dmra3u2ibzgrnblzsepjgrr6pm

Bayesian deep learning for mapping via auxiliary information: a new era for geostatistics? [article]

Charlie Kirkwood, Theo Economou, Nicolas Pugeault
2020 arXiv   pre-print
There is no need to provide terrain derivatives (e.g. slope angles, roughness, etc) because the deep neural network is capable of learning these and arbitrarily more complex derivatives as necessary to  ...  We hope our results will raise awareness of the suitability of Bayesian deep learning - and its feature learning capabilities - for large-scale geostatistical applications where uncertainty matters.  ...  Our thanks go to Nvidia and their GPU grant scheme for kindly providing the Titan X Pascal GPU on which our deep neural network can be trained in under 30 minutes.  ... 
arXiv:2008.07320v3 fatcat:hqrzdcglgfazhmql6gt5pesnu4

Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning [article]

Sebastian Farquhar, Michael Osborne, Yarin Gal
2021 arXiv   pre-print
We propose Radial Bayesian Neural Networks (BNNs): a variational approximate posterior for BNNs which scales well to large models while maintaining a distribution over weight-space with full support.  ...  Other scalable Bayesian deep learning methods, like MC dropout or deep ensembles, have discrete support-they assign zero probability to almost all of the weight-space.  ...  We would also like to thank Milad Alizadeh, Joost van Amersfoort, Gregory Farquhar, Angelos Filos, and Andreas Kirsch for valuable discussions and/or comments on drafts.  ... 
arXiv:1907.00865v4 fatcat:mp3vodz6a5ghdnbnvo6zwxnuwa

A Survey on Bayesian Deep Learning [article]

Hao Wang, Dit-Yan Yeung
2021 arXiv   pre-print
This survey provides a comprehensive introduction to Bayesian deep learning and reviews its recent applications on recommender systems, topic models, control, etc.  ...  Besides, we also discuss the relationship and differences between Bayesian deep learning and other related topics such as Bayesian treatment of neural networks.  ...  A Brief History of Bayesian Neural Networks and Bayesian Deep Learning One topic highly related to BDL is Bayesian neural networks (BNN) or Bayesian treatments of neural networks.  ... 
arXiv:1604.01662v4 fatcat:xuorcc2c3bhpnenw6oec72migi

Towards On-Chip Bayesian Neuromorphic Learning [article]

Nathan Wycoff, Prasanna Balaprakash, Fangfang Xia
2020 arXiv   pre-print
this with Broadcast Alignment (a technique where network weights are replaced with random weights during feedback) and accumulated local information.  ...  The double role weights play in backpropagation-based-learning, dictating how the network reacts to both input and feedback, needs to be decoupled. e-prop 1 is a promising learning algorithm that tackles  ...  Progress has been made in the transfer of classical artificial neural network weights to a spiking neural network (SNN), so that a relationship learned offline can be deployed on the edge.  ... 
arXiv:2005.04165v1 fatcat:pj5dqgi4craqzcsc5vwqhrevou

Bayesian Compression for Deep Learning [article]

Christos Louizos, Karen Ullrich, Max Welling
2017 arXiv   pre-print
to encode the weights.  ...  In this work, we argue that the most principled and effective way to attack this problem is by adopting a Bayesian point of view, where through sparsity inducing priors we prune large parts of the network  ...  Acknowledgments We would like to thank Dmitry Molchanov, Dmitry Vetrov, Klamer Schutte and Dennis Koelma for valuable discussions and feedback. This research was supported by TNO, NWO and Google.  ... 
arXiv:1705.08665v4 fatcat:cyutmpic25fn3a65ep4w3gombu

Universal Target Learning: An Efficient and Effective Technique for Semi-Naive Bayesian Learning

SiQi Gao, Hua Lou, LiMin Wang, Yang Liu, Tiehu Fan
2019 Entropy  
In this paper, we extend TL to Universal Target Learning (UTL) to identify redundant correlations between attribute values and maximize the bits encoded in the Bayesian network in terms of log likelihood  ...  By redefining some criteria from information theory, Target Learning (TL) proposes to build for each unlabeled testing instance P the Bayesian Network Classifier BNC P , which is independent and complementary  ...  Algorithm 1: The learning procedure of KDB T . Input: Training data T , parameter k. Output: KDB T .  ... 
doi:10.3390/e21080729 pmid:33267443 pmcid:PMC7515258 fatcat:qxdhbv2yorf63fnhqgkpoffbv4

Bayesian Structure Adaptation for Continual Learning [article]

Abhishek Kumar, Sunabha Chatterjee, Piyush Rai
2020 arXiv   pre-print
We present a novel Bayesian approach to continual learning based on learning the structure of deep neural networks, addressing the shortcomings of both these approaches.  ...  Two notable directions among the recent advances in continual learning with neural networks are (i) variational Bayes based regularization by learning priors from previous tasks, and, (ii) learning the  ...  learning problems, our approach is applicable to both learning deep discriminative networks (supervised), where each task can be a Bayesian neural network (Neal, 2012; Blundell et al., 2015) , as well  ... 
arXiv:1912.03624v2 fatcat:bk4nd7dazjhizhaj2dbzogo23i

DiBS: Differentiable Bayesian Structure Learning [article]

Lars Lorch, Jonas Rothfuss, Bernhard Schölkopf, Andreas Krause
2021 arXiv   pre-print
This makes our formulation directly applicable to posterior inference of complex Bayesian network models, e.g., with nonlinear dependencies encoded by neural networks.  ...  Bayesian structure learning allows inferring Bayesian network structure from data while reasoning about the epistemic uncertainty -- a key element towards enabling active causal discovery and designing  ...  Automation under grant agreement 51NF40 180545, the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program grant agreement no. 815943, and was supported with  ... 
arXiv:2105.11839v3 fatcat:qqhh6sljsfe3vlqknk5mkk2ahm

Bayesian State Estimation for Unobservable Distribution Systems via Deep Learning [article]

Kursat Rasim Mestav, Jaime Luengo-Rozas, Lang Tong
2019 arXiv   pre-print
A deep learning approach to Bayesian state estimation is proposed for real-time applications.  ...  Comparing with pseudo-measurement techniques, direct Bayesian state estimation via deep learning neural network outperforms existing benchmarks.  ...  Standard techniques include L 1 regularization, dropout, and early stopping [41] . We present next a regularization technique based on structural constraints on the neural network. E.  ... 
arXiv:1811.02756v4 fatcat:bch4d2d74bg4rkupwwzbuvm574

Bayesian Learning of Neural Network Architectures [article]

Georgi Dikov, Patrick van der Smagt, Justin Bayer
2019 arXiv   pre-print
Our results show that regular networks with a learnt structure can generalise better on small datasets, while fully stochastic networks can be more robust to parameter initialisation.  ...  In this paper we propose a Bayesian method for estimating architectural parameters of neural networks, namely layer size and network depth.  ...  That is, the approach of Bayesian architecture learning is applicable to regular neural networks as well and we will show such an example in Section 5.  ... 
arXiv:1901.04436v2 fatcat:ekupxbxxuvguzozs6pxzrdouae

Efficacy of Bayesian Neural Networks in Active Learning [article]

Vineeth Rakesh, Swayambhoo Jain
2021 arXiv   pre-print
In this paper, we explore the efficacy of Bayesian neural networks for active learning, which naturally models uncertainty by learning distribution over the weights of neural networks.  ...  By performing a comprehensive set of experiments, we show that Bayesian neural networks are more efficient than ensemble based techniques in capturing uncertainty.  ...  Active Learning Via Bayesian Neural Networks Bayesian neural network: For a given dataset D = {(x i , y i )} D i=1 , Bayesian neural networks involves calculation of the distribution of weights given the  ... 
arXiv:2104.00896v2 fatcat:5r2t72bcgnfjnceb3dd2452fuq

Bayesian Deep Learning for Exoplanet Atmospheric Retrieval [article]

Frank Soboczenski, Michael D. Himes, Molly D. O'Beirne, Simone Zorzan, Atilim Gunes Baydin, Adam D. Cobb, Yarin Gal, Daniel Angerhausen, Massimo Mascaro, Giada N. Arney, Shawn D. Domagal-Goldman
2018 arXiv   pre-print
Recent advances in machine learning (ML) and computer vision offer new ways to reduce the time to perform a retrieval by orders of magnitude, given a sufficient data set to train with.  ...  Here we present an ML-based retrieval framework called Intelligent exoplaNet Atmospheric RetrievAl (INARA) that consists of a Bayesian deep learning model for retrieval and a data set of 3,000,000 synthetic  ...  Acknowledgements This work originated at the NASA Frontier Development Lab (FDL), an accelerated research program focused on finding solutions for space-related scientific challenges using ML, with support  ... 
arXiv:1811.03390v2 fatcat:3rhhzrj2f5htpeoy3fpf36xrri
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