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A Smooth Representation of Belief over SO(3) for Deep Rotation Learning with Uncertainty [article]

Valentin Peretroukhin, Matthew Giamou, David M. Rosen, W. Nicholas Greene, Nicholas Roy, Jonathan Kelly
2020 arXiv   pre-print
In this work, we present a novel symmetric matrix representation of the 3D rotation group, SO(3), with two important properties that make it particularly suitable for learned models: (1) it satisfies a  ...  smoothness property that improves convergence and generalization when regressing large rotation targets, and (2) it encodes a symmetric Bingham belief over the space of unit quaternions, permitting the  ...  Supervised Learning: Rotation Loss Functions For supervised learning over rotations, there are a number of possible choices for loss functions that are defined over SO (3) .  ... 
arXiv:2006.01031v2 fatcat:dmjy7ivojjgobcuv5ajxrbnow4

3-D Structural geological models: Concepts, methods, and uncertainties [chapter]

Florian Wellmann, Guillaume Caumon
2018 Advances in Geophysics  
We introduce a formulation for geological model representation and interpolation and uncertainty analysis methods with the aim to clarify similarities and differences in the diverse set of approaches that  ...  We believe that some reasons for this omission are (a) an incomplete picture of available geological modeling methods, and (b) the problem of the perceived static picture of an inflexible geological representation  ...  For these cases, a suitable summary measure for a representation of uncertainties in the full 3-D space is required.  ... 
doi:10.1016/bs.agph.2018.09.001 fatcat:drmjlawqy5c5rhotprbh7hlfv4

Temporal Difference Variational Auto-Encoder [article]

Karol Gregor, George Papamakarios, Frederic Besse, Lars Buesing, Theophane Weber
2019 arXiv   pre-print
Motivated by the absence of a model satisfying all these requirements, we propose TD-VAE, a generative sequence model that learns representations containing explicit beliefs about states several steps  ...  To act and plan in complex environments, we posit that agents should have a mental simulator of the world with three characteristics: (a) it should build an abstract state representing the condition of  ...  We are therefore left with a forward encoder which ideally computes the belief state, a backwards encoder which -with the forward encoder -compute posteriors over states, and a state-to-state forward model  ... 
arXiv:1806.03107v3 fatcat:zokst3l3pfbjti5t6j7ttqrncu

Being Bayesian about Categorical Probability [article]

Taejong Joo, Uijung Chung, Min-Gwan Seo
2020 arXiv   pre-print
As a Bayesian alternative to the softmax, we consider a random variable of a categorical probability over class labels.  ...  Neural networks utilize the softmax as a building block in classification tasks, which contains an overconfidence problem and lacks an uncertainty representation ability.  ...  Acknowledgements We would like to thank Dong-Hyun Lee and anonymous reviewers for the discussions and suggestions.  ... 
arXiv:2002.07965v2 fatcat:tzn3sspv6rheljl7jd44qzd32u

Deep Learning Point Cloud Odometry

Bernardo Teixeira, Hugo Silva
2021 U Porto Journal of Engineering  
of the benefits of leveraging learning-based encoding representations of real-world data is provided.  ...  In addition, a broad perspective of emergent Deep Learning robust techniques to track motion and estimate scene structure for real-world applications is the focus of a deeper analysis and comprehensive  ...  Acknowledgements The authors wish to acknowledge the support of the Portuguese funding agency, FCT -Fundação para a Ciência e a Tecnologia, through PhD research grant 2020.05052.BD.  ... 
doi:10.24840/2183-6493_007.003_0006 fatcat:orrlfkowkzgsjijnl5vnlczzzm

3D ShapeNets: A Deep Representation for Volumetric Shapes [article]

Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, Jianxiong Xiao
2015 arXiv   pre-print
Extensive experiments show that our 3D deep representation enables significant performance improvement over the-state-of-the-arts in a variety of tasks.  ...  To this end, we propose to represent a geometric 3D shape as a probability distribution of binary variables on a 3D voxel grid, using a Convolutional Deep Belief Network.  ...  We thank Thomas Funkhouser, Derek Hoiem, Alexei A. Efros, Andrew Owens, Antonio Torralba, Siddhartha Chaudhuri, and Szymon Rusinkiewicz for valuable discussion.  ... 
arXiv:1406.5670v3 fatcat:yyoyxqbyqjdxlifvb73x2rnrvu

3D ShapeNets: A deep representation for volumetric shapes

Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, Jianxiong Xiao
2015 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
Extensive experiments show that our 3D deep representation enables significant performance improvement over the-state-of-the-arts in a variety of tasks.  ...  To this end, we propose to represent a geometric 3D shape as a probability distribution of binary variables on a 3D voxel grid, using a Convolutional Deep Belief Network.  ...  We thank Thomas Funkhouser, Derek Hoiem, Alexei A. Efros, Andrew Owens, Antonio Torralba, Siddhartha Chaudhuri, and Szymon Rusinkiewicz for valuable discussion.  ... 
doi:10.1109/cvpr.2015.7298801 dblp:conf/cvpr/WuSKYZTX15 fatcat:tkadr5auafdpvglgmsooo3ysde

A&G Volume 55 Issue 3, Full Issue

2014 Astronomy & Geophysics  
Here again, we consider a deformable lens rather than a deformable mirror for ease of representation.  ...  The light curve shows no variation for most of the year of data taking, and an upward excursion lasting over one month, with a maximum increase of ~2 mag.  ...  Note that tickets for these events have been selling fast, so get in touch at once if you wish to attend.  ... 
doi:10.1093/astrogeo/atu134 fatcat:c5eui37ap5aophjflkzshczguy

Top–Down Connections in Self-Organizing Hebbian Networks: Topographic Class Grouping

M Luciw, Juyang Weng
2010 IEEE Transactions on Autonomous Mental Development  
As summarized in [32] , other results from similar algorithms on this NORB dataset include logistic regression (19.6%), SVM with a Gaussian kernel (11.6%), and deep belief networks with a greedily pretrained  ...  The visual cortex is a deep hierarchical bidirectional network [1] - [3] .  ... 
doi:10.1109/tamd.2010.2072150 fatcat:4rckp3y445bojipl5j3mfoqmti

Learning to track environment state via predictive autoencoding [article]

Marian Andrecki, Nicholas K. Taylor
2021 arXiv   pre-print
This work introduces a neural architecture for learning forward models of stochastic environments.  ...  The network can output both expectation over future observations and samples from belief distribution. The resulting functionalities are similar to those of a Particle Filter (PF).  ...  The observations can be noisy and intermittent, the outputs include expectation over future observations, samples of future observations and belief states (as a neural representation).  ... 
arXiv:2112.07745v1 fatcat:ehhqc4cmyfdjlgmhra3yh4v7wi

Wind Turbine Drivetrain Expert Fault Detection System: Multivariate Empirical Mode Decomposition based Multi-sensor Fusion with Bayesian Learning Classification

R. Uma Maheswari, R. Umamaheswari
2020 Intelligent Automation and Soft Computing  
(Jing, Wang, Zhao, & Wang, 2017) proposed the deep learning networks to fuse the features learned from the raw vibration signal.  ...  At learning, the cardinality is set to 0 to 3 to calculate the probability. Table 5 compares the Naïve Bayes and belief net classifiers performance.  ... 
doi:10.32604/iasc.2020.013924 fatcat:4dzn6xzsajb43dpwm5zb2bexsi

Autonomous Exploration Under Uncertainty via Deep Reinforcement Learning on Graphs [article]

Fanfei Chen, John D. Martin, Yewei Huang, Jinkun Wang, Brendan Englot
2020 arXiv   pre-print
We propose a novel approach that uses graph neural networks (GNNs) in conjunction with deep reinforcement learning (DRL), enabling decision-making over graphs containing exploration information to predict  ...  For this problem, belief space planning methods that forward-simulate robot sensing and estimation may often fail in real-time implementation, scaling poorly with increasing size of the state, belief and  ...  [19] implementation of iSAM2 [20] to perform nonlinear least-squares smoothing over a factor graph.  ... 
arXiv:2007.12640v1 fatcat:4x2lsdzoazeq7ayauocjsjqqq4

Greedy Deep Dictionary Learning [article]

Snigdha Tariyal, Angshul Majumdar, Richa Singh, Mayank Vatsa
2016 arXiv   pre-print
We compare our results with other deep learning tools like stacked autoencoder and deep belief network; and state of the art supervised dictionary learning tools like discriminative KSVD and label consistent  ...  In this work we propose a new deep learning tool called deep dictionary learning. Multi-level dictionaries are learnt in a greedy fashion, one layer at a time.  ...  Comparison with other Deep Learning Approaches We compared our results with a stacked autoencoder (SAE) and deep belief network (DBN).  ... 
arXiv:1602.00203v1 fatcat:otji3ovu75aijlcji3azh7k3wa

Vol. 2, No. 3, August 2009

Editor IES
2009 International Education Studies  
Process NGT is a method that allows ideas to be generated in relation to a situation that is surrounded with uncertainty.  ...  requires a rich and deep understanding, so teacher behaviors and characteristics that support teacher clarity are valued.  ... 
doi:10.5539/ies.v2n3p0 fatcat:3smidj2gcbd3jlzg6dt56y5e3y

Active Robotic Mapping through Deep Reinforcement Learning [article]

Shane Barratt
2017 arXiv   pre-print
In contrast to prior work, this approach learns an exploration policy based on a user-specified prior over environment configurations and sensor model, allowing it to specialize to the specifications.  ...  We propose an approach to learning agents for active robotic mapping, where the goal is to map the environment as quickly as possible.  ...  First, we require a representation of the map as well as our belief over possible maps. Second, we need a way to update our belief over maps given observations.  ... 
arXiv:1712.10069v1 fatcat:uxkywyzw3zcdtgnsjyd5hpcf74
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