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Bayesian spatio-temporal inference of trace gas emissions using an integrated nested Laplacian approximation and Gaussian Markov random fields
2020
Geoscientific Model Development
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We present a method to infer spatially and spatio-temporally correlated emissions of greenhouse gases from atmospheric measurements and a chemical transport model. ...
The inference is based on an integrated nested Laplacian approximation (INLA) for hierarchical models with Gaussian latent fields. ...
We would like to thank Alfredo Farjat and an anonymous reviewer for their helpful reviews of the manuscript. Review statement. ...
doi:10.5194/gmd-13-2095-2020
fatcat:md37orxfqfdftabty67hcvtwiq
Spatio-temporally efficient coding assigns functions to hierarchical structures of the visual system
[article]
2021
bioRxiv
pre-print
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Our proposed spatio-temporally efficient coding may facilitate deeper mechanistic understanding of the computational processes of hierarchical brain structures. ...
Therefore, we propose a novel computational principle for visual hierarchical structures as spatio-temporally efficient coding underscored by the efficient use of given resources in both neural activity ...
into the principle of spatially efficient coding. ...
doi:10.1101/2021.08.13.456321
fatcat:qz3v625grzf43kseos2xdhhj3q
Exploring the Spatial Hierarchy of Mixture Models for Human Pose Estimation
[chapter]
2012
Lecture Notes in Computer Science
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In this paper, we propose a new hierarchical spatial model that can capture an exponential number of poses with a compact mixture representation on each part. ...
Different from recent hierarchical models that associate each latent node to a mixture of appearance templates (like HoG), we use the hierarchical structure as a pure spatial prior avoiding the large and ...
This research was supported in parts by an ONR Grant N00014-11-1-0295 and a Samsung Advanced Institute of Technology grant. Yuandong Tian is supported by a Microsoft PhD fellowship. ...
doi:10.1007/978-3-642-33715-4_19
fatcat:ngf6igs27fd2zhshs4jjoc2qqi
Spatial Concept-based Topometric Semantic Mapping for Hierarchical Path-planning from Speech Instructions
[article]
2022
arXiv
pre-print
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The purpose of this study is to realize a hierarchical spatial representation using a topometric semantic map and planning efficient paths through human-robot interactions. ...
We also developed approximate inference methods for path planning, where the levels of the hierarchy can influence each other. ...
How robots can efficiently construct and utilize such hierarchical spatial representations for interaction tasks is a major challenge. ...
arXiv:2203.10820v1
fatcat:tsqwersvtvby5a2oeuaxanl35i
Editorial
2011
International Journal of Computer Vision
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They then propose a coarse-to-fine algorithm for efficient detection which exploits the hierarchical nature of the model. ...
a numerical study of the bottom-up and top-down inference processes in hierarchical models using the And-Or graph as an example. ...
doi:10.1007/s11263-011-0420-8
fatcat:2puoqwob25dlbky7lqi275u5p4
Bayesian spatiotemporal inference of trace gas emissions using an integrated nested Laplacian approximation and Gaussian Markov random fields
2019
Geoscientific Model Development Discussions
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</strong> We present a method to infer spatially and spatiotemporally correlated emissions of greenhouse gases from atmospheric measurements and a chemical transport model. ...
The inference is based on an integrated nested Laplacian approximation (INLA) for hierarchical models with Gaussian latent fields. ...
All together, this forms a Bayesian hierarchical model, from which emissions can be inferred. ...
doi:10.5194/gmd-2019-66
fatcat:c7kdntrapvcrrfjqv3dy2undcm
Structured Sparse Modelling with Hierarchical GP
[article]
2017
arXiv
pre-print
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We design an inference algorithm based on Expectation Propagation and evaluate the model over the real data. ...
It incorporates the structural assumptions based on a hierarchical Gaussian process prior for spike and slab coefficients. ...
We also develop an efficient inference method based on EP. The numerical experiments show superiority of the proposed model over the current state-of-the-art. ...
arXiv:1704.08727v1
fatcat:abjcxoqutrak3d44w6mj5chrvy
Study on Recent Approaches for Human Action Recognition in Real Time
2015
International Journal of Engineering Research and
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The main aim of action recognition is an automatic analysis of various actions from video data. ...
The challenge is to recognize human actions with more accuracy and efficiency in recognition time. ...
It efficiently computes the structural and visual similarity of two hierarchical decompositions by relying on models of their parent-child relations. ...
doi:10.17577/ijertv4is080577
fatcat:hikmv56t6jc5la7ipcny5u4kha
PICAR: An Efficient Extendable Approach for Fitting Hierarchical Spatial Models
[article]
2021
arXiv
pre-print
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2019 pre-print arXiv:1912.02382v1 fatcat:lsd33n3uvnbzdmfb3hmehcuxuy
PICAR is computationally efficient and scales well to high dimensions. It is also automated and easy to implement for a wide array of user-specified hierarchical spatial models. ...
Hierarchical spatial models are very flexible and popular for a vast array of applications in areas such as ecology, social science, public health, and atmospheric science. ...
In cases where it is possible to fit the full hierarchical spatial model, we show that our approach yields comparable results in terms of both inference and prediction. ...
arXiv:1912.02382v2
fatcat:bjsusrvsujeopfk2khuslc5ija
Joint Classification of Multiresolution and Multisensor Data Using a Multiscale Markov Mesh Model
2019
IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium
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The proposed model allows taking benefit of strong analytical properties, most remarkably causality, which make it possible to apply time-efficient non-iterative inference algorithms. ...
In this paper, the problem of the classification of multiresolution and multisensor remotely sensed data is addressed by proposing a multiscale Markov mesh model. ...
This combination ensures causality for the whole probabilistic graphical model -thus favoring efficient inference algorithms -, takes benefit from the spatial-contextual information within each layer, ...
doi:10.1109/igarss.2019.8898060
dblp:conf/igarss/MontaldoFHMZS19
fatcat:5qnos44q5bdcbhsct4nlgxxnp4
Grounding Abstract Spatial Concepts for Language Interaction with Robots
2017
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
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inference in the model. ...
We introduce a probabilistic model that incorporates an expressive space of abstract spatial concepts as well as notions of cardinality and ordinality. ...
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence ...
doi:10.24963/ijcai.2017/696
dblp:conf/ijcai/PaulARH17
fatcat:37ggvbwixzegxiqqktad4i25da
A Bayesian approach to multiscale inverse problems using the sequential Monte Carlo method
2011
Inverse Problems
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Based on a hierarchically structured sparse grid, a multiscale representation of the spatial field is constructed. ...
The method is demonstrated with the estimation of permeability in flows through porous media. ...
Acknowledgments This research was supported by the US Department of Energy, Office of Science, Advanced Scientific Computing Research, the Computational Mathematics program of the National Science Foundation ...
doi:10.1088/0266-5611/27/10/105004
fatcat:diri7bvpdvcapjmqwaclswhxr4
Improved return level estimation via a weighted likelihood, latent spatial extremes model
[article]
2018
arXiv
pre-print
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We adopt a hierarchical Bayesian framework for inference, use simulation to show the weighted model provides improved estimates of high quantiles, and apply our model to improve return level estimates ...
Latent spatial extremes models reduce uncertainty by exploiting spatial dependence in statistical characteristics of extreme events to borrow strength across locations. ...
Latent spatial extremes models are a flexible and computationally efficient class of models for marginal distributions of spatial extremes and quantities derived from them, like return levels. ...
arXiv:1810.07318v2
fatcat:s4ld6hv65fcppkpbejpmt27jza
Hierarchical Compositional Representations of Object Structure
[chapter]
2012
Lecture Notes in Computer Science
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of objects to perform efficient object detection. ...
Our framework for learning a hierarchical compositional shape vocabulary for representing multiple object classes takes simple contour fragments and learns their frequent spatial configurations. ...
of modeled object classes. ...
doi:10.1007/978-3-642-34166-3_3
fatcat:myz4tninbfbnthdg56bacajowm
Dynamic Spatial Sparsification for Efficient Vision Transformers and Convolutional Neural Networks
[article]
2022
arXiv
pre-print
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In this paper, we present a new approach for model acceleration by exploiting spatial sparsity in visual data. ...
We extend our method to hierarchical models including CNNs and hierarchical vision Transformers as well as more complex dense prediction tasks that require structured feature maps by formulating a more ...
The emergence of vision Transformers offers us a new way to explore spatial sparsity for learning more efficient models. ...
arXiv:2207.01580v1
fatcat:jqtq6crurzcevcczzxxjgnnoh4
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