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Graph Analysis and Graph Pooling in the Spatial Domain [article]

Mostafa Rahmani, Ping Li
2019 arXiv   pre-print
The proposed approach leverages a spatial representation of the graph which makes the neural network aware of the differences between the nodes and also their locations in the graph.  ...  The spatial representation which is equivalent to a point-cloud representation of the graph is obtained by a graph embedding method.  ...  Accordingly, in order to make the extracted local feature vectors a function of the role of the nodes in the structure of the graph, we propose the approach depicted by the red box in Figure 1 .  ... 
arXiv:1910.01589v1 fatcat:tblkj3f6unahzew575kjfjzvmu

The Cellular Organization of Zebrafish Visuomotor Circuits

Michael B. Orger
2016 Current Biology  
Combining these approaches, in order to investigate the role of individual, identified neurons in generating neural activity dynamics and behavior, has traditionally been possible only for simple invertebrate  ...  Meanwhile, single neurons have been specifically labeled to systematically characterize cell types with distinct morphologies and projection patterns.  ...  approaches in this simple vertebrate [3, 4] .  ... 
doi:10.1016/j.cub.2016.03.054 pmid:27166700 fatcat:qek2a5z6rjft7fyxqgd22amove

Echoes of the Brain within the Posterior Cingulate Cortex

R. Leech, R. Braga, D. J. Sharp
2012 Journal of Neuroscience  
The PCC is a major node within the default mode network (DMN) and has high metabolic activity and dense structural connectivity to widespread brain regions, which suggests it has a role as a cortical hub  ...  However, recent nonhuman primate work provides evidence for a more active role in the control of cognition, through signaling an environmental change and the need to alter behavior.  ...  We have, therefore, used these labels to interpret the results of our analyses. The results of the two approaches to labeling the ROI-derived networks are qualitatively similar.  ... 
doi:10.1523/jneurosci.3689-11.2012 pmid:22219283 pmcid:PMC6621313 fatcat:c4t6i7m3lfcvlhkhcdxh6phobu

Vision Transformer Equipped with Neural Resizer on Facial Expression Recognition Task [article]

Hyeonbin Hwang, Soyeon Kim, Wei-Jin Park, Jiho Seo, Kyungtae Ko, Hyeon Yeo
2022 arXiv   pre-print
As a remedy, Transformer has been introduced to vision fields with global receptive field, but requires adjusting input spatial size to the pretrained models to enjoy their strong inductive bias at hands  ...  In this work, we propose a novel training framework, Neural Resizer, to support Transformer by compensating information and downscaling in a data-driven manner trained with loss function balancing the  ...  Thus, we exploit a simple module, Spatial Transformer Network [25] , to align the necessary features before feeding to Super Resolution (SR) modules as an extended study.  ... 
arXiv:2204.02181v1 fatcat:z5awflmw4repvla73rgoycy7p4

Effective Training of Deep Convolutional Neural Networks for Hyperspectral Image Classification through Artificial Labeling

Wojciech Masarczyk, Przemysław Głomb, Bartosz Grabowski, Mateusz Ostaszewski
2020 Remote Sensing  
In this paper, we propose and investigate a simple and effective strategy of transfer learning that uses unsupervised pre-training step without label information.  ...  The transfer learning approach can be used to alleviate the second requirement for a particular dataset: first the network is pre-trained on some dataset with large amount of training labels available,  ...  The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  ... 
doi:10.3390/rs12162653 fatcat:vd3qrx3fonbwnlgapok67mckti

Fusion of multitemporal contextual information by neural networks for multisensor remote sensing image classification

Farid Melgani, Sebastiano B. Serpico, Gianni Vernazza
2003 Integrated Computer-Aided Engineering  
The neighboring class labels of a given pixel in the non-contextual classification map are exploited to extract spatial information, while temporal information is deduced from the non-contextual maps produced  ...  The first stage is a one-hidden layer MLP whose role is to estimate the single-time posterior probability of each class, given the feature vector.  ...  Spatial and temporal contextual information There may be different ways to model the spatial (or temporal) dependency among the labels in a spatial (or temporal) neighborhood.  ... 
doi:10.3233/ica-2003-10108 fatcat:wnxp3jv4njcnlom6ftyrptgsnm

Page 4 of The Journal of Neuroscience Vol. 15, Issue 2 [page]

1995 The Journal of Neuroscience  
tube gives rise to a variety of divergent neural structures.  ...  The spatial overlap of these genes suggested a role in defining axial position and cell identity, and disruption of Hox gene expression leads to alterations in cell migration and fate (Chisaka and Cappechi  ... 

Computational advances towards linking BOLD and behavior

John T. Serences, Sameer Saproo
2012 Neuropsychologia  
a precise generative model for the underlying neural activity.  ...  More recently, multivariate pattern classification analysis (MVPA) has been applied to fMRI data to evaluate the information content of spatially distributed activation patterns.  ...  This work was supported by NIH grant MH092345 to J.T.S.  ... 
doi:10.1016/j.neuropsychologia.2011.07.013 pmid:21840553 pmcid:PMC3384549 fatcat:a2flu7ydrfewlpmkrr35y333j4

Paradox of Enrichment: Destabilization of Exploitation Ecosystems in Ecological Time

M. L. Rosenzweig
1971 Science  
at the landscape scale: the role of diel foraging migrations by geese at the Bosque del Apache National Wildlife Refuge, New Mexico. Limnol.  ...  Ardea 83, 1-9 28 De Nie, H. (1995) Changes in the inland fish populations in Europe in relation to the increase of the cormorant Phalacrocorax carbo sinensis.  ...  This article was written with support from grants to S.H. from BBSRC and Wellcome and to V.B. from NERC and The Royal Society.  ... 
doi:10.1126/science.171.3969.385 pmid:5538935 fatcat:3bsqbmfpn5gbhcey6fm4nbyihi

Cognitive ecology: a field of substance?

Sue Healy, Victoria Braithwaite
2000 Trends in Ecology & Evolution  
at the landscape scale: the role of diel foraging migrations by geese at the Bosque del Apache National Wildlife Refuge, New Mexico. Limnol.  ...  Ardea 83, 1-9 28 De Nie, H. (1995) Changes in the inland fish populations in Europe in relation to the increase of the cormorant Phalacrocorax carbo sinensis.  ...  This article was written with support from grants to S.H. from BBSRC and Wellcome and to V.B. from NERC and The Royal Society.  ... 
doi:10.1016/s0169-5347(99)01737-1 pmid:10603501 fatcat:5lklyeukqjfmveuyxdhn6a2eva

Semantic Role Labelling for Robot Instructions using Echo State Networks

Johannes Twiefel, Xavier Hinaut, Stefan Wermter
2016 The European Symposium on Artificial Neural Networks  
To control a robot in a real-world robot scenario, a real-time parser is needed to create semantic representations from natural language which can be interpreted.  ...  The parser should be able to create the hierarchical tree-like representations without consulting external systems to show its learning capabilities.  ...  Available from http://www.i6doc.com/en/. connecting it to an existing speech recognition environment [18] and to employ the outputs of the system to control a real-world robot like in [13] .  ... 
dblp:conf/esann/TwiefelHW16 fatcat:gismnr3lczafrc2kuf6iim7ojq

Exploring Fisher vector and deep networks for action spotting

Zhe Wang, Limin Wang, Wenbin Du, Yu Qiao
2015 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
Moreover, we exploit deep neural networks to extract both appearance and motion representation for this task.  ...  For this reason, we submit the results obtained by our Fisher vector approach which achieves a Jaccard Index of 0.5385 and ranks the 1 st place in track 2.  ...  For the attempt on spatial convolutional neural network, we first extract frames from videos. And treat video label as picture label.  ... 
doi:10.1109/cvprw.2015.7301330 dblp:conf/cvpr/WangWD015 fatcat:5c7akoaambajfppowkaz7viaaa

Temporal Attribute Prediction via Joint Modeling of Multi-Relational Structure Evolution

Sankalp Garg, Navodita Sharma, Woojeong Jin, Xiang Ren
2020 Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence  
With a lot of dynamic knowledge graphs available, we can use this additional information to predict the time series better.  ...  In this paper, we propose a new framework to incorporate the information from dynamic knowledge graphs for time series prediction.  ...  Then, we use Recurrent Neural Networks and Logistic Regression to predict spatial role labels of phrases and extract spatial triplet relations among them.  ... 
doi:10.24963/ijcai.2020/382 dblp:conf/ijcai/GuoFZUK20 fatcat:tb6uey3tmnb2fo536rfbpdw77i

ICL-HD at SemEval-2016 Task 8: Meaning Representation Parsing - Augmenting AMR Parsing with a Preposition Semantic Role Labeling Neural Network

Lauritz Brandt, David Grimm, Mengfei Zhou, Yannick Versley
2016 Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)  
We attempt to improve AMR parsing by exploiting preposition semantic role labeling information retrieved from a multi-layer feed-forward neural network.  ...  Despite the usefulness of preposition semantic role labeling information for AMR parsing, it does not have an impact to the parsing F-score of CAMR, but reduces the parsing recall by 1%.  ...  Acknowledgments We would like to thank Sameer Pradhan for providing us with SRL parses of all task data.  ... 
doi:10.18653/v1/s16-1179 dblp:conf/semeval/BrandtGZV16 fatcat:ttbn75lvxfg2rhrf2gcyyv2ajy

Spatial Structured Prediction Models: Applications, Challenges, and Techniques

Zhe Jiang
2020 IEEE Access  
It is able to model complex spatial structure patterns in neural network layers.Blackbox model representation is hard to interpret. It often requires a large amount of training labels.  ...  Finally, deep neural network approach focuses on learning a blackbox model to automatically learn complex spatial structure dependency in an end-to-end manner.  ... 
doi:10.1109/access.2020.2975584 fatcat:oeseqyr3dbhx3hznh6omk2c7mm
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