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Sequential Point Clouds: A Survey
[article]
2022
arXiv
pre-print
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However, many of these applications (e.g. autonomous driving and robotic manipulation) are actually based on sequential point clouds (i.e. four dimensions) because the information of the static point cloud ...
This paper presents an extensive review of the deep learning-based methods for sequential point cloud research including dynamic flow estimation, object detection \& tracking, point cloud segmentation, ...
(a) Voxel-based representation. (b) Point-based representation. (c) Lattice-based representation.
Fig. 5 : 5 Fig. 5: The illustration of different representations for scene flow estimation methods. ...
arXiv:2204.09337v2
fatcat:su7fdqd3xja53jg4zikqcpvoaa
Preface
2004
Applied intelligence (Boston)
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A simultaneous optimization method of a CBR system using a genetic algorithm (GA) for financial forecasting is then explained by Kim. ...
Systems based on this concept are finding widespread applications in problems like medical diagnosis and law interpretation where the knowledge available is incomplete and/or evidence is sparse. ...
For the first step, a method which uses a hierarchical representation of the target object is used to match the cases. ...
doi:10.1023/b:apin.0000043623.41336.bf
fatcat:qdl3bjsls5f4bdtykj4l5gkz5a
Teleconsultation demand classification and service analysis
2021
BMC Medical Informatics and Decision Making
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Methods For the effective teleconsultation classification, a novel ensemble hierarchical clustering method is proposed in this study. ...
In the proposed method, individual clustering results are first obtained by different hierarchical clustering methods, and then ensembled by one-hot encoding, the calculation and division of cosine similarity ...
Acknowledgements We wish to acknowledge the help of the staff in the National Telemedicine Center of China (NTCC) for this study. ...
doi:10.1186/s12911-021-01610-x
fatcat:pya66gzt6vgbviuimfmnrmqc2m
Hierarchical Temporal Memory Theory Approach to Stock Market Time Series Forecasting
2021
Electronics
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HTM is based on the biological functions of the brain as well as its learning mechanism. ...
Over the years, and with the emergence of various technological innovations, the relevance of automatic learning methods has increased exponentially, and they now play a key role in society. ...
Acknowledgments: We thank the administrative staff of the University of Minho for their availability.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/electronics10141630
fatcat:dkzwmy5mpnft3os5jsmdf2t2r4
TRiPOD: Human Trajectory and Pose Dynamics Forecasting in the Wild
[article]
2021
arXiv
pre-print
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In this paper, we propose a novel TRajectory and POse Dynamics (nicknamed TRiPOD) method based on graph attentional networks to model the human-human and human-object interactions both in the input space ...
Finally, we introduce a new benchmark for this joint task based on two challenging datasets (PoseTrack and 3DPW) and propose evaluation metrics to measure the effectiveness of predictions in the global ...
Error rate for ablation study on 3DPW dataset (in cm) using a sparse or dense graph as input skeleton representation. ...
arXiv:2104.04029v2
fatcat:pbsnyjefv5c2hhubsacxcmoo4u
Gaussian process decentralized data fusion meets transfer learning in large-scale distributed cooperative perception
2019
Autonomous Robots
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Gao, liang Zhao* Incorporating Discriminator in Sentence Generation: a Gibbs Sampling Method Jinyue Su, Jiacheng Xu, Xipeng Qiu*, Xuanjing Huang Incorporating GAN for Negative Sampling in Knowledge Representation ...
Hierarchical Discriminative Learning for Visible Thermal Person Re-Identification Mang Ye, Xiangyuan Lan, Jiawei Li, Pong c Yuen* Hierarchical LSTM for Sign Language Translation Dan Guo*, wengang zhou ...
doi:10.1007/s10514-018-09826-z
fatcat:67yqhwmgozccxni56rxmuapjgm
Designing Decision Support Systems for Emergency Response: Challenges and Opportunities
[article]
2022
arXiv
pre-print
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This includes a number of principled subsystems that implement early incident detection, incident likelihood forecasting and strategic resource allocation and dispatch policies. ...
In this paper, we highlight the key challenges and provide an overview of the approach developed by our team in collaboration with our community partners. ...
ACKNOWLEDGEMENTS This work is sponsored by the National Science Foundation under award numbers CNS1640624, CNS1818901, IIS1814958, IIS-1815459 and a grant from the Tennessee Department of Transportation ...
arXiv:2202.11268v2
fatcat:ck7xsflwr5hixdjdekckcvoxdm
Convergence of Photovoltaic Power Forecasting and Deep Learning: State-of-Art Review
2021
IEEE Access
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Non-deep learning methods comprise one to three operational layers, whereas DL methods stacks multiple layers (more than three) of simple modules hierarchically. ...
In [30] , a comprehensive review of RE forecasting methods has been conducted with a particular emphasis on wind and solar energy. ...
doi:10.1109/access.2021.3117004
fatcat:nxgyb5e4rvbynpmgzppv7ecbre
Forecasting with Many Predictors Using Message Passing Algorithms
2017
Social Science Research Network
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In a forecasting exercise involving a large set of orthogonal macroeconomic predictors, I show that Bayesian shrinkage estimators based on GAMP perform very well compared to a large set of alternatives ...
Using Monte Carlo simulations I establish that in certain scenarios GAMP can achieve estimation accuracy comparable to traditional Markov chain Monte Carlo methods, at a tiny fraction of the computing ...
Here, I build on Rangan (2011) and present an intuitive derivation of generalized approximate message passing based on graphical methods. ...
doi:10.2139/ssrn.2977838
fatcat:tifrxophf5alfkzcqmxuc2wz74
Predicting Future Agent Motions for Dynamic Environments
2016
2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)
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We address this by combining a novel multi-camera tracking method, efficient multi-resolution representations of state and a standard Inverse Reinforcement Learning (IRL) technique, to demonstrate performance ...
We present empirical experiments using data gathered in our own lab and external corpora (VIRAT), based on which we find that our algorithm is not only efficiently implementable on a resource constrained ...
These methods include those based on classifiers with structured outputs, which directly discriminate at the level of trajectories albeit with richer representations of the same. ...
doi:10.1109/icmla.2016.0024
dblp:conf/icmla/PrevitaliBIR16
fatcat:pbifal5yt5gfbjmpfc2dv657ra
Bayesian demography 250 years after Bayes
2016
Population Studies
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We focus on three applications: demographic forecasts, limited data, and highly structured or complex models. ...
The aim of this paper is to review the achievements of Bayesian demography, address some misconceptions, and make the case for wider use of Bayesian methods in population studies. ...
All the views and interpretations reported in this paper are those of the authors, and should not be attributed to any institution with which they are or were affiliated. ...
doi:10.1080/00324728.2015.1122826
pmid:26902889
pmcid:PMC4867874
fatcat:44i4xfn2infznatxt3ger536oy
Short-Term Load Forecasting Using a Novel Deep Learning Framework
2018
Energies
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This novel framework is used for short-term load forecasting based on the historical power load data of a town in the UK. ...
In this study, a novel deep-learning framework based on a restricted Boltzmann machine (RBM) and an Elman neural network is presented. ...
In order to further demonstrate the forecast performance of our proposed method, we decomposed the dataset based on different seasons. ...
doi:10.3390/en11061554
fatcat:sl3bn4uqknfdjhixbypm6pwzzy
A Hierarchical Spatio-Temporal Graph Convolutional Neural Network for Anomaly Detection in Videos
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2112.04294v1 fatcat:wsh4fow3ercfxg4lej7k7mejxy
In this paper, we propose a Hierarchical Spatio-Temporal Graph Convolutional Neural Network (HSTGCNN) to address these problems, the HSTGCNN is composed of multiple branches that correspond to different ...
levels of graph representations. ...
Based on the hierarchical graph representation inference introduced in Section III-B, three independent branches are built to provide predictions on the level of anomalies. ...
arXiv:2112.04294v2
fatcat:vi26nkpf2jhc7b2sjseo4b4rii
Biomedical literature classification with a CNNs-based hybrid learning network
2018
PLoS ONE
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OPEN ACCESS Citation: Yan Y, Yin X-C, Yang C, Li S, Zhang B-W (2018) Biomedical literature classification with a CNNs-based hybrid learning network. PLoS ONE 13(7): e0197933. https://doi.org/10. ...
Additionally, we have designed a hierarchical coarse to fine style indexing structure for learning and classifying documents, and a novel feature extension approach with word sequence embedding and Wikipedia ...
Shen [50] proposed a new latent semantic representation for web searches based on CNN. ...
doi:10.1371/journal.pone.0197933
pmid:30048461
pmcid:PMC6061982
fatcat:yarhcljdyna35hcnekwhcin7uu
Kernel Spectral Clustering and applications
[article]
2015
arXiv
pre-print
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Also, two possible ways to perform hierarchical clustering and a soft clustering method are presented. ...
Because of its model-based nature, the KSC method encompasses three main steps: training, validation, testing. ...
In the next Sections two different methods to obtain a sparse KSC model, based on the Incomplete Cholesky Decomposition (ICD) and L 1 and L 0 penalties respectively, are discussed. ...
arXiv:1505.00477v1
fatcat:msx7kebdxzgnjcgfk4ydmdknmy
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