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检索条件"任意字段=1st International Workshop on Deterministic and Statistical Methods in Machine Learning"

共 72 条记录，以下是21-30 订阅

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Spatiotemporal statistical Model of Anatomical Landmarks on a Human Embryonic Brain 1st

Spatiotemporal Statistical Model of Anatomical Landmarks on ...

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1st international workshop on Uncertainty for Safe Utilization of machine learning in Medical Imaging / 8th international workshop on Clinical Image-based Procedures - From Planning to Intervention (CLIP)

作者： Shinjo, Aoi Saito, Atsushi Takakuwa, Tetsuya Yamada, Shigehito Hontani, Hidekata Matsuzoe, Hiroshi Miyauchi, Shoko Morooka, Ken'ichi Shimizu, Akinobu Tokyo Univ Agr & Technol Tokyo Japan Kyoto Univ Kyoto Japan Nagoya Inst Technol Nagoya Aichi Japan Kyushu Univ Fukuoka Japan

ISBN: (纸本)9783030326890;9783030326883

We propose a new method for constructing a spatiotemporal statistical model of the distribution of anatomical landmarks (LMs) of a human embryo. This method exhibits potential for the quantitative assessment of the extent of anomalies and is important in the research of congenital malformations. However, a few of the LMs might not be observed at a specific developmental stage because large morphological deformations exist during the early stages of development. It is difficult for conventional statistical shape analysis methods to handle missing LMs in the training dataset. The basic concept of the proposed method is to conduct statistical analyses by predicting and completing the coordinates of the missing LMs. We demonstrated the proposed method in the context of spatiotemporal statistical modeling of 10 LMs on the brain surface using 37 embryonic subjects with Carnegie stages of 19-22. We conducted a comparative study of the spatiotemporal statistical models between four different prediction methods, and we found that deformable surface mapping was the best prediction method in terms of model generalization and specificity.

关键词： Spatiotemporal analysis statistical model Landmark Embryo

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CMiGIN 2019 - Proceedings of the international workshop on Conflict Management in Global Information Networks, co-located with 1st international Conference on Cyber Hygiene and Conflict Management in Global Information Networks, CyberConf 2019

CMiGIN 2019 - Proceedings of the International Workshop on C...

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8th international Symposium on Highly-Efficient Accelerators and Reconfigurable Technologies, HEART 2017

The proceedings contain 52 papers. The topics discussed include: assessment of operator-pilot training in conflict situations;anonymous decentralized e-voting system;machine learning detection of DDoS attacks based on visualization of recurrence plots;transaction planning methods in hyperconverged architecture systems;integration deterministic, stochastic and non-stochastic uncertainty models in conflict situations;practices of using blockchain technology in ICT under the digitalization of the world economy;methods of nulling numbers in the system of residual classes;estimations of the signal information parameters in radio engineering systems;mathematical model of the biometric system of fingerprint authentication for modern web-services;and method of determining significant linguistic bipolar features on the basis of submitting phonetic assessments of sound-letters

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Forecast of water-cut at wells under design by machine learning methods 5

Forecast of water-cut at wells under design by machine learn...

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Data Science Session at the 5th international Conference on Information Technology and Nanotechnology, DS-ITNT 2019

作者： Enikeev, M.R. Fazlytdinov, M.F. Enikeeva, L.V. Gubaidullin, I.M. Gazpromneft STC Moika River emb. 75-79 liter D St. Petersburg190000 Russia Ufa State Petroleum Technological University Kosmonavtov St. 1 Ufa450062 Russia Institute Petrochemistry and Catalysis Subdivision of the Ufa Federal Research Centre of RAS Oktyabria pr. 141 Ufa450075 Russia

A large amount of data is generated during the operation of oil fields. Such data can be both data already interpreted by a specialist, or "raw" data obtained directly from the devices, both structured and not structured, or locally structured (that is, allowing for local analysis, but in such form not allowing analyzing in conjunction with other types of data). To obtain from such a set of more informative data that will allow making decisions in the course of field operation, it is necessary to involve specialists from different areas of the oil industry. Therefore, it is possible and necessary to use non-deterministic methods for analyzing the data obtained. The article discusses the use of machine learning methods in the task of determining the initial water-cut based on well logging data. © 2019 CEUR workshop Proceedings.

关键词： machine learning

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A Device-Independent Novel statistical Modeling for Cerebral TOF-MRA Data Segmentation 1st

A Device-Independent Novel Statistical Modeling for Cerebral...

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作者： Zhang, Baochang Wu, Zonghan Liu, Shuting Zhou, Shoujun Li, Na Zhao, Gang Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Univ Chinese Acad Sci Shenzhen Coll Adv Technol Shenzhen Peoples R China Tsinghua Univ Sch Grad Sch Shenzhen Shenzhen Peoples R China PLA Gen Hosp Southern Theater Command Neurosurg Dept Guangzhou Peoples R China

ISBN: (纸本)9783030326890;9783030326883

Among the model-driven segmentation methods, the Maximum a Posterior (MAP) & Markov Random Field (MRF) is the popular statistical framework. However, there remains a dominating limitation in the existing statistical modeling, i.e., the data imaged by MR scanners with different types and parameters cannot be adaptively processed to lead accurate and robust vessel segmentation, as is well-known to the researchers in this field. Our methodology steps contribute as: (1) a region-histogram standardization strategy is explored to the time-of-flight magnetic resonance angiography data;(2) a Gaussian mixture models (GMM) is constructed with three Gaussian distributions and a knowledge-based expectation-maximization algorithm is explored to obtain the GMM parameters;(3) a probability feature map is captured according the estimated vascular distribution weight in GMM and then is embedded into the Markov high-level process to relieve the label field noise and rich the vascular structure. Our method wins out the other models with better segmentation accuracy and the sensibility to small-sized vessels or large arteriovenous malformation mass, which is validated on three different datasets and obtains satisfying results on visual and quantitative evaluation with Dice similarity coefficient and positive predictive value of 89.12% and 95.66%.

关键词： Cerebrovascular segmentation statistical model Markov Random Field Data standardization Probability feature map

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Three-Dimensional Face Reconstruction from Uncalibrated Photographs: Application to Early Detection of Genetic Syndromes 1st

Three-Dimensional Face Reconstruction from Uncalibrated Phot...

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作者： Tu, Liyun Porras, Antonio R. Morales, Araceli Perez, Daniel A. Piella, Gemma Sukno, Federico Linguraru, Marius George Sheikh Zayed Inst Pediat Surg Innovat Childrens Natl Hlth Syst Washington DC 20010 USA Univ Pompeu Fabra Dept Informat & Commun Technol Barcelona Spain George Washington Univ Sch Med & Hlth Sci Washington DC 20052 USA

ISBN: (纸本)9783030326890;9783030326883

Facial analysis from photography supports the early identification of genetic syndromes, but clinically-acquired uncalibrated images suffer from image pose and illumination variability. Although 3D photography overcomes some of the challenges of 2D images, 3D scanners are not typically available. We present an optimization method for 3D face reconstruction from uncalibrated 2D photographs of the face using a novel statistical shape model of the infant face. First, our method creates an initial estimation of the camera pose for each 2D photograph using the average shape of the statistical model and a set of 2D facial landmarks. Second, it calculates the camera pose and the parameters of the statistical model by minimizing the distance between the projection of the estimated 3D face in the image plane of each camera and the observed 2D face geometry. Using the reconstructed 3D faces, we automatically extract a set of 3D geometric and appearance descriptors and we use them to train a classifier to identify facial dysmorphology associated with genetic syndromes. We evaluated our face reconstruction method on 3D photographs of 54 subjects (age range 0-3 years), and we obtained a point-to-surface error of 2.01 +/- 0.54%, which was a significant improvement over 2.98 +/- 0.64% using state-of-the-art methods (p < 0.001). Our classifier detected genetic syndromes from the reconstructed 3D faces from the 2D photographs with 100% sensitivity and 92.11% specificity.

关键词： Facial dysmorphology 3D face reconstruction 2D photography Morphable model statistical shape model

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GUARANTEED deterministic BOUNDS ON THE TOTAL VARIATION DIstANCE BETWEEN UNIVARIATE MIXTURES 28

GUARANTEED DETERMINISTIC BOUNDS ON THE TOTAL VARIATION DISTA...

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IEEE 28th international workshop on machine learning for Signal Processing (MLSP)

作者： Nielsen, Frank Sun, Ke Sony Comp Sci Labs Inc Tokyo Japan CSIRO Data61 Canberra ACT Australia

ISBN: (纸本)9781538654774

The total variation distance is a core statistical distance between probability measures that satisfies the metric axioms, with value always falling in [0;1]. Since the total variation distance does not admit closed-form expressions for statistical mixtures, one often has to rely in practice on costly numerical integrations or on fast Monte Carlo approximations that however do not guarantee deterministic bounds. In this work, we consider two methods for bounding the total variation of univariate mixture models: The first is based on the information monotonicity property of the total variation to design guaranteed nested deterministic lower bounds. The second method relies on computing the geometric lower and upper envelopes of weighted mixture components to derive deterministic bounds based on density ratio. We demonstrate the tightness of our bounds through simulating Gaussian, Gamma and Rayleigh mixture models.

关键词： Total variation mixture models information theoretic bounds

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Neural Networks as Model Selection with Incremental MDL Normalization 1st

Neural Networks as Model Selection with Incremental MDL Nor...

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1st international workshop on Human Brain and Artificial Intelligence, HBAI 2019, held in conjunction with the 28th international Joint Conference on Artificial Intelligence, IJCAI 2019

作者： Lin, Baihan Center for Theoretical Neuroscience Columbia University New York United States Zuckerman Mind Brain Behavior Institute Columbia University New York United States Department of Applied Mathematics University of Washington Seattle United States

ISBN: (纸本)9789811513978

If we consider the neural network optimization process as a model selection problem, the implicit space can be constrained by the normalizing factor, the minimum description length of the optimal universal code. Inspired by the adaptation phenomenon of biological neuronal firing, we propose a class of reparameterization of the activation in the neural network that take into account the statistical regularity in the implicit space under the Minimum Description Length (MDL) principle. We introduce an incremental version of computing this universal code as normalized maximum likelihood and demonstrated its flexibility to include data prior such as top-down attention and other oracle information and its compatibility to be incorporated into batch normalization and layer normalization. The empirical results showed that the proposed method outperforms existing normalization methods in tackling the limited and imbalanced data from a non-stationary distribution benchmarked on computer vision and reinforcement learning tasks. As an unsupervised attention mechanism given input data, this biologically plausible normalization has the potential to deal with other complicated real-world scenarios as well as reinforcement learning setting where the rewards are sparse and non-uniform. Further research is proposed to discover these scenarios and explore the behaviors among different variants. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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Exploiting learning and Scenario-based Specification Languages for the Verification and Validation of Highly Automated Driving 1

Exploiting Learning and Scenario-based Specification Languag...

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1st IEEE/ACM international workshop on Software Engineering for AI in Autonomous Systems (SEFAIAS)

作者： Damm, Werner Galbas, Roland OFFIS Oldenburg Germany Robert Bosch GmbH Chassis Syst Control Abstatt Germany

ISBN: (纸本)9781450357395

We propose a series of methods based on learning key structural properties from traffic data-basis and on statistical model checking, ultimately leading to the construction of a scenario catalogue capturing requirements for controlling criticality for highly autonomous vehicles. We sketch underlying mathematical foundations which allow to derive formal confidence levels that vehicles tested by such a scenario catalogue will maintain the required control of criticality in real traffic matching the probability distributions of key parameters of data recorded in the reference data base employed for this process.

关键词： highly automated driving requirement analysis formal specification learning statistical model-checking verification and validation

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Beating Monte Carlo Integration: a Nonasymptotic study of Kernel Smoothing methods 21

Beating Monte Carlo Integration: a Nonasymptotic Study of Ke...

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21st international Conference on Artificial Intelligence and statistics (AIstATS)

作者： Clemencon, stephan Portier, Francois Univ Paris Saclay LTCI Telecom ParisTech St Aubin France

Evaluating integrals is an ubiquitous issue and Monte Carlo methods, exploiting advances in random number generation over the last decades, offer a popular and powerful alternative to integration deterministic techniques, unsuited in particular when the domain of integration is complex. This paper is devoted to the study of a kernel smoothing based competitor built from a sequence of n >= 1 i.i.d random vectors with arbitrary continuous probability distribution f (x)dx, originally proposed in [7], from a nonasymptotic perspective. We establish a probability bound showing that the method under study, though biased, produces an estimate approximating the target integral integral(x is an element of Rd) phi(x)dx with an error bound of order o(1/root n) uniformly over a class Phi of functions phi, under weak complexity/smoothness assumptions related to the class Phi, outperforming Monte-Carlo procedures. This striking result is shown to derive from an appropriate decomposition of the maximal deviation between the target integrals and their estimates, highlighting the remarkable benefit to averaging strongly dependent terms regarding statistical accuracy in this situation. The theoretical analysis then rests on sharp probability inequalities for degenerate U-statistics. It is illustrated by numerical results in the context of covariate shift regression, providing empirical evidence of the relevance of the approach.

关键词： Random number generation

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Designing with citizens: Challenges and evaluation methods for crowd-sourced urban layouts 1

Designing with citizens: Challenges and evaluation methods f...

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1st international workshop on Cognition and Artificial Intelligence for Human-Centred Design, CAID 2017

作者： Mueller, Johannes Lu, Hangxin ETH Zurich Future Cities Laboratories Singapore-ETH Centre 1 CREATE Way CREATE Tower 138602 Singapore ETH Zurich Chair of Information Architecture Wolfgang-Pauli-Str. 27 ZurichCH-8093 Switzerland

This paper presents analysis tools for evaluating crowdsourced geometry-based design proposals for urban planning. With the Quick Urban Analysis Kit, an online platform, citizens are able to manipulate objects and create a preferred layout over a case study area. Given that our case study is on a meso-cale, our analysis is focused on the layout and plot configuration. The proposed analysis tools range from simple counting of object types and a buffer analysis to clustering and spatial autocorrelation tools. Besides these form-based criteria, perception-based criteria are also proposed to link the participating subject's assessment of the designs with the layout. Techniques deployed include supervised machine learning methods, statistical spatial tests, and simple calculations of the area size and frequency of objects. © 2018 CEUR-WS. All rights reserved.

关键词： Urban planning

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