Conferences related to Inference algorithms

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2023 Annual International Conference of the IEEE Engineering in Medicine & Biology Conference (EMBC)

The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted full papers will be peer reviewed. Accepted high quality papers will be presented in oral and poster sessions,will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE.


2020 IEEE International Conference on Robotics and Automation (ICRA)

The International Conference on Robotics and Automation (ICRA) is the IEEE Robotics and Automation Society’s biggest conference and one of the leading international forums for robotics researchers to present their work.


IEEE INFOCOM 2020 - IEEE Conference on Computer Communications

IEEE INFOCOM solicits research papers describing significant and innovative researchcontributions to the field of computer and data communication networks. We invite submissionson a wide range of research topics, spanning both theoretical and systems research.


GLOBECOM 2020 - 2020 IEEE Global Communications Conference

IEEE Global Communications Conference (GLOBECOM) is one of the IEEE Communications Society’s two flagship conferences dedicated to driving innovation in nearly every aspect of communications. Each year, more than 2,900 scientific researchers and their management submit proposals for program sessions to be held at the annual conference. After extensive peer review, the best of the proposals are selected for the conference program, which includes technical papers, tutorials, workshops and industry sessions designed specifically to advance technologies, systems and infrastructure that are continuing to reshape the world and provide all users with access to an unprecedented spectrum of high-speed, seamless and cost-effective global telecommunications services.


2020 IEEE International Symposium on Circuits and Systems (ISCAS)

The International Symposium on Circuits and Systems (ISCAS) is the flagship conference of the IEEE Circuits and Systems (CAS) Society and the world’s premier networking and exchange forum for researchers in the highly active fields of theory, design and implementation of circuits and systems. ISCAS2020 focuses on the deployment of CASS knowledge towards Society Grand Challenges and highlights the strong foundation in methodology and the integration of multidisciplinary approaches which are the distinctive features of CAS contributions. The worldwide CAS community is exploiting such CASS knowledge to change the way in which devices and circuits are understood, optimized, and leveraged in a variety of systems and applications.



Periodicals related to Inference algorithms

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Aerospace and Electronic Systems Magazine, IEEE

The IEEE Aerospace and Electronic Systems Magazine publishes articles concerned with the various aspects of systems for space, air, ocean, or ground environments.


Audio, Speech, and Language Processing, IEEE Transactions on

Speech analysis, synthesis, coding speech recognition, speaker recognition, language modeling, speech production and perception, speech enhancement. In audio, transducers, room acoustics, active sound control, human audition, analysis/synthesis/coding of music, and consumer audio. (8) (IEEE Guide for Authors) The scope for the proposed transactions includes SPEECH PROCESSING - Transmission and storage of Speech signals; speech coding; speech enhancement and noise reduction; ...


Automatic Control, IEEE Transactions on

The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...


Automation Science and Engineering, IEEE Transactions on

The IEEE Transactions on Automation Sciences and Engineering (T-ASE) publishes fundamental papers on Automation, emphasizing scientific results that advance efficiency, quality, productivity, and reliability. T-ASE encourages interdisciplinary approaches from computer science, control systems, electrical engineering, mathematics, mechanical engineering, operations research, and other fields. We welcome results relevant to industries such as agriculture, biotechnology, healthcare, home automation, maintenance, manufacturing, pharmaceuticals, retail, ...


Communications, IEEE Transactions on

Telephone, telegraphy, facsimile, and point-to-point television, by electromagnetic propagation, including radio; wire; aerial, underground, coaxial, and submarine cables; waveguides, communication satellites, and lasers; in marine, aeronautical, space and fixed station services; repeaters, radio relaying, signal storage, and regeneration; telecommunication error detection and correction; multiplexing and carrier techniques; communication switching systems; data communications; and communication theory. In addition to the above, ...



Most published Xplore authors for Inference algorithms

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Xplore Articles related to Inference algorithms

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A Computational Model for Inference Chains in Expert Systems

2009 International Conference on Intelligent Engineering Systems, 2009

This paper deals with the calculations performed in the reasoning process of rule-based expert systems, where inference chains are applied. It presents a logic model for representing the rules and the rule base of a given system. Also, the fact base of the same expert system is involved in the logic model. The proposed equivalent representation manifests itself in a ...


Learning Context-Free Grammars from Partially Structured Examples: Juxtaposition of GCS with TBL

7th International Conference on Hybrid Intelligent Systems (HIS 2007), 2007

This paper juxtaposes performance of the grammar- based classifier system (GCS) with tabular representation algorithm (TBL) on the task of inducing context-free grammars from partially structured examples. In both cases structured examples rapidly improve the efficiency of learning algorithms, although there are substantial differences in the way they work. GCS requires more attention while setting initial system parameters and structured ...


A modified space frequency decomposition algorithm for visual motion

2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698), 2003

In this paper, we present a method to decompose visual motion represented by computed optical flow using an over-complete dictionary of space frequency atoms. This is accomplished by a modified matching pursuit procedure which successively selects space and frequency localized atoms from this dictionary. In effect, this decomposition reveals the structure of optical flow field such that areas with motion ...


Classification of post operative breast cancer patient information using complex valued neural classifiers

2015 International Conference on Cognitive Computing and Information Processing(CCIP), 2015

Classification of Haberman's Survival information is useful to find out the patients survival probability after a breast cancer surgery. Dataset has been collected from a standard benchmark UCI machine learning repository. A study at the hospital named University of Chicago's Billings was conducted between the year 1958 and 1970 to identify the cancer patients who had undergone surgery for breast ...


Efficient Online Inference for Multiple Changepoint Problems

2006 IEEE Nonlinear Statistical Signal Processing Workshop, 2006

We review work on how to perform exact online inference for a class of multiple changepoint models. These models have a conditional independence structure, and require you to be able to integrate out (either analytically or numerically) the parameters associated within each segment. The computational cost per observation increases linearly with the number of observations. This algorithm is closely related ...



Educational Resources on Inference algorithms

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IEEE.tv Videos

Deep Learning & Machine Learning Inference - Ashish Sirasao - LPIRC 2019
Spiking Network Algorithms for Scientific Computing - William Severa: 2016 International Conference on Rebooting Computing
Magneto-electric Approximate Computation for Bayesian Inference - IEEE Rebooting Computing 2017
Vladimir Cherkassky - Predictive Learning, Knowledge Discovery and Philosophy of Science
Random Sparse Adaptation for Accurate Inference with Inaccurate RRAM Arrays - IEEE Rebooting Computing 2017
Learning Method of the SIC Fuzzy Inference Model - Genki Ohashi - ICRC San Mateo, 2019
Introduction: Emerging Technology for Probabilistic Inference - Pierre Bessiere at INC 2019
Part 3: Workshop on Benchmarking Quantum Computational Devices and Systems - ICRC 2018
FPGA demonstrator of a Programmable ML Inference Accelerator - Martin Foltin - ICRC San Mateo, 2019
IMS 2012 Microapps - Custom OFDM Validation of Wireless/Military DSP Algorithms and RF Components Daren McClearnon, Jin-Biao Xu, Agilent EEsof
Cultural Algorithms: Harnessing the Power of Social Intelligence 1
Data for Good: Data Science at Columbia - Jeannette Wing - IEEE Sarnoff Symposium, 2019
Julian Togelius: Algorithms That Play & Design Games
Optimization Algorithms for Signal Processing
Cultural Algorithms: Harnessing the Power of Social Intelligence 2
Stochastic Sampling Machine for Bayesian Inference - Raphael Frisch at INC 2019
The Art of MobileNet Design - Andrew Howard - LPIRC 2019
Welcome & Overview - Emerging Technology for Probabilistic Inference - Arvind Kumar at INC 2019
The Era of AI Hardware - 2018 IEEE Industry Summit on the Future of Computing
Life Through the Eyes of a Machine

IEEE-USA E-Books

  • A Computational Model for Inference Chains in Expert Systems

    This paper deals with the calculations performed in the reasoning process of rule-based expert systems, where inference chains are applied. It presents a logic model for representing the rules and the rule base of a given system. Also, the fact base of the same expert system is involved in the logic model. The proposed equivalent representation manifests itself in a logic network. After that, a four-valued logic algebra is introduced. This algebra is used for the calculations where forward chaining is carried out. Next, the notion of line-value justification is described. This operation is applied in the backward chaining process, also on the base of the previously introduced four- valued logic. The paper describes two exact algorithms which serve for the forward and backward chaining processes. These algorithms make it possible to be implemented by a computer program, resulting in an efficient inference engine of an expert system. The achieved result enhances the reliability and usability of the intelligent software systems which is extremely important in embedded environments.

  • Learning Context-Free Grammars from Partially Structured Examples: Juxtaposition of GCS with TBL

    This paper juxtaposes performance of the grammar- based classifier system (GCS) with tabular representation algorithm (TBL) on the task of inducing context-free grammars from partially structured examples. In both cases structured examples rapidly improve the efficiency of learning algorithms, although there are substantial differences in the way they work. GCS requires more attention while setting initial system parameters and structured examples provide a bit more information than in TBL but on the other hand it is more efficient when tested against the same examples' sets.

  • A modified space frequency decomposition algorithm for visual motion

    In this paper, we present a method to decompose visual motion represented by computed optical flow using an over-complete dictionary of space frequency atoms. This is accomplished by a modified matching pursuit procedure which successively selects space and frequency localized atoms from this dictionary. In effect, this decomposition reveals the structure of optical flow field such that areas with motion at different spatial location and scale are identified. In addition, our method maintains the original resolution of the image when performing the decomposition at each scale. This minimizes the amount of the information lost in the decomposition process. We perform experiments using both synthetic and real image data sets to demonstrate the effectiveness of the proposed method.

  • Classification of post operative breast cancer patient information using complex valued neural classifiers

    Classification of Haberman's Survival information is useful to find out the patients survival probability after a breast cancer surgery. Dataset has been collected from a standard benchmark UCI machine learning repository. A study at the hospital named University of Chicago's Billings was conducted between the year 1958 and 1970 to identify the cancer patients who had undergone surgery for breast cancer and survived. The data obtained are classified using a fully complex valued classifier in this paper. Classifying patient's survival after five years and patients death within five years is a challenging prognosis problem. The effectiveness of the classification achieved can be used by the clinicians for the treatment of patients in the hospitals. For achieving better discrimination, the proposed method uses a fully complex valued fast learning classifier with Gd activation function in the hidden layer. Comparing the classification efficiency of FC-FLC with other networks available in the literature, FC-FLC provides a better classification performance than the SRAN, MCFIS and ELM classifier.

  • Efficient Online Inference for Multiple Changepoint Problems

    We review work on how to perform exact online inference for a class of multiple changepoint models. These models have a conditional independence structure, and require you to be able to integrate out (either analytically or numerically) the parameters associated within each segment. The computational cost per observation increases linearly with the number of observations. This algorithm is closely related to a particle filter algorithm, and we describe how efficient resampling algorithms can be used to produce an accurate particle filter for this class of models.

  • Recognition and geometrical on-line learning algorithm of probability distributions

    An online learning algorithm for probability distributions is constructed in a reparameterization invariant form. It enables us to identify the distributions which transform from one to another by reparameterization. This is an essential property not only for pattern recognition problems but also for the property of 'information'. We can find the algorithm to be optimal, since conformal gauge reduces the problem to a noncovariant case.

  • Estimation of the Bayesian network architecture for object tracking in video sequences

    It was recently proposed the use of Bayesian networks for object tracking. Bayesian networks allow modeling the interaction among detected trajectories, in order to obtain reliable object identification in the presence of occlusions. However, the architecture of the Bayesian network has been defined using simple heuristic rules, which fail in many cases. This paper addresses the above problem and presents a new method to estimate the network architecture from the video sequences using supervised learning techniques. Experimental results are presented showing that significant performance gains (increase of accuracy and decrease of complexity) are achieved by the proposed methods.

  • Expectation propagation for estimating the parameters of the beta distribution

    Parameter estimation for the beta distribution is analytically intractable due to the integration expression in the normalization constant. For maximum likelihood estimation, numerical methods can be used to calculate the parameters. For Bayesian estimation, we can utilize different approximations to the posterior parameter distribution. A method based on the variational inference (VI) framework reported the posterior mean of the parameters analytically but the approximating distribution violated the correlation between the parameters. We now propose a method via the expectation propagation (EP) framework to approximate the posterior distribution analytically and capture the correlation between the parameters. Compared to the method based on VI, the EP based algorithm performs better with small amounts of data and is more stable.

  • A new low-cost reader system for ELISA plates based on automated analysis of digital pictures

    This work presents an approach for efficient interpretation of enzyme linked immuno specific assay (ELISA) diagnostic plates. ELISA tests are among the most powerful diagnostic tools for infectious diseases and media are readily available, but their interpretation requires expensive equipment. This implementation was developed around open-source libraries and designed to be as versatile as possible

  • An Ontology Term Extracting Method Based on Latent Dirichlet Allocation

    Ontology plays an important part on Semantic Web, Information Retrieval, and Intelligent Information Integration etc. Ontology learning gets widely studied due to many problems in totally manual ontology construction. Term extraction influences many respects of ontology learning as it's the basis of ontology learning hierarchical structure. This paper mines topics of the corpus based on Latent Dirichlet Allocation (LDA) which uses Variational Inference and Expectation-Maximization (EM) Algorithm to estimate model parameters. With the help of irrelevant vocabulary, the paper provides better experimental results which show that the distribution of topics on terms reveals latent semantic features of the corpus and relevance among words.



Standards related to Inference algorithms

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No standards are currently tagged "Inference algorithms"