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An improved architecture for cooperative and comparative neurons (CCNs) in neural network

An improved architecture for cooperative and comparative neu...

3rd International workshop on artificial neural networks and Intelligent Information Processing - ANNIIP 2007;In Conjunction with ICINCO 2007

作者： Islam, Md. Kamrul School of Computing Queen's University Kingston K7L 3N6 ON Canada

ISBN: (纸本)9789728865863

The ability to store and retrieve information is critical in any type of neural network. In neural network, the memory particularly associative memory, can be defined as the one in which the input pattern leads to the response of a stored pattern (output vector) that corresponds to the input vector. During the learning phase the memory is fed with a number of input vectors that it learns and remembers and in the recall phase when some known input is presented to it, the network exactly recalls and reproduces the required output vector. In this paper, we improve and increase the storing ability of the memory model proposed in[1]. Besides, we show that there are certain instances where the algorithm in[1] does not produce the desired performance by retrieving exactly the correct vector from the memory. That is, in their algorithm, a number of output vectors can become activated from the stimulus of an input vector while the desired output is just a single correct vector. We propose a simple solution that overcomes this and can uniquely and correctly determine the output vector stored in the associative memory when an input vector is applied. Thus we provide a more general scenario of this neural network memory model consisting of memory element called Competitive Cooperative Neuron (CCN).

关键词： neural networks

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Unsupervised spatial pattern classification of electrical-wafer-sorting maps in semiconductor manufacturing

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pattern recognition LETTERS 2005年第12期26卷 1857-1865页

作者： Di Palma, F De Nicolao, G Miraglia, G Pasquinetti, E Piccinini, F Univ Pavia Dipartimento Informat & Sistemist I-27100 Pavia Italy STMicroelect I-20041 Agrate Brianza Italy

in semiconductor manufacturing, the spatial pattern of failed devices in a wafer can give precious hints on which step of the process is responsible for the failures. In the literature, Kohonen's Self Organizing Feature Maps (SOM) and Adaptive Resonance Theory 1 (ART1) architectures have been compared, concluding that the latter are to be preferred. However, both the simulated and the real data sets used for validation and comparison were very limited. In this paper, the use of ART1 and SOM as wafer classifiers is re-assessed on much more extensive simulated and real data sets. We conclude that ART1 is not adequate, whereas SOM provide completely satisfactory results including visually effective representation of spatial failure probability of the pattern classes. (c) 2005 Elsevier B.V. All rights reserved.

关键词： semiconductor manufacturing wafer maps self organizing feature maps adaptive resonance theory

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3rd International workshop on Computer Vision for Analysis of Underwater Imagery, CVAUI 2018, 7th International workshop on Computational Forensics, IWCF 2018, and International workshop on Multimedia Information Processing for Personality and Social networks Analysis, MIPPSNA 2018 held at 24th International Conference on pattern recognition, ICPR 2018

3rd International Workshop on Computer Vision for Analysis o...

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3rd International workshop on Computer Vision for Analysis of Underwater Imagery, CVAUI 2018, 7th International workshop on Computational Forensics, IWCF 2018, and International workshop on Multimedia Information Processing for Personality and Social networks Analysis, MIPPSNA 2018 held at 24th International Conference on pattern recognition, ICPR 2018

ISBN: (纸本)9783030057916

The proceedings contain 16 papers. The special focus in this conference is on Computer Vision for Analysis of Underwater Imagery. The topics include: Categorization of Document Image Tampering Techniques and How to Identify Them;overview of the Multimedia Information Processing for Personality & Social networks Analysis Contest;handwritten Texts for Personality Identification Using Convolutional neural networks;recognition of Apparent Personality Traits from Text and Handwritten Images;multimodal Database of Emotional Speech, Video and Gestures;from Text to Speech: A Multimodal Cross-Domain Approach for Deception Detection;marine Snow Removal Using a Fully Convolutional 3D neural Network Combined with an Adaptive Median Filter;strategies for Tackling the Class Imbalance Problem in Marine Image Classification;an Online Platform for Underwater Image Quality Evaluation;tracking Sponge Size and Behaviour with Fixed Underwater Observatories;enhancement of Low-Lighting Underwater Images Using Dark Channel Prior and Fast Guided Filters;Underwater-GAN: Underwater Image Restoration via Conditional Generative Adversarial Network;single Image Plankton 3D Reconstruction from Extended Depth of Field Shadowgraph;a Novel Method for Race Determination of Human Skulls.

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Abstract echo state networks 9th

Abstract echo state networks

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9th iapr TC3 International workshop on artificial neural networks in pattern recognition, ANNPR 2020

作者： Senn, Christoph Walter Kumazawa, Itsuo Department of Information and Communications Engineering School of Engineering Tokyo Institute of Technology Tokyo Japan

ISBN: (纸本)9783030583088

Noisy or adverse input is a threat to the safe deployment of neural networks in production. To ensure the safe operations of such networks they need to be hardened to work under such conditions. Abstract interpretation, as a tool to formally verify properties of computations, can be used for this task. But, to date, this has mostly been studied for feed-forward networks, but not so for recurrent neural networks. For a subclass of recurrent neural networks, called echo state networks, we propose a new training algorithm using abstract interpretation and convex programming to increase the robustness against noisy inputs. Our empirical results show that the new training regime improves the performance of echo state networks in an open loop setup under high noise and generally improves their performance in closed loop setups. © Springer Nature Switzerland AG 2020.

关键词： Recurrent neural networks

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Content-Based Retrieval and Classification of Ultrasound Medical Images of Ovarian Cysts

Content-Based Retrieval and Classification of Ultrasound Med...

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4th workshop on artificial neural networks in pattern recognition

作者： Sohail, Abu Sayeed Md Bhattacharya, Prabir Mudur, Sudhir P. Krishnamurthy, Srinivasan Gilbert, Lucy Concordia Univ Dept Comp Sci & Software Engn Montreal PQ Canada Univ Cincinnati Dept Comp Sci Cincinnati OH 45221 USA Royal Victoria Hosp Dept Obstet & Gynaecol Montreal PQ H3A 1A1 Canada

ISBN: (纸本)9783642121586

This paper presents a combined method of content-based retrieval and classification of ultrasound medical images representing three types of ovarian cysts: Simple Cyst, Endometrioma, and Teratoma. Combination of histogram moments and Gray Level Co-Occurrence Matrix (GLCM) based statistical texture descriptors has been proposed as the features for retrieving and classifying ultrasound images. To retrieve images, relevance between the query image and the target images has been measured using a similarity model based on Gower's similarity coefficient. Image classification has been performed applying Fuzzy k-Nearest Neighbour (k-NN) classification technique. A database of 478 ultrasound ovarian images has been used to verify the retrieval and classification accuracy of the proposed system. In retrieving ultrasound images, the proposed method has demonstrated above 79% and 75% of average precision considering the first 20 and 40 retrieved images respectively. Further, 88.12% of average classification accuracy has been achieved in classifying ultrasound images using the proposed method.

关键词： Ultrasound Medical Image Retrieval Ovarian Cyst Classification Texture Feature Histogram Moments Fuzzy k-NN

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innvestigate-gui - explaining neural networks through an interactive visualization tool 9th

innvestigate-gui - explaining neural networks through an int...

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9th iapr TC3 International workshop on artificial neural networks in pattern recognition, ANNPR 2020

作者： Garcea, Fabio Famouri, Sina Valentino, Davide Morra, Lia Lamberti, Fabrizio Dipartimento di Automatica e Informatica Politecnico di Torino Turin Italy

ISBN: (纸本)9783030583088

In recent years, deep neural networks have reached state of the art performance across many different domains. Computer vision in particular has benefited immensely from deep learning. Despite their high performance, deep neural networks often lack interpretability and are mostly regarded as a black box. Therefore, the availability of tools capable to provide insights into the models and identify potential errors is crucial. Such tools need to seamlessly integrate within the workflow of data scientists and ML researchers. In this paper we propose iNNvestigate-GUI, an open-source graphical toolbox which offers an extensive set of functionalities for users to compare different networks behavior and give an explanation to their outputs. © Springer Nature Switzerland AG 2020.

关键词： Deep neural networks

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Unsupervised Hierarchical Disentanglement for Video Prediction 26

Unsupervised Hierarchical Disentanglement for Video Predicti...

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26th International Conference on pattern recognition / 8th International workshop on Image Mining - Theory and Applications (IMTA)

作者： Motallebi, Mohammad Reza Westfechtel, Thomas Li, Yang Harada, Tatsuya Univ Tokyo Tokyo Japan RIKEN Tokyo Japan

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Video prediction is a complicated task as countless possible future frames exist that are equally plausible. While recent work have made progress in the prediction and generation of future video frames, these work have not attempted to disentangle different features of videos such as an object's structure and its dynamics. Such a disentanglement would allow one to control these aspects to some extent in the prediction phase, while at the same time maintain the object's intrinsic properties that are learned as the model's internal representation. In this work, we propose Ladder Variational Recurrent neural networks (LVRNN). We employ a type of ladder autoencoder shown to be effective for feature disentanglement on images and apply it to the Variational Recurrent neural Network (VRNN) architecture, which has been used for video prediction. We rely on extracted keypoints in each frame to separate the structure from the visual features. We then show how different levels of the ladder network learn to disentangle features and demonstrate that each of these levels can be used for controlling different aspects of future frames such as structure and dynamics. We evaluate our method on the Human3.6M and BAIR robot datasets. We show that our method is able to perform hierarchical disentanglement, yet provide reasonable results compared to similar methods.

关键词： Visualization Recurrent neural networks Predictive models Feature extraction Graph neural networks pattern recognition Object tracking

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4th iapr TC 9 workshop on Multimodal pattern recognition of Social Signals in Human-Computer-Interaction, MPRSS 2016

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4th iapr TC 9 workshop on Multimodal pattern recognition of Social Signals in Human-Computer-Interaction, MPRSS 2016

ISBN: (纸本)9783319592589

The proceedings contain 13 papers. The special focus in this conference is on Multimodal pattern recognition of Social Signals in Human-Computer-Interaction. The topics include: Bimodal recognition of Cognitive Load Based on Speech and Physiological Changes;Human Mobility-pattern Discovery and Next-Place Prediction from GPS Data;Fusion Architectures for Multimodal Cognitive Load recognition;Performance Analysis of Gesture recognition Classifiers for Building a Human Robot Interface;On Automatic Question Answering Using Efficient Primal-Dual Models;Hierarchical Bayesian Multiple Kernel Learning Based Feature Fusion for Action recognition;Audio Visual Speech recognition Using Deep Recurrent neural networks;Audio-Visual recognition of Pain Intensity;The SenseEmotion Database: A Multimodal Database for the Development and Systematic Validation of an Automatic Painand Emotion-recognition System;Photometric Stereo for 3D Face Reconstruction Using Non Linear Illumination Models and Recursively Measured Action Units.

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Character recognition with neural assemblies 3rd

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3rd International workshop on artificial neural networks, IWANN 1995

作者： Vico, Francisco J. Ortega, F. Almaraz, J. Sandoval, F. E.T.S.I. de Telecomunicaciòn Dpto. Tecnologfa Electrìnica Dpto. Psicobiologfa Universidad de Màlaga Plaza E1 Ejido s/n Màlaga29013 Spain

ISBN: (纸本)3540594973

In living beings, any pattern recognition task involves complex processes of concepts formation. We propose a model based on the principle of neural assemblies to develop internal representations of characters. neural assemblies self-organize themselves by extracting the relevant information of the characters set depending on the previously stored characters. The principal consequences of this are that less storage capacity is needed, and it makes easer the clasiffication with a delta rule. Examples of this task are exposed, where it can be seen the way in which the assemblies store the characters, and how the classification of the internal representations is performed. © 1995, Springer Verlag. All rights reserved.

关键词： Character recognition

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Bio-inspired optic flow from event-based neuromorphic sensor input 6

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6th iapr TC3 International workshop on artificial neural networks for pattern recognition, ANNPR 2014

作者： Tschechne, Stephan Sailer, Roman Neumann, Heiko Inst. for Neural Information Processing Ulm University Albert-Einstein-Allee 1 Ulm89069 Germany

ISBN: (纸本)9783319116556

Computational models of visual processing often use framebased image acquisition techniques to process a temporally changing stimulus. This approach is unlike biological mechanisms that are spikebased and independent of individual frames. The neuromorphic Dynamic Vision Sensor (DVS) [Lichtsteiner et al., 2008] provides a stream of independent visual events that indicate local illumination changes, resembling spiking neurons at a retinal level. We introduce a new approach for the modelling of cortical mechanisms of motion detection along the dorsal pathway using this type of representation. Our model combines filters with spatio-temporal tunings also found in visual cortex to yield spatiotemporal and direction specificity. We probe our model with recordings of test stimuli, articulated motion and ego-motion. We show how our approach robustly estimates optic flow and also demonstrate how this output can be used for classification purposes. © Springer International Publishing Switzerland 2014.

关键词： Optical flows

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