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1
Learning to Parse Grounded Language using Reservoir Computing
In: ICDL-Epirob 2019 - Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics ; https://hal.inria.fr/hal-02422157 ; ICDL-Epirob 2019 - Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics, Aug 2019, Olso, Norway. ⟨10.1109/devlrn.2019.8850718⟩ ; https://ieeexplore.ieee.org/abstract/document/8850718 (2019)
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2
Image-Based Localization of User-Interfaces
In: Master's Projects (2019)
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3
Deep Machine Learning and Neural Networks: An Overview ...
Chandrahas Mishra; D. L. Gupta. - : Zenodo, 2017
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4
Deep Machine Learning and Neural Networks: An Overview ...
Chandrahas Mishra; D. L. Gupta. - : Zenodo, 2017
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5
Headline Generation using Deep Neural Networks
In: Master's Projects (2017)
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6
Predicting the consequence of action in digital control state spaces
In: https://hal.archives-ouvertes.fr/hal-01374155 ; 2016 (2016)
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7
Cognitive responsive e-assessment of constructive e-learning ...
Morales-Martinez, Guadalupe Elizabeth; Lopez-Ramirez, Ernesto Octavio. - : Journal of e-Learning and Knowledge Society, 2016
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8
Detection of Locations of Key Points on Facial Images
In: Master's Projects (2016)
Abstract: In field of computer vision research, One of the most important branch is Face recognition. It targets at finding size and location of human face on digital image, by identifying and separating faces from the surrounding objects like building, plants etc. For the purpose of developing an advanced face recognition algorithm, Detection of facial key points is the basic and very important task, basically it is about finding out the location of specific key points on facial images. This key points can be mouths, noses, left eyes, right eyes and so on. For implementation of solution, I have used amazon ec2 gpu instance and convolutional networks consisting of multiple levels. Outputs of multiple networks are fused at every level for accurate and robust evaluation. At the stage of initialization, high level features are extracted over the whole face region which helps in locating key points with high accuracy. Local minimum occurred by data corruption and ambiguity in difficult samples of image caused by occlusions, extreme lightings and large variations in poses can be avoided by this method. At later levels, training of networks is adopted to locally refine the initial predictions and the input supplied to them are limited to smaller regions around the predictions that are obtained in initial stage.
Keyword: Artificial Intelligence and Robotics; Facial Recognition Neural Nets GPU programming
URL: https://scholarworks.sjsu.edu/etd_projects/470
https://scholarworks.sjsu.edu/cgi/viewcontent.cgi?article=1470&context=etd_projects
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9
Document Image Parsing and Understanding using Neuromorphic Architecture
In: DTIC (2015)
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10
A Fast Variational Approach for Learning Markov Random Field Language Models
In: DTIC (2015)
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11
A multicategorical view of the speech acoustic-phonetic decoding ; Une approche multicatégorielle du décodage acoustico-phonétique de la parole
In: DigiCosme Datasense Research days ; https://hal.archives-ouvertes.fr/hal-01887795 ; DigiCosme Datasense Research days, Labex Digicosme, Université Paris-Saclay, Jul 2014, Orsay, France (2014)
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12
A Novel Scheme for Speaker Recognition Using a Phonetically-Aware Deep Neural Network
In: DTIC (2014)
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13
Discovery of Deep Structure from Unlabeled Data
In: DTIC (2014)
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14
Mind: meet network. Emergence of features in conceptual metaphor.
Jelec, Anna; Jaworska, Dorota. - : KSU, 2011
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15
Improving the Capacity of Language Recognition Systems to Handle Rare Languages Using Radio Broadcast Data
In: DTIC (2011)
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16
Language and Cognition Interaction Neural Mechanisms
In: DTIC (2011)
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17
Relevant Projects
In: http://www.cs.toronto.edu/~kazemian/resume.pdf (2008)
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18
Simulation of National Intelligence Process with Fusion
In: DTIC (2008)
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19
Transient Cognitive Dynamics, Metastability, and Decision Making
In: DTIC (2008)
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20
Modélisation connexionniste du traitement de l'accès lexical et mise en relation avec des données électro-encéphalographiques
In: Colloque de l'Association pour la Recherche Cognitive - ARCo'07 : Cognition – Complexité – Collectif ; https://hal.inria.fr/inria-00179885 ; Colloque de l'Association pour la Recherche Cognitive - ARCo'07 : Cognition – Complexité – Collectif, ARCo - INRIA - EKOS, Nov 2007, Nancy, France. pp.Poster (2007)
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