�ݺ�ߣshows by User: TraianRebedea

�ݺ�ߣshows by User: TraianRebedea / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: TraianRebedea / Tue, 10 Mar 2020 19:10:22 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: TraianRebedea An Evolution of Deep Learning Models for AI2 Reasoning Challenge /slideshow/an-evolution-of-deep-learning-models-for-ai2-reasoning-challenge/230021238 anevolutionofdeeplearningmodelsforarc-200310191022
An Evolution of Deep Learning Models for AI2 Reasoning Challenge: From Information Retrieval Models, to RNNs and Transformers]]>
An Evolution of Deep Learning Models for AI2 Reasoning Challenge: From Information Retrieval Models, to RNNs and Transformers]]> Tue, 10 Mar 2020 19:10:22 GMT /slideshow/an-evolution-of-deep-learning-models-for-ai2-reasoning-challenge/230021238 TraianRebedea@slideshare.net(TraianRebedea) An Evolution of Deep Learning Models for AI2 Reasoning Challenge TraianRebedea An Evolution of Deep Learning Models for AI2 Reasoning Challenge: From Information Retrieval Models, to RNNs and Transformers <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/anevolutionofdeeplearningmodelsforarc-200310191022-thumbnail.jpg?width=120&height=120&fit=bounds" /> An Evolution of Deep Learning Models for AI2 Reasoning Challenge: From Information Retrieval Models, to RNNs and Transformers

An Evolution of Deep Learning Models for AI2 Reasoning Challenge from Traian Rebedea

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0 AI @ Wholi - Bucharest.AI Meetup #5 /slideshow/ai-wholi-bucharestai-meetup-5/83679701 bucharest-ai-aiwholi-171208225434
Wholi: The right people find each other (at the right time) Two key elements in this talk: •PART 1: Machine learning for entity extraction Natural language processing (NLP), information extraction •PART 2: Matching profiles using deep learning classifier Deep learning, word embeddings ]]>
Wholi: The right people find each other (at the right time) Two key elements in this talk: •PART 1: Machine learning for entity extraction Natural language processing (NLP), information extraction •PART 2: Matching profiles using deep learning classifier Deep learning, word embeddings ]]> Fri, 08 Dec 2017 22:54:34 GMT /slideshow/ai-wholi-bucharestai-meetup-5/83679701 TraianRebedea@slideshare.net(TraianRebedea) AI @ Wholi - Bucharest.AI Meetup #5 TraianRebedea Wholi: The right people find each other (at the right time) Two key elements in this talk: •PART 1: Machine learning for entity extraction Natural language processing (NLP), information extraction •PART 2: Matching profiles using deep learning classifier Deep learning, word embeddings <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/bucharest-ai-aiwholi-171208225434-thumbnail.jpg?width=120&height=120&fit=bounds" /> Wholi: The right people find each other (at the right time) Two key elements in this talk: •PART 1: Machine learning for entity extraction Natural language processing (NLP), information extraction •PART 2: Matching profiles using deep learning classifier Deep learning, word embeddings

AI @ Wholi - Bucharest.AI Meetup #5 from Traian Rebedea

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0 Deep neural networks for matching online social networking profiles /slideshow/deep-neural-networks-for-matching-online-social-networking-profiles/83434291 iccci2017ciorbarurebedea-171205222733
> Proposed a large dataset for matching online social networking profiles ›This allowed us to train a deep neural network for profile matching using both domain-specific features and word embeddings generated from textual descriptions from social profiles ›Experiments showed that the NN surpassed both unsupervised and supervised models, achieving a high precision (P = 0.95) with a good recall rate (R = 0.85)]]>
> Proposed a large dataset for matching online social networking profiles ›This allowed us to train a deep neural network for profile matching using both domain-specific features and word embeddings generated from textual descriptions from social profiles ›Experiments showed that the NN surpassed both unsupervised and supervised models, achieving a high precision (P = 0.95) with a good recall rate (R = 0.85)]]> Tue, 05 Dec 2017 22:27:33 GMT /slideshow/deep-neural-networks-for-matching-online-social-networking-profiles/83434291 TraianRebedea@slideshare.net(TraianRebedea) Deep neural networks for matching online social networking profiles TraianRebedea > Proposed a large dataset for matching online social networking profiles ›This allowed us to train a deep neural network for profile matching using both domain-specific features and word embeddings generated from textual descriptions from social profiles ›Experiments showed that the NN surpassed both unsupervised and supervised models, achieving a high precision (P = 0.95) with a good recall rate (R = 0.85) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/iccci2017ciorbarurebedea-171205222733-thumbnail.jpg?width=120&height=120&fit=bounds" /> > Proposed a large dataset for matching online social networking profiles ›This allowed us to train a deep neural network for profile matching using both domain-specific features and word embeddings generated from textual descriptions from social profiles ›Experiments showed that the NN surpassed both unsupervised and supervised models, achieving a high precision (P = 0.95) with a good recall rate (R = 0.85)

Deep neural networks for matching online social networking profiles from Traian Rebedea

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0 Intro to Deep Learning for Question Answering /slideshow/intro-to-deep-learning-for-auestion-answering/71562763 introtodeeplearningforquestionanswering-170130223740
A presention of 3 papers on question answering using deep neural networks (CNNs, DT-RNN and LSTM) to improve vector space representation. ]]>
A presention of 3 papers on question answering using deep neural networks (CNNs, DT-RNN and LSTM) to improve vector space representation. ]]> Mon, 30 Jan 2017 22:37:40 GMT /slideshow/intro-to-deep-learning-for-auestion-answering/71562763 TraianRebedea@slideshare.net(TraianRebedea) Intro to Deep Learning for Question Answering TraianRebedea A presention of 3 papers on question answering using deep neural networks (CNNs, DT-RNN and LSTM) to improve vector space representation. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/introtodeeplearningforquestionanswering-170130223740-thumbnail.jpg?width=120&height=120&fit=bounds" /> A presention of 3 papers on question answering using deep neural networks (CNNs, DT-RNN and LSTM) to improve vector space representation.

Intro to Deep Learning for Question Answering from Traian Rebedea

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0 What is word2vec? /slideshow/what-is-word2vec/52297101 whatisword2vec-150901142702-lva1-app6891
General presentation about the word2vec model, including some explanations for training and reference to the implicit factorization done by the model]]>
General presentation about the word2vec model, including some explanations for training and reference to the implicit factorization done by the model]]> Tue, 01 Sep 2015 14:27:02 GMT /slideshow/what-is-word2vec/52297101 TraianRebedea@slideshare.net(TraianRebedea) What is word2vec? TraianRebedea General presentation about the word2vec model, including some explanations for training and reference to the implicit factorization done by the model <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/whatisword2vec-150901142702-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds" /> General presentation about the word2vec model, including some explanations for training and reference to the implicit factorization done by the model

What is word2vec? from Traian Rebedea

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0 How useful are semantic links for the detection of implicit references in cscl chats /TraianRebedea/how-useful-are-semantic-links-for-the-detection-of-implicit-references-in-cscl-chats howusefularesemanticlinksforthedetectionofimplicitreferencesincsclchats-150819120928-lva1-app6892
Roedunet 2014 Conference Paper]]>
Roedunet 2014 Conference Paper]]> Wed, 19 Aug 2015 12:09:28 GMT /TraianRebedea/how-useful-are-semantic-links-for-the-detection-of-implicit-references-in-cscl-chats TraianRebedea@slideshare.net(TraianRebedea) How useful are semantic links for the detection of implicit references in cscl chats TraianRebedea Roedunet 2014 Conference Paper <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/howusefularesemanticlinksforthedetectionofimplicitreferencesincsclchats-150819120928-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds" /> Roedunet 2014 Conference Paper

How useful are semantic links for the detection of implicit references in cscl chats from Traian Rebedea

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0 A focused crawler for romanian words discovery /slideshow/a-focused-crawler-for-romanian-words-discovery/51807885 afocusedcrawlerforromanianwordsdiscovery-150819120759-lva1-app6891
Roedunet 2014 Conference paper]]>
Roedunet 2014 Conference paper]]> Wed, 19 Aug 2015 12:07:59 GMT /slideshow/a-focused-crawler-for-romanian-words-discovery/51807885 TraianRebedea@slideshare.net(TraianRebedea) A focused crawler for romanian words discovery TraianRebedea Roedunet 2014 Conference paper <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/afocusedcrawlerforromanianwordsdiscovery-150819120759-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds" /> Roedunet 2014 Conference paper

A focused crawler for romanian words discovery from Traian Rebedea

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0 Detecting and Describing Historical Periods in a Large Corpora /slideshow/detecting-and-describing-historical-periods-in-a-large-corpora/41759265 detectinganddescribinghistoricalperiodsinalarge-141119094500-conversion-gate01
Many historic periods (or events) are remembered by slogans, expressions or words that are strongly linked to them. Educated people are also able to determine whether a particular word or expression is related to a specific period in human history. The present paper aims to establish correlations between significant historic periods (or events) and the texts written in that period. In order to achieve this, we have developed a system that automatically links words (and topics discovered using Latent Dirichlet Allocation) to periods of time in the recent history. For this analysis to be relevant and conclusive, it must be undertaken on a representative set of texts written throughout history. To this end, instead of relying on manually selected texts, the Google Books Ngram corpus has been chosen as a basis for the analysis. Although it provides only word n-gram statistics for the texts written in a given year, the resulting time series can be used to provide insights about the most important periods and events in recent history, by automatically linking them with specific keywords or even LDA topics.]]>
Many historic periods (or events) are remembered by slogans, expressions or words that are strongly linked to them. Educated people are also able to determine whether a particular word or expression is related to a specific period in human history. The present paper aims to establish correlations between significant historic periods (or events) and the texts written in that period. In order to achieve this, we have developed a system that automatically links words (and topics discovered using Latent Dirichlet Allocation) to periods of time in the recent history. For this analysis to be relevant and conclusive, it must be undertaken on a representative set of texts written throughout history. To this end, instead of relying on manually selected texts, the Google Books Ngram corpus has been chosen as a basis for the analysis. Although it provides only word n-gram statistics for the texts written in a given year, the resulting time series can be used to provide insights about the most important periods and events in recent history, by automatically linking them with specific keywords or even LDA topics.]]> Wed, 19 Nov 2014 09:45:00 GMT /slideshow/detecting-and-describing-historical-periods-in-a-large-corpora/41759265 TraianRebedea@slideshare.net(TraianRebedea) Detecting and Describing Historical Periods in a Large Corpora TraianRebedea Many historic periods (or events) are remembered by slogans, expressions or words that are strongly linked to them. Educated people are also able to determine whether a particular word or expression is related to a specific period in human history. The present paper aims to establish correlations between significant historic periods (or events) and the texts written in that period. In order to achieve this, we have developed a system that automatically links words (and topics discovered using Latent Dirichlet Allocation) to periods of time in the recent history. For this analysis to be relevant and conclusive, it must be undertaken on a representative set of texts written throughout history. To this end, instead of relying on manually selected texts, the Google Books Ngram corpus has been chosen as a basis for the analysis. Although it provides only word n-gram statistics for the texts written in a given year, the resulting time series can be used to provide insights about the most important periods and events in recent history, by automatically linking them with specific keywords or even LDA topics. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/detectinganddescribinghistoricalperiodsinalarge-141119094500-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Many historic periods (or events) are remembered by slogans, expressions or words that are strongly linked to them. Educated people are also able to determine whether a particular word or expression is related to a specific period in human history. The present paper aims to establish correlations between significant historic periods (or events) and the texts written in that period. In order to achieve this, we have developed a system that automatically links words (and topics discovered using Latent Dirichlet Allocation) to periods of time in the recent history. For this analysis to be relevant and conclusive, it must be undertaken on a representative set of texts written throughout history. To this end, instead of relying on manually selected texts, the Google Books Ngram corpus has been chosen as a basis for the analysis. Although it provides only word n-gram statistics for the texts written in a given year, the resulting time series can be used to provide insights about the most important periods and events in recent history, by automatically linking them with specific keywords or even LDA topics.

Detecting and Describing Historical Periods in a Large Corpora from Traian Rebedea

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0 Practical machine learning - Part 1 /slideshow/practical-machine-learning-part-1/35250583 practicalmachinelearning-140529025808-phpapp02
Practical Machine Learning - Part 1 contains: - Basic notations of ML (what tasks are there, what is a model, how to measure performance) - A couple of examples of problems and solutions (taken from previous work) - A brief presentation of open-source software used for ML (R, scikit-learn, Weka)]]>
Practical Machine Learning - Part 1 contains: - Basic notations of ML (what tasks are there, what is a model, how to measure performance) - A couple of examples of problems and solutions (taken from previous work) - A brief presentation of open-source software used for ML (R, scikit-learn, Weka)]]> Thu, 29 May 2014 02:58:08 GMT /slideshow/practical-machine-learning-part-1/35250583 TraianRebedea@slideshare.net(TraianRebedea) Practical machine learning - Part 1 TraianRebedea Practical Machine Learning - Part 1 contains: - Basic notations of ML (what tasks are there, what is a model, how to measure performance) - A couple of examples of problems and solutions (taken from previous work) - A brief presentation of open-source software used for ML (R, scikit-learn, Weka) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/practicalmachinelearning-140529025808-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Practical Machine Learning - Part 1 contains: - Basic notations of ML (what tasks are there, what is a model, how to measure performance) - A couple of examples of problems and solutions (taken from previous work) - A brief presentation of open-source software used for ML (R, scikit-learn, Weka)

Practical machine learning - Part 1 from Traian Rebedea

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0 Propunere de dezvoltare a carierei universitare /slideshow/propunere-de-dezvoltare-a-carierei-universitare/26436793 propunerededezvoltareacariereiuniversitare-130922165338-phpapp01
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]]> Sun, 22 Sep 2013 16:53:38 GMT /slideshow/propunere-de-dezvoltare-a-carierei-universitare/26436793 TraianRebedea@slideshare.net(TraianRebedea) Propunere de dezvoltare a carierei universitare TraianRebedea <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/propunerededezvoltareacariereiuniversitare-130922165338-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" />

Propunere de dezvoltare a carierei universitare from Traian Rebedea

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0 Automatic plagiarism detection system for specialized corpora /slideshow/automatic-plagiarism-detection-system-for-specialized-corpora/26436774 automaticplagiarismdetectionsystemforspecializedcorpora-130922165259-phpapp01
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]]> Sun, 22 Sep 2013 16:52:59 GMT /slideshow/automatic-plagiarism-detection-system-for-specialized-corpora/26436774 TraianRebedea@slideshare.net(TraianRebedea) Automatic plagiarism detection system for specialized corpora TraianRebedea <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/automaticplagiarismdetectionsystemforspecializedcorpora-130922165259-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" />

Automatic plagiarism detection system for specialized corpora from Traian Rebedea

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0 Relevance based ranking of video comments on YouTube /slideshow/relevance-based-ranking-of-video-comments-on-youtube/26436757 relevance-basedrankingofvideocommentsonyoutube-130922165201-phpapp02
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]]> Sun, 22 Sep 2013 16:52:01 GMT /slideshow/relevance-based-ranking-of-video-comments-on-youtube/26436757 TraianRebedea@slideshare.net(TraianRebedea) Relevance based ranking of video comments on YouTube TraianRebedea <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/relevance-basedrankingofvideocommentsonyoutube-130922165201-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" />

Relevance based ranking of video comments on YouTube from Traian Rebedea

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0 Opinion mining for social media and news items in Romanian /slideshow/opinion-mining-for-social-media-and-news-items-in-romanian/26436746 opinionminingforsocialmediaandnewsitemsinromanian-130922165112-phpapp01
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]]> Sun, 22 Sep 2013 16:51:12 GMT /slideshow/opinion-mining-for-social-media-and-news-items-in-romanian/26436746 TraianRebedea@slideshare.net(TraianRebedea) Opinion mining for social media and news items in Romanian TraianRebedea <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/opinionminingforsocialmediaandnewsitemsinromanian-130922165112-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" />

Opinion mining for social media and news items in Romanian from Traian Rebedea

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0 PhD Defense: Computer-Based Support and Feedback for Collaborative Chat Conversations and Discussion Forums /slideshow/phd-defense-computerbased-support-and-feedback-for-collaborative-chat-conversations-and-discussion-forums/16566428 phdthesistraianrebedea-130216060127-phpapp02
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]]> Sat, 16 Feb 2013 06:01:27 GMT /slideshow/phd-defense-computerbased-support-and-feedback-for-collaborative-chat-conversations-and-discussion-forums/16566428 TraianRebedea@slideshare.net(TraianRebedea) PhD Defense: Computer-Based Support and Feedback for Collaborative Chat Conversations and Discussion Forums TraianRebedea <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/phdthesistraianrebedea-130216060127-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" />

PhD Defense: Computer-Based Support and Feedback for Collaborative Chat Conversations and Discussion Forums from Traian Rebedea

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0 Importanța algoritmilor pentru problemele de la interviuri /slideshow/importana-algoritmilor-pentru-problemele-de-la-interviuri/13605038 importanaalgoritmilorpentruproblemeledelainterviuri-120711073439-phpapp01
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]]> Wed, 11 Jul 2012 07:34:37 GMT /slideshow/importana-algoritmilor-pentru-problemele-de-la-interviuri/13605038 TraianRebedea@slideshare.net(TraianRebedea) Importanța algoritmilor pentru problemele de la interviuri TraianRebedea <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/importanaalgoritmilorpentruproblemeledelainterviuri-120711073439-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" />

Importanネ嫗 algoritmilor pentru problemele de la interviuri from Traian Rebedea

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0 Web services for supporting the interactions of learners in the social web - Roedunet 2010 /TraianRebedea/web-services-for-supporting-the-interactions-of-learners-in-the-social-web-roedunet-2010 webservicesforsupportingtheinteractionsoflearnersinthesocialweb-roedunet2010-111112081156-phpapp02
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]]> Sat, 12 Nov 2011 08:11:53 GMT /TraianRebedea/web-services-for-supporting-the-interactions-of-learners-in-the-social-web-roedunet-2010 TraianRebedea@slideshare.net(TraianRebedea) Web services for supporting the interactions of learners in the social web - Roedunet 2010 TraianRebedea <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/webservicesforsupportingtheinteractionsoflearnersinthesocialweb-roedunet2010-111112081156-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" />

Web services for supporting the interactions of learners in the social web - Roedunet 2010 from Traian Rebedea

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0 Automatic assessment of collaborative chat conversations with PolyCAFe - EC-TEL2011 /slideshow/automatic-assessment-of-collaborative-chat-conversations-with-polycafe-ectel2011/10130358 automaticassessmentofcollaborativechatconversationswithpolycafe-ectel2011-111112061255-phpapp02
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]]> Sat, 12 Nov 2011 06:12:53 GMT /slideshow/automatic-assessment-of-collaborative-chat-conversations-with-polycafe-ectel2011/10130358 TraianRebedea@slideshare.net(TraianRebedea) Automatic assessment of collaborative chat conversations with PolyCAFe - EC-TEL2011 TraianRebedea <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/automaticassessmentofcollaborativechatconversationswithpolycafe-ectel2011-111112061255-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" />

Automatic assessment of collaborative chat conversations with PolyCAFe - EC-TEL2011 from Traian Rebedea

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0 Conclusions and Recommendations of the Romanian ICT RTD Survey /slideshow/conclusions-and-recommendations-of-the-romanian-ict-rtd-survey/10120766 conclusionsandrecommendationsoftheromanianictrtd-111111094803-phpapp02
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]]> Fri, 11 Nov 2011 09:48:00 GMT /slideshow/conclusions-and-recommendations-of-the-romanian-ict-rtd-survey/10120766 TraianRebedea@slideshare.net(TraianRebedea) Conclusions and Recommendations of the Romanian ICT RTD Survey TraianRebedea <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/conclusionsandrecommendationsoftheromanianictrtd-111111094803-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" />

Conclusions and Recommendations of the Romanian ICT RTD Survey from Traian Rebedea

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0 Istoria Web-ului - part 2 - tentativ How to Web 2009 /slideshow/istoria-webului-part-2-tentativ-how-to-web-2009/7121415 howtowebpart2-110302094642-phpapp02
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]]> Wed, 02 Mar 2011 09:46:39 GMT /slideshow/istoria-webului-part-2-tentativ-how-to-web-2009/7121415 TraianRebedea@slideshare.net(TraianRebedea) Istoria Web-ului - part 2 - tentativ How to Web 2009 TraianRebedea <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/howtowebpart2-110302094642-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" />

Istoria Web-ului - part 2 - tentativ How to Web 2009 from Traian Rebedea

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]]> Wed, 02 Mar 2011 09:46:38 GMT /slideshow/how-toweb-pat1/7121414 TraianRebedea@slideshare.net(TraianRebedea) Istoria Web-ului - part 1 (2) - tentativ How to Web 2009 TraianRebedea <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/howtowebpat1-110302094644-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" />

Istoria Web-ului - part 1 (2) - tentativ How to Web 2009 from Traian Rebedea

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0 https://cdn.slidesharecdn.com/profile-photo-TraianRebedea-48x48.jpg?cb=1634722735 Interested in machine learning, text mining, information extraction and machine learning. Teaching all of the above, plus algorithm design and analysis at University Politehnica of Bucharest and collaborating with various companies in Bucharest on these topics. ro.linkedin.com/in/trebedea https://cdn.slidesharecdn.com/ss_thumbnails/anevolutionofdeeplearningmodelsforarc-200310191022-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/an-evolution-of-deep-learning-models-for-ai2-reasoning-challenge/230021238 An Evolution of Deep L... https://cdn.slidesharecdn.com/ss_thumbnails/bucharest-ai-aiwholi-171208225434-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/ai-wholi-bucharestai-meetup-5/83679701 AI @ Wholi - Bucharest... https://cdn.slidesharecdn.com/ss_thumbnails/iccci2017ciorbarurebedea-171205222733-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/deep-neural-networks-for-matching-online-social-networking-profiles/83434291 Deep neural networks f...