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Deep Learning for the Web

Kyomin Jung, Byoung-Tak Zhang, Prasenjit Mitra
2015 Proceedings of the 24th International Conference on World Wide Web - WWW '15 Companion  
Deep learning has a huge potential to improve the intelligence of the web and the web service systems by efficiently and effectively mining big data on the Web[4][5].  ...  Recent success of deep learning has shown that it outperforms state-of-the-art systems in image processing, voice recognition, web search, recommendation systems, etc [1].  ...  We give the motivation and underlying ideas of deep learning and describe the architectures and learning algorithms for various deep learning models.  ... 
doi:10.1145/2740908.2741982 dblp:conf/www/JungZM15 fatcat:5tkrrrwvcjbcxhcoyck4sqmoxy

Leveraging Crowdsourcing Data for Deep Active Learning An Application

Jie Yang, Thomas Drake, Andreas Damianou, Yoelle Maarek
2018 Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18  
for optimally training the deep learning model.  ...  It provides a unified Bayesian model to simultaneously infer the true labels and train the deep learning model in order to reach an optimal learning efficacy.  ...  [12] for Databases, CrowdSearcher by Bozzon et al. [2] for Information Retrieval, and Zencrowd by Demartini et al. [8] for Semantic Web.  ... 
doi:10.1145/3178876.3186033 dblp:conf/www/YangDDM18 fatcat:6ngqcgzjnrc6tg47tmhbfcmwri

DDGK: Learning Graph Representations for Deep Divergence Graph Kernels

Rami Al-Rfou, Bryan Perozzi, Dustin Zelle
2019 The World Wide Web Conference on - WWW '19  
Our experimental results show that Deep Divergence Graph Kernels can learn an unsupervised alignment between graphs, and that the learned representations achieve competitive results when used as features  ...  We propose Deep Divergence Graph Kernels, an unsupervised method for learning representations over graphs that encodes a relaxed notion of graph isomorphism. Our method consists of three parts.  ...  over directed web graphs.  ... 
doi:10.1145/3308558.3313668 dblp:conf/www/Al-RfouPZ19 fatcat:mhhomms6uffrvlvbfb5u62eire

Deep Learning for Hate Speech Detection in Tweets

Pinkesh Badjatiya, Shashank Gupta, Manish Gupta, Vasudeva Varma
2017 Proceedings of the 26th International Conference on World Wide Web Companion - WWW '17 Companion  
Our experiments on a benchmark dataset of 16K annotated tweets show that such deep learning methods outperform state-of-the-art char/word n-gram methods by ~18 F1 points.  ...  We perform extensive experiments with multiple deep learning architectures to learn semantic word embeddings to handle this complexity.  ...  To the best of our knowledge, we are the first to experiment with deep learning architectures for the hate speech detection task.  ... 
doi:10.1145/3041021.3054223 dblp:conf/www/BadjatiyaG0V17 fatcat:va2jfr6zyzhxxahhjzamml3rge

bot.zen $@$ EmpiriST 2015 - A minimally-deep learning PoS-tagger (trained for German CMC and Web data)

Egon Stemle
2016 Proceedings of the 10th Web as Corpus Workshop  
Labelled data (Tiger v2.2 and EmpiriST) and unlabelled data (German Wikipedia) were used for training. The system is available under the APLv2 open-source license.  ...  The system combines a small assertion of trending techniques, which implement matured methods, from NLP and ML to achieve competitive results on PoS tagging of German CMC and Web corpus data; in particular  ...  We participated in the PoS tagging subtask of the ST with our new minimally-deep learning PoS-tagger: We combine word2vec (w2v) word embeddings (WEs) with a single-layer Long Short Term Memory (LSTM) recurrent  ... 
doi:10.18653/v1/w16-2614 dblp:conf/aclwac/Stemle16 fatcat:ccahnaz6dnc7xc3n3h5o35escy

DLWoT'22: 2nd International Workshop on Deep Learning for the Web of Things

Wenzhong Guo, Chin-Chen Chang, Eyhab Al-Masri, Chi-Hua Chen, Haishuai Wang, Qichun Zhang, K. Shankar
2022 Companion Proceedings of the Web Conference 2022  
Therefore, the title of this workshop is "the 2nd International Workshop on Deep Learning for the Web of Things (DLWoT'22)" for the Web Conference 2022 (WWW'22).  ...  In recent years, deep learning and web of things (WoT) have become hot topics. The relevant research issues in deep learning have been in increasingly investigated and published.  ...  ACKNOWLEDGMENTS We thank authors who submitted their valuable papers to the workshop, entitled "DLWoT'22: 2nd International Workshop on Deep Learning for the Web of Things", of the Web Conference 2022  ... 
doi:10.1145/3487553.3524878 fatcat:cas4muis2zc7xgr6q2pbsvoopu

On the conditions for integrating deep learning into the study of visual politics

Matteo Magnani, Alexandra Segerberg
2021 13th ACM Web Science Conference 2021  
We examine the conditions of integrating a deep learning tool for image classification into the large-scale study of visual politics in digital and social media along these two dimensions.  ...  On the other hand, it is important to acknowledge that a deep learning tool will never simply replace specific tasks inside a research process: its adoption has implications for the broader process from  ...  ACKNOWLEDGMENTS This research has been funded by the AI4Research initiative at Uppsala University and the Swedish Research Council (2015-01835).  ... 
doi:10.1145/3447535.3462511 fatcat:paay5vpv35ggnoo347njhw3ciu

DeepScreening: a deep learning-based screening web server for accelerating drug discovery

2019 Database: The Journal of Biological Databases and Curation  
We believe this deep learning-based web server will benefit to both biologists and chemists for probes or drugs discovery.  ...  Therefore, we developed a user-friendly web server with integration of the state of art deep learning algorithm, which utilizes either the public or user-provided dataset to help biologists or chemists  ...  Therefore, we developed a user-friendly web server with integration of the state of art deep learning algorithm, which utilizes either the public or user-provided dataset to help biologists or chemists  ... 
doi:10.1093/database/baz104 pmid:31608949 pmcid:PMC6790966 fatcat:37lcdva7dngbfexyvqcnfllvre

ActiveLink: Deep Active Learning for Link Prediction in Knowledge Graphs

Natalia Ostapuk, Jie Yang, Philippe Cudre-Mauroux
2019 The World Wide Web Conference on - WWW '19  
Inspired by recent advances in Bayesian deep learning, ActiveLink takes a Bayesian view on neural link predictors, thereby enabling uncertainty sampling for deep active learning.  ...  In this work, we demonstrate that we can get the best of both worlds while drastically reducing the amount of data needed to train a deep network by leveraging active learning.  ...  ACKNOWLEDGMENTS This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement 683253/GraphInt).  ... 
doi:10.1145/3308558.3313620 dblp:conf/www/OstapukYC19 fatcat:2kx3zkvntbfelf4gr5cg6whdpy

Identification of Query Forms for Retrieving the Information From Deep Web

Nripendra Narayan Das, Ela Kumar
2014 Transactions on Machine Learning and Artificial Intelligence  
Web databases are now present everywhere. The data from the Deep Web cannot be accessed by Search engine and web crawlers directly.  ...  Retrieving information from deep web pages using wrappers is a fundamental problem arising in a huge range of web pages of vast practical interests.  ...  Query Forms for Retrieving the Information from Deep Web.  ... 
doi:10.14738/tmlai.26.778 fatcat:zmzasv6btvfi7ldhiqdnopva2u

A Fast Deep Learning Model for Textual Relevance in Biomedical Information Retrieval

Sunil Mohan, Nicolas Fiorini, Sun Kim, Zhiyong Lu
2018 Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18  
This results in a fast model suitable for use in an online search engine. The model is robust and outperforms comparable state-of-the-art deep learning approaches.  ...  Towards addressing the problem of relevance in biomedical literature search, we introduce a deep learning model for the relevance of a document's text to a keyword style query.  ...  ACKNOWLEDGMENTS This research was supported by the Intramural Research Program of the NIH, National Library of Medicine.  ... 
doi:10.1145/3178876.3186049 dblp:conf/www/MohanFKL18 fatcat:hceiu7l4lngizbbjeuex5q2yya

Deep Learning from Web-Scale Corpora for Better Dictionary Interfaces

Pavel Smrz, Lubomir Otrusina
2014 Proceedings of the 4th Workshop on Cognitive Aspects of the Lexicon (CogALex)  
This paper explores advanced learning mechanisms -neural networks trained by the Word2Vec method -for predicting word associations.  ...  The paper concludes with a proposal for a new collective effort to assemble real tip-of-the-tongue situation records for future, more-realistic evaluations.  ...  CZ.1.05/1.1.00/02.0070, supported by Operational Programme "Research and Development for Innovations" funded by Structural Funds of the European Union and the state budget of the Czech Republic.  ... 
doi:10.3115/v1/w14-4703 dblp:conf/cogalex/SmrzO14 fatcat:bsgh5vky5re4fg744zz4wu5lky

Classifying News Media Coverage for Corruption Risks Management with Deep Learning and Web Intelligence

Albert Weichselbraun, Sandro Hörler, Christian Hauser, Anina Havelka
2020 Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics  
The research presented in this paper introduces the Integrity Risks Monitor, an analytics dashboard that applies Web Intelligence and Deep Learning to english and germanspeaking documents for the task  ...  The experiments performed to evaluate the classifiers draw upon popular algorithms used for text classification such as Naïve Bayes, Support Vector Machines (SVM) and Deep Learning architectures (LSTM,  ...  Acknowledgement The research presented in this paper has been conducted within the Integrity Risk Monitor (IRM) project funded by the KBA Notasys Integrity funds.  ... 
doi:10.1145/3405962.3405988 fatcat:nzutrdthpjam7isklcxt7di5fi

A tool for Emergency Detection with Deep Learning Neural Networks

Emanuele Cipolla, Riccardo Rizzo, Dario Stabile, Filippo Vella
2016 International Workshop on Knowledge Discovery on the Web  
The ubiquitous presence of sensor networks, control units and detection devices allows for a significant availability of data.  ...  The results are encouraging and show how machine learning can help in predicting emergency situations and to reduce the impact of critical situations.  ...  used for deep learning: TensorFlow and Theano.  ... 
dblp:conf/kdweb/CipollaRSV16 fatcat:ogmqvfrqbvgxpjuu54iaf4npoa

Deep learning for plant identification: how the web can compete with human experts

Hervé Goëau, Alexis Joly, Pierre Bonnet, Mario Lasseck, Milan Šulc, Siang Thye Hang
2018 Biodiversity Information Science and Standards  
The main outcome of this work is that the performance of state-of-the-art deep learning models is now close to the most advanced human expertise.  ...  Automated identification of plants and animals has improved considerably in the last few years, in particular thanks to the recent advances in deep learning.  ...  The main outcome of this work is that the performance of state-of-the-art deep learning models is now close to the most advanced human expertise.  ... 
doi:10.3897/biss.2.25637 fatcat:rxznrrnub5bbxm2sf6zzkhz2xa
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