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Deep Learning for Information Retrieval

Hang Li, Zhengdong Lu
2016 Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval - SIGIR '16  
This tutorial aims at summarizing and introducing the results of recent research on deep learning for information retrieval, in order to stimulate and foster more significant research and development work  ...  In the first part, we introduce the fundamental techniques of deep learning for natural language processing and information retrieval, such as word embedding, recurrent neural networks, and convolutional  ...  Future Directions of Deep Learning for IR We conclude the tutorial by summarizing the major challenges and opportunities in deep learning for information retrieval.  ... 
doi:10.1145/2911451.2914800 dblp:conf/sigir/LiL16 fatcat:fosiii4wt5fv5lyv52ntc3afna

A Tutorial on Deep Learning for Music Information Retrieval [article]

Keunwoo Choi, György Fazekas, Kyunghyun Cho, Mark Sandler
2018 arXiv   pre-print
Following their success in Computer Vision and other areas, deep learning techniques have recently become widely adopted in Music Information Retrieval (MIR) research.  ...  Finally, guidelines for new tasks and some advanced topics in deep learning are discussed to stimulate new research in this fascinating field.  ...  We appreciate Adib Mehrabi, Beici Liang, Delia Fanoyela, Blair Kaneshiro, and Sertan Şentürk for their helpful comments on writing this paper.  ... 
arXiv:1709.04396v2 fatcat:ohozqahyn5fudhj76h5aehsclm

Deep learning vector quantization for acoustic information retrieval

Zhen Huang, Chao Weng, Kehuang Li, You-Chi Cheng, Chin-Hui Lee
2014 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)  
We propose a novel deep learning vector quantization (DLVQ) algorithm based on deep neural networks (DNNs).  ...  Tested on an audio information retrieval task, the proposed DLVQ achieves a quite promising performance when it is initialized by the k-means VQ technique.  ...  We refer to this deep structured LVQ as deep learning vector quantization (DLVQ). The proposed DLVQ method is tested on an audio information retrieval task.  ... 
doi:10.1109/icassp.2014.6853817 dblp:conf/icassp/HuangWLCL14 fatcat:6ojc5ripwrfbxpc2wz4gphcfrm

Explainable information retrieval using deep learning for medical images

Apoorva Singh, Husanbir Pannu, Avleen Malhi
2021 Computer Science and Information Systems  
So, we have proposed an efficient deep learning model for image classification and the proof-of-concept has been the case studied on gastrointestinal images for bleeding detection.  ...  Image segmentation is useful to extract valuable information for an efficient analysis on the region of interest.  ...  There are a variety to deep learning models available for CNN architecture. Similar to the CNN model proposed by Jia et al.  ... 
doi:10.2298/csis201030049s fatcat:o7zbak3svbcczlxh2vxsfp3kvy

Learning Deep Features For MSR-bing Information Retrieval Challenge

Qiang Song, Sixie Yu, Cong Leng, JiaXiang Wu, Qinghao Hu, Jian Cheng
2015 Proceedings of the 23rd ACM international conference on Multimedia - MM '15  
In this paper, we propose a CNN-based feature representation for visual recognition only using image-level information.  ...  To address the information retrieval task, we raise and integrate a series of methods with visual features obtained by convolution neural network (CNN) models.  ...  We discover that the Hierarchical clustering and PageRank methods are mutually complementary for information retrieval.  ... 
doi:10.1145/2733373.2809928 dblp:conf/mm/SongYLWHC15 fatcat:xiqww2ztrfherjplvyirykzyzq

DRL4IR: 3rd Workshop on Deep Reinforcement Learning for Information Retrieval

Xiangyu Zhao, Xin Xin, Weinan Zhang, Li Zhao, Dawei Yin, Grace Hui Yang
2022 Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval  
In the last ten years, deep reinforcement learning (DRL) has become a promising direction for decision-making, since DRL utilizes the high model capacity of deep learning for complex decision-making tasks  ...  Information retrieval (IR) systems have become an essential component in modern society to help users find useful information, which consists of a series of processes including query expansion, item recall  ...  Xiangyu Zhao is partially supported by Start-up Grant (No.9610565) for the New Faculty of the City University of Hong Kong and the CCF-Tencent Open Fund.  ... 
doi:10.1145/3477495.3531703 fatcat:5gmafvsikrb7njqhke4x2kmfou

Deep Learning for Biomedical Information Retrieval: Learning Textual Relevance from Click Logs

Sunil Mohan, Nicolas Fiorini, Sun Kim, Zhiyong Lu
2017 BioNLP 2017  
We describe a Deep Learning approach to modeling the relevance of a document's text to a query, applied to biomedical literature.  ...  followed by a deep regression network to produce the estimated probability of the document's relevance to the query.  ...  Concluding Remarks We have demonstrated a Deep Learning approach for learning textual relevance from a fairly small labelled training dataset.  ... 
doi:10.18653/v1/w17-2328 dblp:conf/bionlp/MohanFKL17 fatcat:gewwdfk3xrbrpdtez7hoqabkxe

An Evaluation of Two Commercial Deep Learning-Based Information Retrieval Systems for COVID-19 Literature [article]

Sarvesh Soni, Kirk Roberts
2020 arXiv   pre-print
This has implications for developing biomedical retrieval systems for future health crises as well as trust in popular health search engines.  ...  While most research in search engines is performed in the academic field of information retrieval (IR), most academic search enginesx2013though rigorously evaluatedx2013are sparsely utilized, while major  ...  Acknowledgments The authors thank Meghana Gudala and Jordan Godfrey-Stovall for conducting the additional retrieval assessments.  ... 
arXiv:2007.03106v2 fatcat:gwbqgaqipbhh3dv6fwb77xjyta

Design ensemble deep learning model for pneumonia disease classification

Khalid El Asnaoui
2021 International Journal of Multimedia Information Retrieval  
In this work, we aim to evaluate the performance of single and ensemble learning models for the pneumonia disease classification.  ...  As a result, for a single model, we found out that InceptionResNet_V2 gives 93.52% of F1 score.  ...  Acknowledgements We thank the reviewer for his/her thorough review and highly appreciate the comments, corrections, and suggestions that ensued, which significantly contributed to improving the quality  ... 
doi:10.1007/s13735-021-00204-7 pmid:33643764 pmcid:PMC7896551 fatcat:pzyozwqndve4jh7dbfr6jsdoay

A Deep Learning Approach to Persian Plagiarism Detection

Erfaneh Gharavi, Kayvan Bijari, Kiarash Zahirnia, Hadi Veisi
2016 Forum for Information Retrieval Evaluation  
Due to drawbacks and inefficiency of traditional methods and lack of proper algorithms for Persian plagiarism detection, in this paper, we propose a deep learning based method to detect plagiarism.  ...  CCS Concepts • Information systems → Near-duplicate and plagiarism detection • Information systems → Evaluation of retrieval results.  ...  DEEP LEARNING FOR FEATURE EXTRACTION Deep learning is a branch of machine learning which tries to find more abstract features using deep multiple layer graph.  ... 
dblp:conf/fire/GharaviBZV16 fatcat:qq6uybdyjralja2tg4ddfqvpva

Review of Recent Deep Learning Based Methods for Image-Text Retrieval

Jianan Chen, Lu Zhang, Cong Bai, Kidiyo Kpalma
2020 2020 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)  
In this paper, we highlight key points of recent cross-modal retrieval approaches based on deep-learning, especially in the image-text retrieval context, and classify them into four categories according  ...  Cross-modal retrieval aims to retrieve relevant information across different modalities.  ...  In this paper, we focus on cross-modal retrieval methods based on deep-learning, only for image-text context, and proposed in the last two years as some new methods based on deep learning have been proposed  ... 
doi:10.1109/mipr49039.2020.00042 dblp:conf/mipr/ChenZBK20 fatcat:fps5wiw4ezf7teko3vegaxq4tq

Deep Learning Model for Enhanced Crop Identification from Landsat 8 Images

2022 International Journal of Information Retrieval Research  
Deep learning is a powerful state-of-the-art technique for image processing, including remote sensing images.  ...  This paper describes a multilevel deep learning based crop type identification system that targets land cover and crop type classification from multi-temporal multisource satellite imagery.  ...  machine/deep learning techniques.  ... 
doi:10.4018/ijirr.298648 fatcat:ano5ersmqvb5peavv2guxgwfsq

A Deep Learning Approach towards Cross-Lingual Tweet Tagging

Nikhil Bharadwaj Gosala, Shalini Chaudhuri, Monica Adusumilli, Kartik Sethi
2016 Forum for Information Retrieval Evaluation  
Although Twitter Data is noisy, it is valuable due to the amount of information it can provide. Therefore, NER for Twitter Data is necessary.  ...  Long Short Term Memory (LSTM) was used to learn long term dependencies in our supervised learning model.  ...  RNN Core Upon studying various models for NER tagging, Deep Learning and especially Recurrent Neural Networks (RNNs) was chosen for the task of Tweet Tagging.  ... 
dblp:conf/fire/GosalaCAS16 fatcat:72bnizqoafcjjbfeu5isut3ciu

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.  ...  We described the Delta Relevance model, a new deep learning model for text relevance, targeted for information retrieval in biomedical science literature.  ... 
doi:10.1145/3178876.3186049 dblp:conf/www/MohanFKL18 fatcat:hceiu7l4lngizbbjeuex5q2yya

Multimodal Deep Learning for Music Genre Classification

Sergio Oramas, Francesco Barbieri, Oriol Nieto, Xavier Serra
2018 Transactions of the International Society for Music Information Retrieval  
Intermediate representations of deep neural networks are learned from audio tracks, text reviews, and cover art images, and further combined for classification.  ...  information.  ...  However, to the best of our knowledge, no multimodal approach based on deep learning architectures has been proposed for this Music Information Retrieval (MIR) task, neither for singlelabel nor multi-label  ... 
doi:10.5334/tismir.10 fatcat:xfkr3e3atne3hbiwoyaxqv35za
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