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Optimizing Visual Representations in Semantic Multi-modal Models with Dimensionality Reduction, Denoising and Contextual Information [chapter]

Maximilian Köper, Kim-Anh Nguyen, Sabine Schulte im Walde
<span title="">2018</span> <i title="Springer International Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
This paper improves visual representations for multi-modal semantic models, by (i) applying standard dimensionality reduction and denoising techniques, and by (ii) proposing a novel technique ContextVision  ...  We explore our contribution in a visual and a multi-modal setup and evaluate on benchmark word similarity and relatedness tasks.  ...  We would like to thank the four anonymous reviewers for their comments and suggestions.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-73706-5_25">doi:10.1007/978-3-319-73706-5_25</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xgw6j6yalrg3rohauwxeeb7bxq">fatcat:xgw6j6yalrg3rohauwxeeb7bxq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190218050050/http://pdfs.semanticscholar.org/095c/1e53e59caf86efc55eda31b4c516ed9b9b6f.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/09/5c/095c1e53e59caf86efc55eda31b4c516ed9b9b6f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-73706-5_25"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Contents

<span title="">2015</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/cx3f4s3qmfe6bg4qvuy2cxezyu" style="color: black;">Procedia Computer Science</a> </i> &nbsp;
Partitioning: An Extended Experiments and Analysis in Low Dimensional Scenario V.  ...  Health Information Systems Q.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/s1877-0509(15)02213-9">doi:10.1016/s1877-0509(15)02213-9</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ltb3x34y2jhnhlhgssoi67sdk4">fatcat:ltb3x34y2jhnhlhgssoi67sdk4</a> </span>
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2020 Index IEEE Transactions on Image Processing Vol. 29

<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/dhlhr4jqkbcmdbua2ca45o7kru" style="color: black;">IEEE Transactions on Image Processing</a> </i> &nbsp;
., +, TIP 2020 265-276 Multi-View Image Classification With Visual, Semantic and View Consistency.  ...  Li, J., +, TIP 2020 5817-5831 A Multi-Domain and Multi-Modal Representation Disentangler for Cross-Domain Image Manipulation and Classification.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tip.2020.3046056">doi:10.1109/tip.2020.3046056</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/24m6k2elprf2nfmucbjzhvzk3m">fatcat:24m6k2elprf2nfmucbjzhvzk3m</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201224144031/https://ieeexplore.ieee.org/ielx7/83/8835130/09301460.pdf?tp=&amp;arnumber=9301460&amp;isnumber=8835130&amp;ref=" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/56/93/5693eebc307c33915511489f6dcddcb127981534.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tip.2020.3046056"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Transformers in Vision: A Survey [article]

Salman Khan, Muzammal Naseer, Munawar Hayat, Syed Waqas Zamir, Fahad Shahbaz Khan, Mubarak Shah
<span title="2021-10-03">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
, multi-modal tasks (e.g., visual-question answering, visual reasoning, and visual grounding), video processing (e.g., activity recognition, video forecasting), low-level vision (e.g., image super-resolution  ...  This survey aims to provide a comprehensive overview of the Transformer models in the computer vision discipline.  ...  We would also like to thank Mohamed Afham for his help with a figure.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2101.01169v4">arXiv:2101.01169v4</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ynsnfuuaize37jlvhsdki54cy4">fatcat:ynsnfuuaize37jlvhsdki54cy4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211006053637/https://arxiv.org/pdf/2101.01169v4.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/42/2c/422ce70e13b65b91b680ed89828a3822641bd32c.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2101.01169v4" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Sentiment analysis using deep learning approaches: an overview

Olivier Habimana, Yuhua Li, Ruixuan Li, Xiwu Gu, Ge Yu
<span title="2019-12-26">2019</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ikvx2lmj7rew7jpw4lygqgjpby" style="color: black;">Science China Information Sciences</a> </i> &nbsp;
Nowadays, with the increasing number of Web 2.0 tools, users generate huge amounts of data in an enormous and dynamic way.  ...  Suggestions include the use of bidirectional encoder representations from transformers (BERT), sentiment-specific word embedding models, cognition-based attention models, common sense knowledge, reinforcement  ...  The Bi-GRNN model deals with noisy text and learns the semantic and syntactic information from input tweets. In addition, it models the contextual information where a word appears.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s11432-018-9941-6">doi:10.1007/s11432-018-9941-6</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/nbevrfiyybhszirol2af26c6ve">fatcat:nbevrfiyybhszirol2af26c6ve</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210731104047/https://www.sciengine.com/doi/pdf/87C836B7D3C344A69C3E3AD011E0A572" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/79/90/79901b358e5a6faae592953cf6ad2199e5c813ec.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s11432-018-9941-6"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Deep Learning: Methods and Applications

Li Deng
<span title="">2014</span> <i title="Now Publishers"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/qqxcxey64vdbrpzf3zwaaw652a" style="color: black;">Foundations and Trends® in Signal Processing</a> </i> &nbsp;
In other words, multi-modality learning can use text information to help image/visual recognition, and vice versa.  ...  An illustration of the multi-modal language model is shown in Figure 11 .2. Multi-modalities: Speech and image Ngiam et al.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1561/2000000039">doi:10.1561/2000000039</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vucffxhse5gfhgvt5zphgshjy4">fatcat:vucffxhse5gfhgvt5zphgshjy4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20150425062228/http://research.microsoft.com:80/pubs/219984/DeepLearningBook_RefsByLastFirstNames.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/fc/d3/fcd37fe5de53436e7b8e5705b21e8d6bda81473b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1561/2000000039"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances [article]

Yan Wang, Wei Song, Wei Tao, Antonio Liotta, Dawei Yang, Xinlei Li, Shuyong Gao, Yixuan Sun, Weifeng Ge, Wei Zhang, Wenqiang Zhang
<span title="2022-03-20">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Affective computing is realized based on unimodal or multimodal data, primarily consisting of physical information (e.g., textual, audio, and visual data) and physiological signals (e.g., EEG and ECG signals  ...  baseline dataset, fusion strategies for multimodal affective analysis, and unsupervised learning models.  ...  In the multi-physical modalities [406] , there are three kinds of combinations of different modalities, consisting of visual-audio, text-audio and visual-audio-text.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2203.06935v3">arXiv:2203.06935v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/h4t3omkzjvcejn2kpvxns7n2qe">fatcat:h4t3omkzjvcejn2kpvxns7n2qe</a> </span>
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2020 Index IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 42

<span title="">2021</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/3px634ph3vhrtmtuip6xznraqi" style="color: black;">IEEE Transactions on Pattern Analysis and Machine Intelligence</a> </i> &nbsp;
., and Nishino, K., Recognizing Material Properties from Images; 1981-1995 Sebe, N., see Pilzer, A., 2380-2395 Seddik, M., see Tamaazousti, Y., 2212-2224 Shah, M., see Kalayeh, M.M., TPAMI June 2020  ...  ., +, TPAMI Jan. 2020 192-202 Dimensionality reduction Learning Low-Dimensional Temporal Representations with Latent Align- ments.  ...  Iglesias, F., +, TPAMI Sept. 2020 2096-2112 Data models Learning Low-Dimensional Temporal Representations with Latent Align- ments.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tpami.2020.3036557">doi:10.1109/tpami.2020.3036557</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3j6s2l53x5eqxnlsptsgbjeebe">fatcat:3j6s2l53x5eqxnlsptsgbjeebe</a> </span>
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Mining multi-tag association for image tagging

Yang Yang, Zi Huang, Heng Tao Shen, Xiaofang Zhou
<span title="2010-12-22">2010</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/tdniohqnfvcqrpinoqffpwlpgq" style="color: black;">World wide web (Bussum)</a> </i> &nbsp;
In this paper, we propose a novel automatic image tagging method aiming to automatically discover more complete tags associated with information importance for test images.  ...  To further reduce noisy tags, a visual relevance score is also computed for each candidate tag to the test image based on a new tag model.  ...  The label reconstruction coefficients were further used to perform dimensionality reduction over the feature representation derived from Gaussian Mixture Model.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s11280-010-0099-8">doi:10.1007/s11280-010-0099-8</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/bi36x7ot6jfw3e7fx7u3stbp6e">fatcat:bi36x7ot6jfw3e7fx7u3stbp6e</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20130428135907/http://itee.uq.edu.au/~yangyang/papers/wwwj.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ca/25/ca25f23e0a5270d56b7057a657509597e1f6dbe0.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s11280-010-0099-8"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Domain Adaptation for Visual Applications: A Comprehensive Survey [article]

Gabriela Csurka
<span title="2017-03-30">2017</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The aim of this paper is to give an overview of domain adaptation and transfer learning with a specific view on visual applications.  ...  After a general motivation, we first position domain adaptation in the larger transfer learning problem.  ...  These tasks include clustering, dimensionality reduction and density estimation [38, 39] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1702.05374v2">arXiv:1702.05374v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5va4oz4evjfhxgxddflpbb6pxi">fatcat:5va4oz4evjfhxgxddflpbb6pxi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200907090703/https://arxiv.org/pdf/1702.05374v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/33/0d/330dda431e0343a96f9d630a0b4ee526bd93ad11.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1702.05374v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Beyond Just Vision: A Review on Self-Supervised Representation Learning on Multimodal and Temporal Data [article]

Shohreh Deldari, Hao Xue, Aaqib Saeed, Jiayuan He, Daniel V. Smith, Flora D. Salim
<span title="2022-06-08">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Recently, Self-Supervised Representation Learning (SSRL) has attracted much attention in the field of computer vision, speech, natural language processing (NLP), and recently, with other types of modalities  ...  of their objective function, network architecture and potential applications, and 4) review existing multimodal techniques in each category and various modalities.  ...  Acknowledgments Authors would like to acknowledge the support from CSIRO Data61 Scholarship program (Grant number 500588), RMIT Research International Tuition Fee Scholarship and Australian Research Council  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2206.02353v2">arXiv:2206.02353v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ljkxvfxsand43otrpq4effs7cq">fatcat:ljkxvfxsand43otrpq4effs7cq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220701200728/https://arxiv.org/pdf/2206.02353v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/55/e3/55e31baa3ae5f32fb5e695761892319e26dbc639.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2206.02353v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

2020 Index IEEE Transactions on Circuits and Systems for Video Technology Vol. 30

<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/jqw2pm7kwvhchpdxpcm5ryoic4" style="color: black;">IEEE transactions on circuits and systems for video technology (Print)</a> </i> &nbsp;
., TCSVT Jan. 2020 217-231 Hu, X., see Zhu, L., TCSVT Oct. 2020 3358-3371 Hu, Y., Lu, M., Xie, C., and Lu, X  ...  ., and Zeng, B., MUcast: Linear Uncoded Multiuser TCSVT Nov. 2020 4299-4308 Hu, R., see Chen, L., TCSVT Dec. 2020 4513-4525 Hu, R., see Wang, X., TCSVT Nov. 2020 4309-4320 Hu, X., see Zhang, X  ...  ., +, TCSVT Feb. 2020 457-467 Multimodal Transformer With Multi-View Visual Representation for Image Captioning.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tcsvt.2020.3043861">doi:10.1109/tcsvt.2020.3043861</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/s6z4wzp45vfflphgfcxh6x7npu">fatcat:s6z4wzp45vfflphgfcxh6x7npu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201229235746/https://ieeexplore.ieee.org/ielx7/76/9280452/09309118.pdf?tp=&amp;arnumber=9309118&amp;isnumber=9280452&amp;ref=" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/8c/47/8c47707a649339c85261ed9ea9455eb629c7f804.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tcsvt.2020.3043861"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

DeepHealth: Review and challenges of artificial intelligence in health informatics [article]

Gloria Hyunjung Kwak, Pan Hui
<span title="2020-08-08">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We highlight ongoing popular approaches' research and identify several challenges in building models.  ...  Despite its notable advantages, there are some key challenges on data (high dimensionality, heterogeneity, time dependency, sparsity, irregularity, lack of label, bias) and model (reliability, interpretability  ...  Data Informativeness (high dimensionality, heterogeneity, multi-modality) To cope with the lack of information and sparse, heterogeneous data and low dose radiation images, unsupervised learning for high-dimensionality  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1909.00384v2">arXiv:1909.00384v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/sy7pm2c2uvdd3pal2russn4xri">fatcat:sy7pm2c2uvdd3pal2russn4xri</a> </span>
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Vision Transformers in Medical Computer Vision – A Contemplative Retrospection [article]

Arshi Parvaiz, Muhammad Anwaar Khalid, Rukhsana Zafar, Huma Ameer, Muhammad Ali, Muhammad Moazam Fraz
<span title="2022-03-29">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Along with this, we also demystify several imaging modalities used in Medical Computer Vision.  ...  Recent escalation in the field of computer vision underpins a huddle of algorithms with the magnificent potential to unravel the information contained within images.  ...  To achieve this task, firstly they extracted meaningful semantic low-dimensional features from high-dimensional visual neural activities (low-level raw fMRI data) using two-layer one dimensional CNN.  ... 
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Representation Learning for Electronic Health Records [article]

Wei-Hung Weng, Peter Szolovits
<span title="2019-09-19">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Due to the advances in machine learning, we now can learn better and meaningful representations from EHR through disentangling the underlying factors inside data and distilling large amounts of information  ...  In this chapter, we first introduce the background of learning representations and reasons why we need good EHR representations in machine learning for medicine and healthcare in Section 1.  ...  Visualization instead requires dimensionality reduction algorithms to get the dimensions to be two or three, which is visualizable for human interpretation.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1909.09248v1">arXiv:1909.09248v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/c3yrpp5wanhc7gb5bjpqe4ivbm">fatcat:c3yrpp5wanhc7gb5bjpqe4ivbm</a> </span>
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