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X-WR-CALNAME:DeepLearning
X-ORIGINAL-URL:https://web.math.unipd.it/deeplearning
X-WR-CALDESC:Events for DeepLearning
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DTSTART:20150101T000000
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BEGIN:VEVENT
DTSTART;VALUE=DATE:20180708
DTEND;VALUE=DATE:20180714
DTSTAMP:20260419T075658
CREATED:20200228T104858Z
LAST-MODIFIED:20200228T104858Z
UID:198-1531008000-1531526399@web.math.unipd.it
SUMMARY:Special Session on Interpretable Deep Learning Classifiers @ WCCI (IJCNN) 2018.
DESCRIPTION:Chairs: Plamen P. Angelov\, Lancaster University\, UK p.angelov@lancaster.ac.uk\nJose C. Principe\, University of Florida\, principe@cnel.ufl.edu . \nSynopsis: Deep Learning is becoming a synonym of highly precise (reaching or surpassing capabilities of a human) computational intelligence technique. Very interesting and important results were reported recently in both scientific literature and also grabbed the imagination of the wider public and industry helping propel the interest towards AI\, neural networks\, machine learning. It was applied mostly to solve classification problems in image processing\, but also for predictive tasks in speech processing and other problems. Despite the undoubted success in achieving high precision and avoiding handcrafting in feature selection a number of issues remain unresolved\, such as: i) transparency and interpretability; ii) the requirement for extremely large training data set\, computational resources and time; iii) overfitting and catastrophic failures with high confidence in some cases; iv) convergence proof for the case of reinforcement learning; v) rigid structure unable to be adapted/to dynamically evolve with new samples and/or new classes; vi) repeatability of the results. Methodologically\, the vast majority of the techniques of this hot and quickly developing area are based exclusively on neural networks (convolutional\, belief based\, etc.). Very recently publications appear where the deep learning (multi-layer) architecture with different levels of abstraction is build based on fuzzy rule-based systems or fuzzy sets are used to represent coefficients/weights in Restricted Bolzman Machines\, etc. The aim of the special session is to address the bottleneck issues listed above and discuss and represent alternative and most recent methods\, techniques and approaches that can help resolve these issues. \nThe specific sub-topics that will be of interest include: \n\nInterpretable/Transparent Deep Learning\nComputational and time complexity/efficiency of Deep Learning Methods\nRepeatability of the results of Deep Learning Methods\nDegree of confidence in the results of Deep Learning\nHighly Parallelisable Deep Learning Methods\nDeep Learning with proven convergence\nRe-trainability and dynamically evolving structures/architectures for Deep Learning\nEnsembles of Deep Learning Classifiers\nFuzzy Deep Rule-based Classifiers\nSelf-adaptive and Self-organising Deep Learning Architectures\n\nAlso applications to: \n\nComputer Vision\nImage Classification\nRobotics\nRemote Sensing\nBiology and Tomography\nSurveillance and Defense\nIndustry 4.0\nAssistive Technologies and Digital Health\n\nImportant dates: \n\nPaper Submission Deadline: 15 January 2018\nPaper Acceptance Notification Date: 15 March 2018\nFinal Paper Submission and Early Registration Deadline: 1st May 2018\nIEEE WCCI 2018: 08-13 July 2018\n\nSubmission Guidelines: Please follow the regular submission guidelines of WCCI 2018. Please notify the chairs of your submission by sending an email to: p.angelov@lancaster.ac.uk or principe@cnel.ufl.edu. \nThis special session is supported by the IEEE Task Force on Deep Learning and by Evolving and Adaptive Fuzzy Systems. \nConference Web Site
URL:https://web.math.unipd.it/deeplearning/event/special-session-on-interpretable-deep-learning-classifiers-wcci-ijcnn-2018/
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20180708
DTEND;VALUE=DATE:20180714
DTSTAMP:20260419T075658
CREATED:20200228T104804Z
LAST-MODIFIED:20200228T104804Z
UID:195-1531008000-1531526399@web.math.unipd.it
SUMMARY:Special Session on Deep Learning for Structured and Multimedia Information @ WCCI (IJCNN) 2018.
DESCRIPTION:Chairs: Davide Bacciu (bacciu@di.unipi.it )\, Silvio Jamil F. Guimarães and Zenilton K. G. Patrocínio Jr. \nhttp://www.icei.pucminas.br/projetos/viplab/ijcnn-deepsm/  \nA key factor triggering the deep learning revolution has been its ground-breaking performance on image and video processing applications. These have been built mostly on a (multi-dimensional) raw data representation of the visual information. Multimedia content\, on the other hand\, calls for more articulated data representations catering for the multimodal nature of this information. These are often based on a structured representation that can capture the complexity of the contextual\, semantic and geometrical relationships among the visual\, phonetic and textual entities and concepts. \nScope and Topics: \nThe goal of this special session is to provide a forum for researchers working on the next generation of deep learning models for machine vision and multimedia information\, which are capable of extracting and processing information in a structured representation and/or with a multimodal nature. We welcome contributions proposing innovative deep models dealing with: \n\nlearning hierarchical or networked representations of multimedia information;\nprocessing of structured multimedia information under the form of sequences\, labelled trees\, as well as more general forms of graphs;\nunderstanding and synthesizing of multimodal data;\nfusion of multimodal information.\n\nThis special session is meant to attract researchers from deep learning\, machine vision and multimedia information communities. We aim to bring together researchers with consolidated tradition on structured data processing (such as in machine learning and NLP) with those with machine vision and multimedia processing insight\, but mostly working with flat-data representations. \nTopics of interest for this special session include\, but are not limited to\, the following: \n\ndeep learning models for structured data;\nrepresentation learning in machine vision and multimedia processing;\nhierarchical/structured visual processing;\ndeep models for visual data streams;\ngenerative and variational deep learning for multimedia data;\nmultimedia data synthesis;\nattentional and bio-inspired models for the processing of visual and audio information;\napplied deep learning to machine vision and multimedia processing\, such as: biomedical images and biobanks\, pose and gesture estimation from graphs\, etc.;\ninnovative software and libraries for deep learning and multimedia content understanding.\n\nImportant dates: \n\nPaper Submission Deadline: 15 January 2018\nPaper Acceptance Notification Date: 15 March 2018\nFinal Paper Submission and Early Registration Deadline: 1st May 2018\nIEEE WCCI 2018: 08-13 July 2018\n\nThis special session is supported by the IEEE Task Force on Deep Learning. \nConference Web Site
URL:https://web.math.unipd.it/deeplearning/event/special-session-on-deep-learning-for-structured-and-multimedia-information-wcci-ijcnn-2018/
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20180708
DTEND;VALUE=DATE:20180714
DTSTAMP:20260419T075658
CREATED:20200228T104716Z
LAST-MODIFIED:20200228T104716Z
UID:193-1531008000-1531526399@web.math.unipd.it
SUMMARY:Special Session on Empowering Deep Learning Models @ WCCI (IJCNN) 2018.
DESCRIPTION:Chairs: Nicolò Navarin nnavarin@math.unipd.it\, Luca Oneto\, Luca Pasa and Alessandro Sperduti. \nDescription: In recent years\, Deep Learning has become the go-to solution for a broad range of applications\, often outperforming state-of-the-art. Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition\, computer vision\, drug discovery\, genomics and many others. \nScope and Topics The goal of this special session is to provide a forum for focused discussions on new extensions of deep learning models and techniques\, and to gain a deeper understanding of the difficulties and limitations associated with state-of-the-art approaches and algorithms. Practitioners should provide practical insights to the theoreticians\, which in turn\, should supply theoretical insights and guarantees\, further strengthening and sharpening practical intuitions and wisdom. \nExamples of these possible extensions are: \n\nMultimodal and Multitask Deep Learning\nDeep Transfer Learning\nDeep Recurrent and Recursive Neural Networks\nDeep Learning on Structured Data\nInterpretability of Deep Learning\nPrivate and Federated Deep Learning\nGenerative and Adversarial Deep Learning\nRandomized Deep Learning (Deep ELM\, Deep ESN\, Deep Reservoir Computing)\n\nThe focus of this special session is to attract both solid contributions or preliminary results which show the potentiality and the limitations of new ideas\, refinements\, or contaminations between the different fields of deep learning and other fields of research in solving real world problems. Both theoretical and practical results (e.g. Social Data Analysis\, Speech\, Natural Language Processing\, Cybersecurity) are welcome to our special session. This special session is supported by the IEEE Task Force on Deep Learning . \nImportant dates: \n\nPaper Submission Deadline: 15 January 2018\nPaper Acceptance Notification Date: 15 March 2018\nFinal Paper Submission and Early Registration Deadline: 1st May 2018\nIEEE WCCI 2018: 08-13 July 2018\n\nConference Web Site
URL:https://web.math.unipd.it/deeplearning/event/special-session-on-empowering-deep-learning-models-wcci-ijcnn-2018/
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20171127
DTEND;VALUE=DATE:20171202
DTSTAMP:20260419T075658
CREATED:20200228T105012Z
LAST-MODIFIED:20200228T105012Z
UID:200-1511740800-1512172799@web.math.unipd.it
SUMMARY:2017 IEEE Symposium on Deep Learning (IEEE DL'17) @ SSCI 2017.
DESCRIPTION:Description: Deep Learning (DL) is growing in popularity because it exploits rather well the unreasonable effectiveness of data to solve complex problems in machine learning. In fact\, multi scale machine perception tasks such as object and speech recognitions using DL have recently outperformed systems that have been under development for many years. The principles of DL\, and its ability to capture multi scale representations\, are very general and the technology can be applied to many other problem domains\, which makes it quite attractive. Sponsored by the IEEE Computational Intelligence Society\, this event will attract top scientists\, researchers\, professionals\, practitioners and students from around the world. \nThe goal of the IEEE Symposium on DL is to provide a forum for interactions between researchers and practitioners in DL as well as in Artificial Neural Networks\, Bayesian Learning\, Generative and Predictive Modeling\, Optimization\, Cognitive Architectures and Machine Learning with an interest in DL. We are interested in discussing the new DL advances\, the challenges ahead\, and to brainstorm about new solutions and directions. We also seek applications from large engineering firms dedicated to construction and services in energy\, autonomous transportation\, communications industries\, web\, marketing\, medical and financial services\, and scientific fields that require big data analytics. \nTopics of IEEE DL’17 include but are not limited to: \n\nUnsupervised\, semi-supervised\, and supervised learning\nDeep reinforcement learning (deep value function estimation\, policy learning and stochastic control)\nMemory Networks and differentiable programming\nImplementation issues\, both software and hardware platforms\nApplications in vision\, audio\, speech\, natural language processing\, robotics\, navigation\, control\, games AI\, cognitive architectures\, etc.\nDimensionality expansion and sparse modeling\nLearning representations from large-scale data\nMulti-task learning\nLearning from multiple modalities\nWeakly supervised learning\nMetric learning and kernel learning\nHierarchical models\nParalleliisation in DL\nNon-Iterative DL\nRecursive DL\nIncremental DL\nEvolving DL\nFast DL\n\nSymposium Web Site
URL:https://web.math.unipd.it/deeplearning/event/2017-ieee-symposium-on-deep-learning-ieee-dl17-ssci-2017/
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20160724
DTEND;VALUE=DATE:20160730
DTSTAMP:20260419T075658
CREATED:20200228T105509Z
LAST-MODIFIED:20200228T105509Z
UID:210-1469318400-1469836799@web.math.unipd.it
SUMMARY:Special Session on Deep Learning for Brain-Like Computing and Pattern Recognition @ IJCNN 2016.
DESCRIPTION:Description: CDeep learning is a topic of broad interest\, both to researchers who develop new deep architectures and learning algorithms\, as well as to practitioners who apply deep learning models to a wide range of applications\, from image classification to video tracking\, etc. Brain-like computing combines computational techniques with cognitive ideas\, principles and models inspired by the brain for building information systems used in humans’ common life. Pattern recognition is a conventional area of artificial intelligence\, which focuses on the recognition of patterns and regularities in data. \nRecently\, there has been very rapid and impressive progress in these three areas\, in terms of both theories and applications\, but many challenges remain. This workshop aims at bringing together researchers in machine learning and related areas to discuss the utility of deep learning for brain-like computing and pattern recognition\, the advances\, the challenges we face\, and to brainstorm about new solutions and directions. \nTopics of interest to the special session include\, but are not limited to: \n\nunsupervised\, semisupervised\, and supervised deep learning;\nactive learning\, transfer learning and multi-task learning;\ndimensionality reduction\, metric learning and kernel learning;\nsparse modeling;\nensemble learning;\nhierarchical architectures;\nDoptimization for deep models;\nintelligent data analysis and recommendation systems;\nimplementation issues\, parallelization\, software platforms\, hardware for deep learning and big data analysis\napplications in video\, image\, texture\, text processing\, neuroscience\, medical imaging or any other field.\n\nSpecial Session Web Site
URL:https://web.math.unipd.it/deeplearning/event/special-session-on-deep-learning-for-brain-like-computing-and-pattern-recognition-ijcnn-2016/
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20160724
DTEND;VALUE=DATE:20160730
DTSTAMP:20260419T075658
CREATED:20200228T105429Z
LAST-MODIFIED:20200228T105429Z
UID:208-1469318400-1469836799@web.math.unipd.it
SUMMARY:Special Session on Deep Learning for Big Multimedia Understanding @ IJCNN 2016.
DESCRIPTION:Description: Conventional multimedia understanding is usually built on top of handcrafted features\, which are often much restrictive in capturing complex multimedia content. Recent progress on deep learning opens an exciting new era\, placing multimedia understanding on a more rigorous foundation with automatically learned representations to model the multimodal data and the cross-media interactions. Existing studies have revealed promising results that have greatly advanced the state-of-the-art performance in a series of multimedia research areas\, from the multimedia content analysis\, to modeling the interactions between multimodal data\, to multimedia content recommendation systems\, to name a few here. \nThis special session aims to provide a forum for the presentation of recent advancements in deep learning research that directly concerns the multimedia community. For multimedia research\, it is especially important to develop deep learning methods to capture the dependencies between different genres of data\, building joint deep representation for diverse modalities. \nTopics of interest to the special session include\, but are not limited to: \n\nNovel deep network architectures for multimodal data representation;\nDeep learning for new multimedia applications;\nEfficient training and inference methods for multimedia deep networks;\nEmerging applications of deep learning in multimedia search\, retrieval and management;\nDeep learning for multimedia content analysis and recommendation;\nDeep learning for cross-media analysis\, knowledge transfer and information sharing;\nDistributed computing\, GPUs and new hardware for deep learning in multimedia research;\nOther deep learning topics for multimedia computing\, involving at least two modalities.\n\nDr. Jinhui Tang\, Nanjing University of Science and Technology\, China. Dr. Zechao Li\, Nanjing University of Science and Technology\, China\nSpecial Session Web Site
URL:https://web.math.unipd.it/deeplearning/event/special-session-on-deep-learning-for-big-multimedia-understanding-ijcnn-2016/
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20160724
DTEND;VALUE=DATE:20160730
DTSTAMP:20260419T075658
CREATED:20200228T105338Z
LAST-MODIFIED:20200228T105338Z
UID:206-1469318400-1469836799@web.math.unipd.it
SUMMARY:Special Session on Theoretical Foundations of Deep Learning Models and Algorithms @ IJCNN 2016.
DESCRIPTION:Description: Deep learning models and techniques are becoming more and more the computational tool of choice when facing difficult applicative problems\, such as speech and image understanding. The reason for this huge interest in deep learning is due to the fact that their adoption leads to human (and\, in some cases\, super-human) performances. These successes\, however\, have been mainly obtained on empirical basis\, often thanks to the computational power provided by parallel computer facilities such as GPUs or CPU clusters. \nAlthough some recent works have addressed deep learning from a theoretical perspective\, still there is a limited understanding of why deep architectures work so well and on how to design computationally efficient and effective training This special session aims to gather together leading scientists in deep learning and related areas within computational intelligence\, neuroscience\, machine learning\, artificial intelligence\, mathematics\, and statistics\, interested in all aspects of deep architectures and deep learning\, with a particular emphasis on understanding fundamental principles. \nTopics of interest to the special session include\, but are not limited to: \n\nTheoretical results on representation and learning in natural or artificial deep architectures;\nTheoretical and/or empirical analysis of specific natural or artificial deep architectures or algorithms;\nInnovative deep architectures/algorithms for data representation and analysis\, including both supervised methods like deep convolution networks and unsupervised ones like stacked auto-encoders and deep Boltzmann machines;\nDesign and/or analysis of recurrent and recursive architectures for processing of sequences and more general data structures;\nApplications of deep learning in data representation and analysis\, including recognition\, understanding\, detection\, segmentation\, retrieval\, restoration\, super-resolution\, and compression;\nDeep learning algorithms that efficiently handle large-scale data.\n\nAlessandro Sperduti\, Univ. Padova (Italy)\, Jose C. Principe\, University of Florida (USA)\, Plamen Angelov\, Lancaster University (UK).\nSpecial Session Web Site
URL:https://web.math.unipd.it/deeplearning/event/special-session-on-theoretical-foundations-of-deep-learning-models-and-algorithms-ijcnn-2016/
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20160724
DTEND;VALUE=DATE:20160730
DTSTAMP:20260419T075658
CREATED:20200228T105249Z
LAST-MODIFIED:20200228T105249Z
UID:204-1469318400-1469836799@web.math.unipd.it
SUMMARY:Special Session on Deep Learning\, Medical Imaging\, and Translational Medicine @ IJCNN 2016.
DESCRIPTION:Description: Deep learning has demonstrated its capability for many vision problems\, such as face detection and recognition\, image classification\, etc. It is expected that this technique can benefit the area of medical image analysis\, as well as imaging-based translational medicine. Though a few pioneering works can be found in the literature\, there are still a lot of unresolved issues when applying deep learning for medical images. \nThe goal of special session is to present works that focus on the design and use of deep learning in medical image analysis as well as imaging-based translational medical studies. This special session is going to set the trends and identify the challenges of the use of deep learning methods in the field of medical image. Meanwhile\, it is expected to increase the connection between software developers\, specialist researchers and applied end-users from diverse fields. \nTopics of interest to the special session include\, but are not limited to: \n\nImage descriptor and feature extraction;\nImage super-resolution;\nImage reconstruction;\nImage registration;\nImage segmentation and labeling;\nComputer-assisted lesion detection;\nComputer-assisted diagnosis;\nDeep learning model selection;\nMeta-heuristic techniques for fine-tuning parameter in deep learning-based architectures;\nOther related translational medical applications.\n\nOrganized by Qian Wang\, Jun Shi\, Shihui Ying\, Manhua Liu and Yonghong Shi\nSpecial Session Web Site
URL:https://web.math.unipd.it/deeplearning/event/special-session-on-deep-learning-medical-imaging-and-translational-medicine-ijcnn-2016/
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20160427
DTEND;VALUE=DATE:20160430
DTSTAMP:20260419T075658
CREATED:20200228T105144Z
LAST-MODIFIED:20200228T105144Z
UID:202-1461715200-1461974399@web.math.unipd.it
SUMMARY:Special Session on Deep Learning @ ESANN 2016.
DESCRIPTION:Description: Deep learning models and techniques are nowadays the leading approaches to face complex machine learning and pattern recognition problems\, especially when considering perceptual tasks such as speech and image understanding. The adoption of deep architectures\, comprising multiple\, adaptable\, processing layers\, has recently allowed significant improvements in performance for these type of tasks. Both unsupervised and supervised approaches for training deep architectures have been empirically explored\, also thanks to the adoption of parallel computer facilities such as GPUs or CPU clusters. Despite of that\, there is a limited understanding of why deep architectures work so well and on how to design computationally efficient and effective training algorithms. A natural source of inspiration for a better understanding of these issues is the study of human brain\, where deep structures are now well recognized and pervasive (e.g. human visual recognition requires the activation of a hierarchy of processing stages and pathways.) \nThis special session focuses on all aspects of deep architectures and deep learning\, with a particular emphasis on understanding fundamental principles. Because of that\, it aims to bring together leading scientists in deep learning and related areas within neuroscience\, machine learning\, artificial intelligence\, mathematics\, and statistics. \nTopics of interest to the special session include\, but are not limited to: \n\nTheoretical results on representation and learning in natural or artificial deep architectures;\nTheoretical and/or empirical analysis of specific natural or artificial deep architectures or algorithms;\nInnovative deep architectures/algorithms for data representation and analysis\, including both supervised methods like deep convolution networks and unsupervised ones like stacked auto-encoders and deep Boltzmann machines;\nDesign and/or analysis of recurrent and recursive architectures for processing of sequences and more general data structures;\nApplications of deep learning in data representation and analysis\, including recognition\, understanding\, detection\, segmentation\, retrieval\, restoration\, super-resolution\, and compression;\nDeep learning algorithms that efficiently handle large-scale data;\nDeep learning software and hardware architectures for applications.\n\nSpecial Session on Deep Learning @ ESANN 2016
URL:https://web.math.unipd.it/deeplearning/event/special-session-on-deep-learning-esann-2016/
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