Both disciplines have had a remarkable impact in data and knowledge analysis, as well as knowledge representation, and in fact constitute two complementary directions for modeling linguistic phenomena and solving semantically complex problems. In this context, and following the main foundations set in past editions, SemDeep-6 aims to bring together SW and DL research as well as industrial communities. Call for Papers: SemDeep-6 is interested in contributions of Deep Learning to classic problems in semantic applications, such as: semi-automated ontology learning, ontology alignment, ontology annotation, duplicate recognition, ontology prediction, knowledge base completion, relation extraction, and semantically grounded inference, among many others. At the same time, we invite contributions that analyse the interaction of Semantic Web technologies and resources with DL architectures, such as knowledge-based embeddings, lexical entailment, relation classification or knowledge base completion. This year we are particularly interested in how this combination can contribute to the bigger field of Explainable AI. This workshop seeks to provide an invigorating environment where semantically challenging problems which appeal to both Semantic Web and Deep Learning communities are addressed and discussed. While progress has been made in recent years, the evaluation of WSD models has been limited to a set of mostly SemEval-based standard datasets. Each instance in the dataset is associated with a target word and single sense, and therefore systems are not required to model all senses of the target word, but rather only a single sense. The task is to decide if the target word is used in the target sense or not, a binary classification task. Therefore, the task statement of WiC-TSV resembles the usage of automatic tagging in enterprise settings.
Deep Learning for Geometric Computing
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Times and dates are listed below; see the individual Workshop web pages for DeepTest, 2nd Workshop on Testing for Deep Learning and Deep Learning for.
Irrespective of your level of knowledge, we offer you information modules tailored to your needs in order to specifically build up or deepen your know-how with regard to battery cell technology and production. Our practical training courses give you the opportunity to adapt our knowledge and incorporate it into your projects in a targeted manner. Who should participate? In practical modules, starting with raw materials, slurry and electrodes, the production of pouch cells and electrochemical characterization, cell assembly is carried out in a practical manner in this seminar.
The corresponding lecture program provides insights into the latest trends in technology. You can open the specific seminar program by clicking the button below.
Yogaquest – Deep Vinyasa Workshop
While supervised and unsupervised learning have been extensively used for knowledge discovery for decades and have achieved immense success, much less attention has been paid to reinforcement learning in knowledge discovery until the recent emergence of deep reinforcement learning DRL. By integrating deep learning into reinforcement learning, DRL is not only capable of continuing sensing and learning to act, but also capturing complex patterns with the power of deep learning.
Recent years have witnessed the enormous success of DRL for numerous domains such as the game of Go, video games, and robotics, leading up to increasing advances of DRL for knowledge discovery. For instance, RL-based recommender systems have been developed to produce recommendations that maximize user utility reward in the long run for interactive systems; RL-based traffic signal systems have been designed to control traffic lights in real time to enhance traffic efficiency for urban computing.
Similar excitement has been generated in other areas of knowledge discovery, such as graph optimization, interactive dialogue systems, and big data systems.
We hope that PRIME workshop becomes a nest for high-precision predictive medicine, one that is set to PRIME-MICCAI will feature a single-track workshop with keynote speakers with deep expertise in Workshop date: Oct 13,
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Identifying and Understanding Deep Learning Phenomena
Big and complex data is fuelling diverse research directions in both medical image analysis and computer vision research fields. These can be divided into two main categories: 1 analytical methods , and 2 predictive methods. While analytical methods aim to efficiently analyse, represent and interpret data static or longitudinal , predictive methods leverage the data currently available to predict observations at later time-points i.
Workshop to discuss the implementation of paragraphs , and to of ecosystems and the long-term sustainability of deep-sea fish stocks have agreed that the Workshop should be postponed to a later date.
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As you shift to this deeper psychology of dating, you end up with many unexpected benefits even if someone is not your life partner, they might become a life-long friend. You laugh more.
Cognitive Services And Deep Learning Training | Microsoft Cloud Workshop in Almaty
Toggle navigation. The purpose of the Neural Information Processing Systems annual meeting is to foster the exchange of research on neural information processing systems in their biological, technological, mathematical, and theoretical aspects. The core focus is peer-reviewed novel research which is presented and discussed in the general session, along with invited talks by leaders in their field. On Sunday is an Expo, where our top industry sponsors give talks, panels, demos, and workshops on topics that are of academic interest.
The general sessions are held Tuesday – Thursday, and include talks, posters, and demonstrations.
The workshop will be co-located with the DATE conference. Impact of On-Chip Interconnect on In-Memory Acceleration of Deep Neural.
Deep video understanding is a difficult task which requires systems to develop a deep analysis and understanding of the relationships between different entities in video, to use known information to reason about other, more hidden information, and to populate a knowledge graph KG with all acquired information. The aim of this workshop is to push the limits of multimodal extraction, fusion, and analysis techniques to address the problem of analysing long duration videos holistically and extracting useful knowledge to utilize it in solving different types of queries.
The target knowledge includes both visual and non-visual elements. As videos and multimedia data are getting more and more popular and usable by users in different domains, the research, approaches and techniques we aim to be applied in this workshop will be very relevant in the coming years and near future. This workshop will support two tracks of research contributions:.
Track 1: Interested authors are invited to apply their approaches and methods on a novel High-Level Video Understanding HLVU dataset being made available by the workshop organizers. These include 10 movies with a Creative Commons license. The organizers will also support evaluation and scoring of two main query types distributed with the dataset:. Track 2: Contributions related but not limited to the following topics applied on the provided HLVU dataset or any external datasets are invited:.
Submissions shall be single blind, i. Papers submitted at ICMI must not have been published previously.
Dating workshop events in Ann Arbor, MI
Our flagship event is the annual WiML Workshop, a technical workshop for women to present their research in machine learning. Looking for local meetups? Check out WiMLDS, another organization that supports women in machine learning by organizing local meetups. Our goal is to enhance the experience of women in machine learning, and thereby increase the number and impact of women in machine learning.
We work to increase awareness and appreciation of the achievements of women in machine learning. Our programs help women build their technical confidence and their voice, and our publicity efforts help ensure that women in machine learning and their achievements are known in the community.
It is an academic workshop co-located with the DeepSec conference, and it will be in parallel with the DeepSec conference days. Neutral We are neutral and we.
Conventional deep learning architectures involve composition of simple feedforward processing functions that are explicitly defined. Recently, researchers have been exploring deep learning models with implicitly defined components. To distinguish these from conventional deep learning models we call them deep declarative networks , borrowing nomenclature from the programming languages community Gould et al. Processing nodes in deep declarative networks involve solving an optimization problem in the forward pass.
End-to-end learning back-propagates gradients through the node, which requires the optimization problem to be differentiable. A few recent works have studied various optimization problem classes and shown how backpropagation is possible even without the knowledge of the algorithm used for solving the problem in the first place Agrawal et al. The ideas have been applied to various problems including video classification Fernando and Gould, ; Cherian et al.
Variants of deep declarative networks have also been studied recently such as methods for imposing hard constraints on the output of neural network models Neila et al. This workshop explores the advantages and potential shortcomings of declarative networks and their variants, bringing ideas developed in different contexts under a common umbrella. We will discuss technical issues that need to be overcome in developing such models and applications of these models to computer vision problems that show benefit over conventional approaches.
Topics include:. Times below are given for the first instance; add 12 hours for the repeated sessions. We invite paper submissions of up to four 4 pages describing work in areas related to the workshop topics. Accepted submissions will be presented as short orals or posters at the workshop and will appear on the workshop website.
Workshop on Deep Reinforcement Learning for Knowledge Discovery
Deep generative models are a large class of learning algorithms which have stolen the attention of artists over the past two years, by hallucinating imitations of images from the uncanny valley. This workshop will survey the fast-moving landscape of these algorithms, reviewing the properties of variational autoencoders and generative adversarial networks, as well as surveying existing codebases which implement them and artistic projects which have made use of them. The tutorial will overview how to install and use the software, and various considerations in constructing a dataset to train it on.
Event, Description. Date. Time. Location. The History of Our Things, Join Pat Sweeney, historian for the Osborne Homestead Museum, in a series of three.
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customcells® workshops & seminars
No matter what you are faced with, from physical issues, such as feeling tired, stressed, or exhausted, to emotional and psychological issues, such as healing from grief or loss, dealing with depression, anxiety and fear, or relationship problems, this retreat will address your needs. This deeply healing, personal growth retreat integrates the latest research and understandings in scientific and spiritual development.
It is for people who long for deeper meaning and more honesty with themselves and others, who are tired of being enslaved by their fear and resignation, and have come to realize that if they want to change the world they have to start with themselves.
Workshop: Hyper-Realistic Multimedia for Enhanced Quality of Experience: Keynote. Date & Time The popularity of deep learning in multimedia processing tasks has largely increased in recent years due to its impressive.
ICSE workshops provide forums for small-group discussions on topics in software engineering research and practice. Workshops also provide opportunities for researchers to exchange and discuss scientific and engineering ideas at an early stage, before they have matured to warrant conference or journal publication. In this manner, an ICSE workshop serves as an incubator for a scientific community that forms and shares a particular research agenda.
Each workshop at ICSE is one or two days long no half-day workshops and will be held before the main conference. Participation in an ICSE workshop is preferably open. Scope and Goal Each workshop at the International Conference on Software Engineering ICSE provides a forum for a group of 20—50 participants to discuss a topic in software engineering research and practice. An ICSE workshop serves as an incubator for a scientific community that forms and shares a particular research agenda.
An ICSE workshop also provides opportunities for researchers to exchange and discuss scientific and engineering ideas at an early stage, before they have matured to warrant conference or journal publication. In evaluating a workshop proposal, special attention will be given to the following criteria:.
Deeper and deeper
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Important Dates. Submission deadline: 1 March 20 March (extended); Author notification: 13 April ; Workshop.
The 7th workshop Artificial Intelligence for Knowledge Management focus on AI applied to face the current challenge such as climate change, eco-innovation, societal innovation and global security. The objective of this multidisciplinary session is to gather both researchers and practitioners to discuss methodological, technical and organizational aspects of AI used for knowledge management and to share the feedback on KM applications using AI.
Knowledge management powered by AI for Business Intelligence, advisors, simulators, virtual training, all applications of machine learning to support innovation and eco-innovation, knowledge visualization for improving the creativity and human-machine interfaces, image mining making links between data and images ex bio-detection and others are welcome.
Despite achieving great success in a range of important applications deep learning continues to face challenging questions around its robustness, extrapolation and transfer learning, reasoning and explanation capabilities. Developments in the field of neural-symbolic integration offer an opportunity to address such challenges through the integration of well-founded symbolic Artificial Intelligence AI with efficient neural computation.
The Workshop on Neural-Symbolic Learning and Reasoning will provide a forum for the presentation, exchange of ideas, and discussion of the key topics related to neural-symbolic computing and AI. The aim of this workshop is to provide a forum where international participants can share knowledge on applying NLP to the Financial Technology FinTech domain. With the sharing of the researchers in FinNLP, the challenging problems of blending FinTech and NLP will be identified, and the future research direction will be shaped.
The 3rd IJCAI Workshop on Affective Computing brings together researchers on affective computing topics, including machine learning for affect recognition from vision, video. The BOOM workshop aims at catalyzing synergies among biomedical informatics, artificial intelligence, machine learning, and optimization. This workshop is targeting an audience of applied mathematicians, computer scientists, industrial engineers, bioinformaticians, computational biologists, clinicians and healthcare researchers who are interested in exploring the emerging and fascinating interdisciplinary topics.
The modelling and simulation of complex systems, such as ecosystem, social networks, economic systems and transportation systems, is difficult to accomplish using traditional computational approaches due to their distributed and dynamic features. The Qualitative Reasoning QR community is involved with the development and application of qualitative representations to understand the world from incomplete, imprecise, or uncertain data.