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.
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.
As such predictive intelligence develops and improves —and this is likely to do so exponentially in the coming years— this will have far-reaching consequences for the development of new treatment procedures and novel technologies.
CUSTOMCELLS® offers a comprehensive range of workshops and seminars. technology, processes, machines and the market; Up-to-date information on the our individually bookable training courses give you a deep understanding of.
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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.
KEY DATES: Submission deadline: May 22, Author notification: June 3, Camera ready deadline: July 1, Workshop date: July.
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. While these successes show the promise of DRL, applying learning from game-based DRL to knowledge discovery is fraught with unique challenges, including, but not limited to, extreme data sparsity, power-law distributed samples, and large state and action spaces.
Therefore, it is timely and necessary to provide a venue, which can bring together academia researchers and industry practitioners 1 to discuss the principles, limitations and applications of DRL for knowledge discovery; and 2 to foster research on innovative algorithms, novel techniques, and new applications of DRL to knowledge discovery. All papers will be peer reviewed, single-blinded. Submitted papers will be assessed based on their novelty, technical quality, potential impact, insightfulness, depth, clarity, and reproducibility.
All the papers are required to be submitted via EasyChair system. For more questions about the workshop and submissions, please send email to zhaoxi35 msu. We encourage submissions on a broad range of DRL for knowledge discovery in various domains. Topics of interest include but are not limited to theoretical aspects, algorithms, methods, applications, and systems, such as:.
Cognitive Services And Deep Learning Training | Microsoft Cloud Workshop in Almaty
The class will begin with 40 minutes of supported yoga on the swing and will follow with 35 minutes of live sound therapy while you cocoon yourself in the swing yoga hammock. This workshop can hold a maximum of 6 people. Every BODY welcome… no experience needed. Date: 21st February Time: 7.
Computer vision approaches have made tremendous efforts toward understanding shape from various data formats, especially since entering the deep learning.
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All papers must be formatted according to the ACM proceedings style. Click on the link to access Latex and Word templates for this format. Please ensure the appropriate length of your submission, namely:. Long research papers, are up to 8 pages, plus additional pages for the list of references. These type of papers will have oral and poster presentations at the conference.
Event, Description. Date. Time. Location. The History of Our Things, Join Pat Sweeney, historian for the Osborne Homestead Museum, in a series of three.
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. Friday – Saturday are the workshops, which are smaller meetings focused on current topics, and provide an informal, cutting edge venue for discussion. Toggle navigation Toggle navigation Login. Year Sat Dec 5th through Sat the 12th Saturday is the start of our 2 day industry expo.
June 12, — NeurIPS will be held entirely online. See our blog post for more information.
Yogaquest – Deep Vinyasa Workshop
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.
Important Dates. Jan 27, Mar 12, Deadline for Workshop Paper Submission. Mar 15, Deep learning for multimedia retrieval. Advanced.
At DEEP we had a taste of topics that we want to know more about and advance. The We Count — Digging DEEPer series will be a variety of activities and events that will allow us to learn, collaborate, co-design and share. Artificial intelligence is more and more responsible for decisions that have a huge impact on our lives.
But predictions made using data mining and algorithms can affect population subgroups differently. Academic researchers and journalists have shown that decisions taken by predictive algorithms sometimes lead to biased outcomes, reproducing inequalities already present in society. Is it possible to make a fairness-aware data mining process?
Are algorithms biased because people are too? Or is it how machine learning works at the most fundamental level?