Transparency and Interpretability for Learned Representations of Artificial Neur

Description: Transparency and Interpretability for Learned Representations of Artificial Neural Networks by Richard Meyes Estimated delivery 3-12 business days Format Paperback Condition Brand New Description Starting off with individual and purely theoretical research efforts in the 1950s, AI has grown into a fully developed research field of modern times and may arguably emerge as one of the most important technological advancements of mankind. Publisher Description Artificial intelligence (AI) is a concept, whose meaning and perception has changed considerably over the last decades. Starting off with individual and purely theoretical research efforts in the 1950s, AI has grown into a fully developed research field of modern times and may arguably emerge as one of the most important technological advancements of mankind. Despite these rapid technological advancements, some key questions revolving around the matter of transparency, interpretability and explainability of an AIs decision-making remain unanswered. Thus, a young research field coined with the general term Explainable AI (XAI) has emerged from increasingly strict requirements for AI to be used in safety critical or ethically sensitive domains. An important research branch of XAI is to develop methods that help to facilitate a deeper understanding for the learned knowledge of artificial neural systems. In this book, a series of scientific studies are presented that shed lighton how to adopt an empirical neuroscience inspired approach to investigate a neural networks learned representation in the same spirit as neuroscientific studies of the brain. Author Biography Richard Meyes is head of the research group "Interpretable Learning Models" at the institute of Technologies and Management of Digital Transformation at the University of Wuppertal. His current research focusses on transparency and interpretability of decision-making processes of artificial neural networks. Details ISBN 365840003X ISBN-13 9783658400033 Title Transparency and Interpretability for Learned Representations of Artificial Neural Networks Author Richard Meyes Format Paperback Year 2022 Pages 211 Edition 1st Publisher Springer Fachmedien Wiesbaden GE_Item_ID:160031966; About Us Grand Eagle Retail is the ideal place for all your shopping needs! With fast shipping, low prices, friendly service and over 1,000,000 in stock items - you're bound to find what you want, at a price you'll love! Shipping & Delivery Times Shipping is FREE to any address in USA. Please view eBay estimated delivery times at the top of the listing. Deliveries are made by either USPS or Courier. We are unable to deliver faster than stated. International deliveries will take 1-6 weeks. NOTE: We are unable to offer combined shipping for multiple items purchased. This is because our items are shipped from different locations. Returns If you wish to return an item, please consult our Returns Policy as below: Please contact Customer Services and request "Return Authorisation" before you send your item back to us. Unauthorised returns will not be accepted. Returns must be postmarked within 4 business days of authorisation and must be in resellable condition. Returns are shipped at the customer's risk. We cannot take responsibility for items which are lost or damaged in transit. For purchases where a shipping charge was paid, there will be no refund of the original shipping charge. Additional Questions If you have any questions please feel free to Contact Us. Categories Baby Books Electronics Fashion Games Health & Beauty Home, Garden & Pets Movies Music Sports & Outdoors Toys

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End Time: 2025-01-09T03:14:14.000Z

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