Submissions should be academic papers that demonstrate the benefits of sparse networks in both application and theory from the computer science, cognitive science, neuroscience, biophysics, natural science and like communities.More specific topic areas may include (but are not limited to): Pattern recognition applications including facial identification, voice and language processing, handwriting processing and identification, text string pattern search and identification, image and video processing, or other sensory classifications Machine learning processes including recurrent models, Bayesian models, information-theoretic learning models, or other seo göteborg learning models Neural network architecture including model construction, model tuning and pruning, self-organizing models, cognitive networks, meta-learning algorithms, feature selection, or evolutionary models Abstracts will be accepted until Tuesday, January 31, 2017. All selected speakers will be notified by February 7, 2017, and will be provided with further details and next steps. The HNN Workshop will offer travel and accommodations to select speakers as well as waive registrations costs. Submissions can be directed toward: firstname.lastname@example.org and should include “HNN Workshop Abstract Submission” as the subject. For more information on the event and to register, please visit: https://www.knuedge.com/hnnworkshop/ About KnuEdge Inc. KnuEdge Inc. was founded by former NASA Chief Dan Goldin. The company has separate divisions for voice biometrics and data center neural computing — both of which are delivering industry-changing products in fast-growing, multibillion-dollar markets. KnuVerse uniquely leverages the human voice in its military-grade recognition and authentication technology. It is now possible to authenticate to computers, mobile/web apps and IoT devices with only a few words spoken into a microphone, in any language, no matter how many other people are talking nearby. KNUPATH delivers a new neural computing acceleration technology, and it will reset the standard for chip/system-level compute performance in data centers and IoT devices.
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