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Machines watching movies
⏲ 1 min 94 sec ✓ 17-Apr-2016
Mardon and Mitsuhashi Tender Engine Teaser
⏲ 2 min 23 sec ✓ 21-Sep-2019
About anything else you need to know (2018)
⏲ 1 min 51 sec ✓ 06-Nov-2018
RMR_Live @ SITUATED SOUNDS _ TORONTO
⏲ 14 min 82 sec ✓ 31-Mar-2021
Stage Presentatie RNN7 '07'08
⏲ 2 min 63 sec ✓ 26-Feb-2008
Canon EOS 60D Test 50mm 1.8 Lens
⏲ 76 sec ✓ 05-Jun-2011
Survol travaux 1
⏲ 1 min 61 sec ✓ 16-Mar-2017
Sequence-to-Action: End-to-End Semantic Graph Generation for Semantic Parsing
⏲ 14 min 73 sec ✓ 20-Aug-2018
Beyond Word2Vec: Recent Developments in Document Embedding
⏲ 41 min 49 sec ✓ 02-Nov-2017
\
⏲ 2 min 32 sec ✓ 11-Feb-2020
Deep Learning Aphex Twin
⏲ 4 min 79 sec ✓ 12-Nov-2017
RNN14 GTI-R 0-160Kph
⏲ 1 min 15 sec ✓ 08-Jul-2012
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_machines-watching-movies.webp "How would machines understand a movie? nNew advancements in machine learning make real-time image analysis possible and move us closer to nthose long-anticipated philosophical sci-fi scenarios. All the captions for these clips are generated in real-time using machine learning algorithms. They are not always right. Sometimes they are funny, flat out or understandably wrong. When they are right though - they are uncanny.nnThis research experiment is using recurrent neural networks (RNN) using Andr")
_mardon-and-mitsuhashi-tender-engine-teaser.webp "Tender Engine is a collaboration between choreographer Alexa Mardon, choreographer/set designer Erika Mitsuhashi, programmer Brynn McNab, a recursive neural network named UXIE, sound designer Paul Paroczai and performers Elissa Hanson and Zahra Shahab. Tender Engine is a contemporary dance/installation piece, with highly integrated new media elements (algorithms, projection design). An RNN which has learned to speak from the language the performers have spoken to it underscores the work. Tender")
_about-anything-else-you-need-to-know-2018.webp "About anything else you need to know / 당신이 알아야 할 다른 것에 대해서nnArtist / 작가 : Youngkak Cho / 조영각nnSoftware Develop cooperation : Youngtak Cho / 조영탁nnType _ 1CH Interactive installation, 2CH Video(20min for each 1channel)nMaterial _ PC, Projector, Monitor, Webcam, Screen, Deep Learning Libraries(GAN, RNN), News Text DB Set, News Video DB Set nSize _ Dimension VariablenYear _ 2018 nn[당신이 알아야 할 다른 것에 대해서 (About anything else you ne")
_rmr-live-situated-sounds---toronto.webp "Recurrent Morphing Radio (RMR) is a generative neural audio production system fuelled by the most consumed genres on digital music platforms. The system extracts and restructures the main audio features found in these styles and generates new sounds that are later continuously broadcasted. For the development of RMR, Interspecifics have employed computer models such as Recurrent Neural Networks (RNN) to build an automated system using platforms such as OpenAI’s Jukebox, SampleRNN, and Wavenet,")
_stage-presentatie-rnn7-39073908.webp "Dit filmpje is een snel gemaakte presentatie over mijn tv programma's die ik monteerde bij RNN7. Door deze presentatie krijg je een beeld van de montage stijl van Wonen en slapen met John, en zie je hoe makkelijk het programma in elkaar zit. De beelden zijn gefilmd door de cameramensen van RNN7, waaronder stagiaires.nVoor meer informatie over RNN7 en de tv programma's kijkt u ophttp://www.RNN7.nl, http://www.wonenmetjohn.nl en http://www.slapenmetjohn.nl Het doel van deze presentatie is om med")
_canon-eos-60d-test-50mm-1-8-lens.webp "Testing out my Canon EOS 60D With a Canon 50mm f/1.8 II.nnA Few shots of my dog and my mother baking a cake.nn1920x1080 24fpsnnhttp://www.youtube.com/watch?v=gn_iWP_9N0o&NR=1nnhttp://www.youtube.com/watch?v=fH-InLGEKK0&feature=relatednnnhttp://www.youtube.com/watch?v=rNn-_vC33GI&feature=related")
_survol-travaux-1.webp "Vidéo prise par drone - Survol des travaux de remobilisation des marges alluviales (1ere partie)nLieu = RNN de l'île de la PlatièrenDate = Février 2017")
_sequence-to-action-end-to-end-semantic-graph-generation-for-semantic-parsing.webp "This paper proposes a neural semantic parsing approach – Sequence-to-Action, which models semantic parsing as an end-to-end semantic graph generation process. Our method simultaneously leverages the advantages from two recent promising directions of semantic parsing. Firstly, our model uses a semantic graph to represent the meaning of a sentence, which has a tight-coupling with knowledge bases. Secondly, by leveraging the powerful representa-tion learning and prediction ability of neural netwo")
_beyond-word2vec-recent-developments-in-document-embedding.webp "Main Talk: Beyond Word2Vec: Recent Developments in Document EmbeddingnnAndrew Blevins- Metis (https://www.thisismetis.com/)nnAbstract: It is easy to be amazed by their seemingly magical power of word2vec. But in real business use cases, we rarely need to understand single words. So how do we apply the power of word2vec to phrases, sentences, paragraphs or entire documents? We will compare various techniques of generating useful representations of documents of indeterminate length and look at w")
_92.webp "\ from rnn")
_deep-learning-aphex-twin.webp "AI Generated Music trained on a model of Aphex Twin Midi files using the Magenta Performance RNN.nnListen to the song:nhttps://soundcloud.com/ac_gr-ns/afx-rnn")
_rnn14-gti-r-0-160kph.webp "Italvolanti Imola steeringwheel, 3")
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