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Neural Kubrick | Interactive Architecture Lab
Stanley Kubrick in 1968 speculated on the arrival of human-level Artificial Intelligence in “2001 A Space Odyssey”. Some 16 years past his prediction, our project “Neural Kubrick” examines the state of the art in Machine Learning, using the latest in “Deep Neural Network” techniques to reinterpret and redirect Kubrick’s own films. Three machine learning algorithms take respective roles in our AI film crew; Art Director, Film Editor and Director of Photography.

The outlook of the project is an artist-machine collaboration. The limitations of the machine are achieved by the artist and the limitations of the artist are achieved by the algorithm. In the context of the project, what the machine interprets is limited to either numbers, classification of features or generation of abstract images. This output is curated by us into a coherent narrative, translated back into human perception.

The project is based on Stanley Kubrick’s movies as input for three machine learning models, namely The Shining, A Clockwork Orange and 2001 A Space Odyssey. The generated videos display a machinic interpretation of the three movies, through a collaborative effort between the artist and the algorithm.
ai  rnn  neuralnetwork  film  art  architecture  vr 
4 days ago by mildlydiverting
[1803.01271] An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
For most deep learning practitioners, sequence modeling is synonymous with recurrent networks. Yet recent results indicate that convolutional architectures can outperform recurrent networks on tasks such as audio synthesis and machine translation. Given a new sequence modeling task or dataset, which architecture should one use? We conduct a systematic evaluation of generic convolutional and recurrent architectures for sequence modeling. The models are evaluated across a broad range of standard tasks that are commonly used to benchmark recurrent networks. Our results indicate that a simple convolutional architecture outperforms canonical recurrent networks such as LSTMs across a diverse range of tasks and datasets, while demonstrating longer effective memory. We conclude that the common association between sequence modeling and recurrent networks should be reconsidered, and convolutional networks should be regarded as a natural starting point for sequence modeling tasks. To assist related work, we have made code available at this https://github.com/locuslab/TCN
CNN  vs  RNN  DL-theory  papers  LSTM  GRU  TCN 
27 days ago by foodbaby
MIT Deep Learning
Collection of MIT courses and lectures on deep learning, deep reinforcement learning, autonomous vehicles, and artificial intelligence taught by Lex Fridman
deeplearning  video  learning  AI  reinforcementlearning  cnn  rnn  neuralnetwork 
4 weeks ago by sachaa
The Unreasonable Effectiveness of Recurrent Neural Networks
Amazing article showing the generation of new things from trained RNNs. Includes one of my favourite examples.- machine generated Shakespeare.
rnn  recurrentneuralnetwork  ann  artificialneuralnetwork  ai  machinelearning  deeplearning 
5 weeks ago by ids
larspars/word-rnn: Recurrent Neural Network that predicts word-by-word
Recurrent Neural Network that predicts word-by-word - larspars/word-rnn
machine_learning  ml  rnn 
5 weeks ago by jkeefe
Mask R-CNN with OpenCV - PyImageSearch
In this tutorial you will learn how to use Mask R-CNN with Deep Learning, OpenCV, and Python to predict pixel-wise masks for every object in an image.
python  CV  RNN  ML  tutorial  opencv 
7 weeks ago by mootPoint

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