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Deep feature synthesis
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#	Item
1	Fully Autonomous Real-Time Autoencoder-Augmented Hebbian Learning through the Collection of Novel Experiences To appear in: Proc. of the Fifteenth International Conference on the Synthesis and Simulation of Living System Add to Reading List Source URL: eplex.cs.ucf.edu Language: English - Date: 2016-04-27 14:27:24 Computational neuroscience Neuroscience Artificial neural networks Cybernetics Cognition Machine learning Computational statistics Hebbian theory Autoencoder Deep learning Feature learning Unsupervised learning
2	Teaching a computer to be a data scientist Kalyan Veeramachaneni Over the past 2 years, I have been chasing a foundational question: ”How can I make a computer do what my peers and I do as data scientists?” How can I Add to Reading List Source URL: groups.csail.mit.edu Language: English - Date: 2016-01-22 18:19:29 Machine learning Learning Statistics Cognition Deep feature synthesis Predictive analytics Big data Deep learning Data analysis Feature engineering Statistical classification Feature selection
3	Kalyan Veeramachaneni Computer Science and Artificial Intelligence Laboratory, 32-D540, MIT, Cambridge, MA 02139, USA, This version: January 8, 2016 Latest: www.kalyanv.org Add to Reading List Source URL: groups.csail.mit.edu Language: English - Date: 2016-01-08 02:34:58 Evolution Cybernetics Machine learning Association for Computing Machinery Genetic and Evolutionary Computation Conference Deep feature synthesis Evolutionary computation Data mining Evolutionary algorithm Computational intelligence Academia Computer science
4	Deep Feature Synthesis: Towards Automating Data Science Endeavors James Max Kanter Kalyan Veeramachaneni Add to Reading List Source URL: groups.csail.mit.edu Language: English - Date: 2015-10-15 14:47:29 Machine learning Formal sciences Learning Artificial intelligence Statistics K-means clustering Pattern recognition Deep feature synthesis Deep learning Data mining Support vector machine Artificial neural network
5	Real-time Hebbian Learning from Autoencoder Features for Control Tasks To appear in: Proc. of the Fourteenth International Conference on the Synthesis and Simulation of Living Systems (ALIFE 14). Cambridge, MA: MIT Press Add to Reading List Source URL: eplex.cs.ucf.edu Language: English - Date: 2014-06-04 09:58:39 Artificial neural networks Neuroscience Applied mathematics Cybernetics Autoencoder Hebbian theory Feature learning Deep learning Restricted Boltzmann machine Unsupervised learning Biological neural network Backpropagation
6	MODELLING ACOUSTIC FEATURE DEPENDENCIES WITH ARTIFICIAL NEURAL NETWORKS: TRAJECTORY-RNADE Benigno Uria1 1 Iain Murray1 Add to Reading List Source URL: www.cstr.ed.ac.uk Language: English - Date: 2015-09-29 11:06:25 Computational linguistics Speech recognition Acoustic model Artificial neural network Time series Speech synthesis Pattern recognition Machine learning Deep learning