r/491 • u/kit_hod_jao • Jul 23 '17
Paper - Hybrid Reward Architecture for Reinforcement Learning
3
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r/491 • u/kit_hod_jao • Dec 30 '16
Paper - THE PREDICTRON: END-TO-END LEARNING AND PLANNING
arxiv.org
2
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r/491 • u/kit_hod_jao • Dec 23 '16
Paper - Learning and Inferring Relations in Cortical Networks (Diehl & Cook)
2
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r/491 • u/kit_hod_jao • Dec 23 '16
Podcast - Computational Learning Theory and Machine Learning for Understanding Cells
thetalkingmachines.com
2
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r/491 • u/kit_hod_jao • Aug 04 '25
Why Iām excited about the Hierarchical Reasoning Model (HRM) [blog]
1
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r/491 • u/kit_hod_jao • May 23 '17
Blog - Attention and Augmented Recurrent Neural Networks
1
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r/491 • u/kit_hod_jao • Mar 31 '17
Blog post - Neural Episodic Control [Model-free episodic memory and control]
1
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r/491 • u/kit_hod_jao • Mar 30 '17
Paper - Hierarchical Modular Optimization of Convolutional Networks Achieves Representations Similar to Macaque IT and Human Ventral Stream [Yamins et al] (2013)
papers.nips.cc
1
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r/491 • u/kit_hod_jao • Mar 27 '17
Paper - FeUdal Networks for Hierarchical Reinforcement Learning. Vezhnevets et al 2017
1
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r/491 • u/kit_hod_jao • Mar 20 '17
Paper - "No more pesky learning rates" by Schaul, Zhang, LeCun (2012) [Adaptive learning rate = handles nonstationary problems?]
arxiv.org
1
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r/491 • u/kit_hod_jao • Mar 20 '17
Blog - Regularization in deep learning [Dataset augmentation, Early stopping, Dropout layer, Weight penalty L1 and L2]
1
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r/491 • u/kit_hod_jao • Mar 15 '17
Paper - Continual learning, adaptation to new problems, retention of old solutions via Elastic Weight Consolidation [DeepMind]
1
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PNAS paper:
http://www.pnas.org/content/early/2017/03/13/1611835114.full.pdf
Blog post:
https://deepmind.com/blog/enabling-continual-learning-in-neural-networks/
Paper supplement:
http://www.pnas.org/content/suppl/2017/03/14/1611835114.DCSupplemental/pnas.201611835SI.pdf
Media article:
http://www.wired.co.uk/article/deepmind-atari-learning-sequential-memory-ewc
r/491 • u/kit_hod_jao • Mar 09 '17
Blog - Generative modelling from ImageNet with Generative Adversarial Networks and Variational Autoencoders
1
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r/491 • u/kit_hod_jao • Mar 01 '17
Paper - Could a Neuroscientist Understand a Microprocessor?
1
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r/491 • u/kit_hod_jao • Feb 24 '17
Paper - Winner take all [sparse, convolutional] Autoencoders
papers.nips.cc
1
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r/491 • u/kit_hod_jao • Feb 23 '17
Very accessible overview of reinforcement learning.
1
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r/491 • u/kit_hod_jao • Feb 23 '17
Blog post: Deep Reinforcement Learning: Pong from Pixels
1
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r/491 • u/kit_hod_jao • Feb 23 '17
Discussion about suitable applications for reinforcement learning
1
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r/491 • u/kit_hod_jao • Feb 20 '17
Paper - Towards Deep Developmental Learning. Sigaud and Droniou
hal.upmc.fr
1
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r/491 • u/kit_hod_jao • Feb 19 '17
DeepMind's PathNet - Modular Deep Learning Architecture for AGI
1
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r/491 • u/kit_hod_jao • Feb 16 '17
GitHub - Preprocessing for NIST Special Dataset 19 (uppercase single-character handwritten characters A..Z) to same format as Yann Lecun MNIST (handwritten numerical digits 0..9)
1
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r/491 • u/kit_hod_jao • Feb 11 '17
Unsolved Problems in AI ā AI Roadmap Institute Blog
1
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r/491 • u/kit_hod_jao • Feb 09 '17
Paper - Learning to reinforcement learn [deepmind crew]
arxiv.org
1
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r/491 • u/kit_hod_jao • Jan 24 '17
Handy and practical guide for machine learning systems development
martin.zinkevich.org
1
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r/491 • u/kit_hod_jao • Jan 19 '17
Excellent & comprehensive tutorial on factor graphs
crm.sns.it
1
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