TCN Paper Ideas - Revision history

Gisely: /* Spring 2016 */

2016-01-08T21:02:59Z

Spring 2016

← Older revision		Revision as of 20:54, 8 January 2016
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	* [http://www.ncbi.nlm.nih.gov/pubmed/19346478 Learning reward timing in cortex through reward dependent expression of synaptic plasticity]		* [http://www.ncbi.nlm.nih.gov/pubmed/19346478 Learning reward timing in cortex through reward dependent expression of synaptic plasticity]
			* [http://www.cell.com/cell/abstract/S0092-8674%2815%2900973-3 Central Cholinergic Neurons Are Rapidly Recruited by Reinforcement Feedback]
			* [http://www.sciencedirect.com/science/article/pii/S0960982215004790 Selective Activation of a Putative Reinforcement Signal Conditions Cued Interval Timing in Primary Visual Cortex]
			* [http://www.sciencedirect.com/science/article/pii/S0896627305003624 Uncertainty, Neuromodulation, and Attention]
			* [http://www.gatsby.ucl.ac.uk/~dayan/papers/25lessons.pdf Twenty-Five Lessons from Computational Neuromodulation]

Gisely: /* Spring 2016 */

2016-01-08T20:59:52Z

Spring 2016

← Older revision		Revision as of 20:51, 8 January 2016
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	Vijay Mohan a post-doc from UNC generously put together this reading list for me on computational models of neuromodulators. Haven't read them all yet, but looks like some good stuff and might be a good way to add some neuroscience to the mix to counterbalance all the deep learning.		Vijay Mohan a post-doc from UNC generously put together this reading list for me on computational models of neuromodulators. Haven't read them all yet, but looks like some good stuff and might be a good way to add some neuroscience to the mix to counterbalance all the deep learning.

	* [~~Learning reward timing in cortex through reward dependent expression of synaptic plasticity \|~~ http://www.ncbi.nlm.nih.gov/pubmed/19346478]		* [http://www.ncbi.nlm.nih.gov/pubmed/19346478 Learning reward timing in cortex through reward dependent expression of synaptic plasticity]

Gisely: /* Spring 2016 */

2016-01-08T20:59:13Z

Spring 2016

← Older revision		Revision as of 20:51, 8 January 2016
Line 19:		Line 19:
	Vijay Mohan a post-doc from UNC generously put together this reading list for me on computational models of neuromodulators. Haven't read them all yet, but looks like some good stuff and might be a good way to add some neuroscience to the mix to counterbalance all the deep learning.		Vijay Mohan a post-doc from UNC generously put together this reading list for me on computational models of neuromodulators. Haven't read them all yet, but looks like some good stuff and might be a good way to add some neuroscience to the mix to counterbalance all the deep learning.

	* Learning reward timing in cortex through reward dependent expression of synaptic plasticity		* [Learning reward timing in cortex through reward dependent expression of synaptic plasticity \| http://www.ncbi.nlm.nih.gov/pubmed/19346478]

Gisely: /* Spring 2016 */

2016-01-08T20:57:30Z

Spring 2016

← Older revision		Revision as of 20:49, 8 January 2016
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	* Hippocampal place cells construct reward related sequences through unexplored space		* Hippocampal place cells construct reward related sequences through unexplored space
	* Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images, http://arxiv.org/abs/1506.07365[11]		* Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images, http://arxiv.org/abs/1506.07365[11]

			Vijay Mohan a post-doc from UNC generously put together this reading list for me on computational models of neuromodulators. Haven't read them all yet, but looks like some good stuff and might be a good way to add some neuroscience to the mix to counterbalance all the deep learning.

			* Learning reward timing in cortex through reward dependent expression of synaptic plasticity

Gisely: /* Spring 2016 */

2015-12-27T07:10:17Z

Spring 2016

← Older revision		Revision as of 07:02, 27 December 2015
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	Ideas from the Nando Fretas AMA:		Ideas from the Nando Fretas AMA:

	* Teaching machines to read and comprehend, http://arxiv.org/abs/1506.03340[1~~] (can someone help me with how to add links to this properly???)~~		* Teaching machines to read and comprehend, http://arxiv.org/abs/1506.03340[1]
	* Spatial transformer networks, http://arxiv.org/abs/1506.02025[2]
	* Pointer networks, http://arxiv.org/abs/1506.03134[3]		* Pointer networks, http://arxiv.org/abs/1506.03134[3]
	* Neural GPUs learn algorithms, http://arxiv.org/abs/1511.08228[4]		* Neural GPUs learn algorithms, http://arxiv.org/abs/1511.08228[4]

Gisely: Created page with "Post ideas about interesting papers to read below. I ==Spring 2016== Ideas from the Nando Fretas AMA: * Teaching machines to read and comprehend, http://arxiv.org/abs/1506...."

2015-12-27T07:09:48Z

Created page with "Post ideas about interesting papers to read below. I ==Spring 2016== Ideas from the Nando Fretas AMA: * Teaching machines to read and comprehend, http://arxiv.org/abs/1506...."

New page

Post ideas about interesting papers to read below. I

==Spring 2016==

Ideas from the Nando Fretas AMA:

* Teaching machines to read and comprehend, http://arxiv.org/abs/1506.03340[1] (can someone help me with how to add links to this properly???)
* Spatial transformer networks, http://arxiv.org/abs/1506.02025[2]
* Pointer networks, http://arxiv.org/abs/1506.03134[3]
* Neural GPUs learn algorithms, http://arxiv.org/abs/1511.08228[4]
* Learning to see by moving, http://arxiv.org/abs/1505.01596[5]
* Unitary evolution recurrent neural networks http://arxiv.org/abs/1511.06464[6]
* Action-Conditional Video Prediction using Deep Networks in Atari Games, http://arxiv.org/abs/1507.08750[7]
* Deep Reinforcement Learning with Double Q-learning, http://arxiv.org/abs/1509.06461[8]
* Towards Trainable Media: Using Waves for Neural Network-Style Training, http://arxiv.org/abs/1510.03776[9]
* Weakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis, http://www-personal.umich.edu/~reedscot/nips15_rotator_final.pdf[10]
* Hippocampal place cells construct reward related sequences through unexplored space
* Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images, http://arxiv.org/abs/1506.07365[11]