https://rctn.org/w/index.php?title=TCN_Paper_Ideas&feed=atom&action=history
TCN Paper Ideas - Revision history
2024-03-29T13:53:10Z
Revision history for this page on the wiki
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https://rctn.org/w/index.php?title=TCN_Paper_Ideas&diff=8441&oldid=prev
Gisely: /* Spring 2016 */
2016-01-08T21:02:59Z
<p><span dir="auto"><span class="autocomment">Spring 2016</span></span></p>
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 20:54, 8 January 2016</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* [http://www.ncbi.nlm.nih.gov/pubmed/19346478 Learning reward timing in cortex through reward dependent expression of synaptic plasticity]</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* [http://www.ncbi.nlm.nih.gov/pubmed/19346478 Learning reward timing in cortex through reward dependent expression of synaptic plasticity]</div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">* [http://www.cell.com/cell/abstract/S0092-8674%2815%2900973-3 Central Cholinergic Neurons Are Rapidly Recruited by Reinforcement Feedback]</ins></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">* [http://www.sciencedirect.com/science/article/pii/S0960982215004790 Selective Activation of a Putative Reinforcement Signal Conditions Cued Interval Timing in Primary Visual Cortex]</ins></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">* [http://www.sciencedirect.com/science/article/pii/S0896627305003624 Uncertainty, Neuromodulation, and Attention]</ins></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">* [http://www.gatsby.ucl.ac.uk/~dayan/papers/25lessons.pdf Twenty-Five Lessons from Computational Neuromodulation]</ins></div></td></tr>
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Gisely
https://rctn.org/w/index.php?title=TCN_Paper_Ideas&diff=8440&oldid=prev
Gisely: /* Spring 2016 */
2016-01-08T20:59:52Z
<p><span dir="auto"><span class="autocomment">Spring 2016</span></span></p>
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 20:51, 8 January 2016</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>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.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>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.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>* [<del style="font-weight: bold; text-decoration: none;">Learning reward timing in cortex through reward dependent expression of synaptic plasticity | </del>http://www.ncbi.nlm.nih.gov/pubmed/19346478]</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>* [http://www.ncbi.nlm.nih.gov/pubmed/19346478 <ins style="font-weight: bold; text-decoration: none;">Learning reward timing in cortex through reward dependent expression of synaptic plasticity</ins>]</div></td></tr>
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Gisely
https://rctn.org/w/index.php?title=TCN_Paper_Ideas&diff=8439&oldid=prev
Gisely: /* Spring 2016 */
2016-01-08T20:59:13Z
<p><span dir="auto"><span class="autocomment">Spring 2016</span></span></p>
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 20:51, 8 January 2016</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>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.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>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.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>* Learning reward timing in cortex through reward dependent expression of synaptic plasticity</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>* <ins style="font-weight: bold; text-decoration: none;">[</ins>Learning reward timing in cortex through reward dependent expression of synaptic plasticity <ins style="font-weight: bold; text-decoration: none;">| http://www.ncbi.nlm.nih.gov/pubmed/19346478]</ins></div></td></tr>
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Gisely
https://rctn.org/w/index.php?title=TCN_Paper_Ideas&diff=8438&oldid=prev
Gisely: /* Spring 2016 */
2016-01-08T20:57:30Z
<p><span dir="auto"><span class="autocomment">Spring 2016</span></span></p>
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 20:49, 8 January 2016</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Hippocampal place cells construct reward related sequences through unexplored space</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Hippocampal place cells construct reward related sequences through unexplored space</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images, http://arxiv.org/abs/1506.07365[11]</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images, http://arxiv.org/abs/1506.07365[11]</div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"></ins></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">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.</ins></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"></ins></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">* Learning reward timing in cortex through reward dependent expression of synaptic plasticity</ins></div></td></tr>
</table>
Gisely
https://rctn.org/w/index.php?title=TCN_Paper_Ideas&diff=8435&oldid=prev
Gisely: /* Spring 2016 */
2015-12-27T07:10:17Z
<p><span dir="auto"><span class="autocomment">Spring 2016</span></span></p>
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 07:02, 27 December 2015</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Ideas from the Nando Fretas AMA:</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Ideas from the Nando Fretas AMA:</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>* Teaching machines to read and comprehend, http://arxiv.org/abs/1506.03340[1<del style="font-weight: bold; text-decoration: none;">] (can someone help me with how to add links to this properly???)</del></div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>* Teaching machines to read and comprehend, http://arxiv.org/abs/1506.03340[1] </div></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del style="font-weight: bold; text-decoration: none;">* Spatial transformer networks, http://arxiv.org/abs/1506.02025[2</del>]</div></td><td colspan="2" class="diff-side-added"></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Pointer networks, http://arxiv.org/abs/1506.03134[3]</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Pointer networks, http://arxiv.org/abs/1506.03134[3]</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Neural GPUs learn algorithms, http://arxiv.org/abs/1511.08228[4]</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Neural GPUs learn algorithms, http://arxiv.org/abs/1511.08228[4]</div></td></tr>
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Gisely
https://rctn.org/w/index.php?title=TCN_Paper_Ideas&diff=8434&oldid=prev
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
<p>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...."</p>
<p><b>New page</b></p><div>Post ideas about interesting papers to read below. I<br />
<br />
==Spring 2016==<br />
<br />
Ideas from the Nando Fretas AMA:<br />
<br />
* 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???)<br />
* Spatial transformer networks, http://arxiv.org/abs/1506.02025[2]<br />
* Pointer networks, http://arxiv.org/abs/1506.03134[3]<br />
* Neural GPUs learn algorithms, http://arxiv.org/abs/1511.08228[4]<br />
* Learning to see by moving, http://arxiv.org/abs/1505.01596[5]<br />
* Unitary evolution recurrent neural networks http://arxiv.org/abs/1511.06464[6]<br />
* Action-Conditional Video Prediction using Deep Networks in Atari Games, http://arxiv.org/abs/1507.08750[7]<br />
* Deep Reinforcement Learning with Double Q-learning, http://arxiv.org/abs/1509.06461[8]<br />
* Towards Trainable Media: Using Waves for Neural Network-Style Training, http://arxiv.org/abs/1510.03776[9]<br />
* Weakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis, http://www-personal.umich.edu/~reedscot/nips15_rotator_final.pdf[10]<br />
* Hippocampal place cells construct reward related sequences through unexplored space<br />
* Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images, http://arxiv.org/abs/1506.07365[11]</div>
Gisely