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Swaroop Guntupalli
@
swaroopgj
Hanover, NH & Stanford, CA
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Neuroscientist
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44
Tweetovi
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133
Pratim
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121
Osobe koje vas prate
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Vicarious
@vicariousai
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7. pro |
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You'd think a generative model should answer different queries but deep learning models like VAEs are trained to answer just one type of query! We introduce a method called query-training that creates an inference network that can answer novel query types. openreview.net/pdf?id=rJeoKJ3… pic.twitter.com/SVR0kxpwQJ
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Michael Hanke
@eknahm
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7. pro |
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How is it even possible, now that "AI has taken over", that I buy a washing machine and everything starts recommending me more washing machines?
(don't get me wrong: it is a comforting feeling that, outside the movies, not much has happened since Eliza)
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Vicarious
@vicariousai
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6. pro |
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We show that a singular principle of variable-order sequence learning in a structured model can explain cognitive maps phenom: spatial maps, transitive inference, temporal order, hierarchical planning, remapping etc. -- all important for AI & neuroscience. biorxiv.org/content/10.110… pic.twitter.com/k8guF87lEB
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Michael Hanke
@eknahm
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6. pro |
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Great interview, in which the man @yarikoptic himself explains how he manages to get an infinite amount of work done in the same finite time that the rest of us have ;-) thx @vsoch ! twitter.com/vsoch/status/1…
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Dileep George
@dileeplearning
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13. ruj |
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It also learns a lossy compressed graph representation of the underlying sequences. Closely related to our 1st paper on cognitive maps as higher-order sequences. @rvrikhye @swaroopgj #CCN2019 twitter.com/vicariousai/st…
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Swaroop Guntupalli
@swaroopgj
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5. ruj |
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Insightful commentary from @dileeplearning arxiv.org/abs/1909.01561
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Vicarious
@vicariousai
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27. kol |
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Our #CCN2019 paper proposes a new model for cognitive maps in the hippocampus which involves learning higher-order graphs of event sequences. It can explain flexible planning, route-dependence of place cells, and subsumes successor representations. biorxiv.org/content/10.110… pic.twitter.com/KASM6l6RBK
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Surya Ganguli
@SuryaGanguli
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18. srp |
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1/ New in @sciencemagazine w/ @KarlDeisseroth lab: science.sciencemag.org/content/early/…: new opsin + multi-photon holography to image ~4000 cells in 3D volumes over 5 cortical layers while also stimulating ~50 neurons to directly drive visual percepts; data analysis and theory reveal… pic.twitter.com/Wy1PLvrzz6
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Dileep George
@dileeplearning
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7. svi |
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Thread about our new paper on learning the structure of variable order sequences. Imposing a biologically-inspired sparsity structure alleviates the credit diffusion problem in HMMs! Also relevant for neuroscience... (1) @vicariousai arxiv.org/abs/1905.00507 pic.twitter.com/K2iXJmM6XW
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Vicarious
@vicariousai
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6. svi |
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If yuor laugnage moedl cnnaot raed tihs, it mgiht not be vrey dnagorues 😜. See our paper on learning graphs that can model higher-order sequences and deal with uncertainty. arxiv.org/abs/1905.00507 pic.twitter.com/b1bHNZ7Om4
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Vicarious
@vicariousai
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22. sij 2019. |
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A thought is a program! Check out our blog to see how we are building a cognitive architecture towards AGI. Also included: code, data, and a free link to our recent Science Robotics paper on concept learning. vicarious.com/2019/01/18/a-t…
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Dileep George
@dileeplearning
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17. sij 2019. |
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Mini thread about the cognitive science and neuroscience inspirations behind our new paper in which we learn concepts as 'cognitive programs' on a 'visual cognitive computer'. robotics.sciencemag.org/content/4/26/e… pic.twitter.com/HxIH3ZQdPE
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News from Science
@NewsfromScience
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16. sij 2019. |
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Instead of relying on a list or rules, a new computational framework for learning lets robots come up with their own concepts by detecting abstract differences in images and then recreating them in real life: bit.ly/2RyGw9c pic.twitter.com/pu8nwPnh7S
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Vicarious
@vicariousai
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16. sij 2019. |
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Our concept-learning work is now published in Science Robotics! Checkout this very nice video that Science made to explain the paper. twitter.com/NewsfromScienc…
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Vicarious
@vicariousai
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10. pro 2018. |
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Our new paper learns concepts as programs on a 'visual cognitive computer', showing zero-shot task-transfer on robots, and strong generalization. We bring cogsci ideas about perceptual symbol systems and image schemas into the realm of machine learning. arxiv.org/abs/1812.02788 pic.twitter.com/wkKrPe8CZA
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Steven Strogatz
@stevenstrogatz
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18. ruj 2018. |
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What a gift! Ramanujan’s papers, now online. twitter.com/TrinCollLibCam…
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Rodney Brooks
@rodneyabrooks
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11. ruj 2018. |
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Moore's law sputtering to an end. Now is the time for new architectures and creativity! spectrum.ieee.org/nanoclast/semi…
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Sam Nastase
@samnastase
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11. ruj 2018. |
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New paper with @mafeilong, @swaroopgj, and @haxbylab! Hyperalignment factors out idiosyncrasies in functional–anatomical correspondence, revealing individual differences in fine-grained functional architecture otherwise obscured by anatomical normalization doi.org/10.1016/j.neur… twitter.com/mafeilong/stat…
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Vicarious
@vicariousai
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7. kol 2018. |
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Checkout our CCN-18 papers! (1) RCN-derived circuits suggest precise functional roles for feed-forward, feedback, and lateral connections, and for the thalamus. (2) RCN can explain multiple visual phenomena including two well-known illusions. #CCN18 #microcircuits pic.twitter.com/HfoKoHIscT
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Vicarious
@vicariousai
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7. kol 2018. |
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Here are the links to our CNN-18 papers:
Cortical Microcircuits from a Generative Vision Model: biorxiv.org/content/early/…
Explaining Visual Cortex Phenomena using Recursive Cortical Network: biorxiv.org/content/early/…
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