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Prikvačeni tweet
New paper out on
#NeurIPS2019: “From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction” with fantastic collaborators@aran_nayebi,@niru_m,@lmcintosh, Stephen Baccus,@SuryaGanguli. https://papers.nips.cc/paper/9060-from-deep-learning-to-mechanistic-understanding-in-neuroscience-the-structure-of-retinal-prediction …pic.twitter.com/FvLyTmbZtY
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Will be presenting our work this morning: Please drop by if you are at
#NeurIPS2019 10:45 AM—12:45 PM poster #152 https://twitter.com/Hidenori8Tanaka/status/1197624452281798656 …pic.twitter.com/aCLu5pLxKi
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Hidenori Tanaka proslijedio/la je Tweet
Deep learning achieved great success in modeling sensory processing. However, such models raise questions about the very nature of explanation in neuroscience. Are we simply replacing one complex system (biological circuit) with another (a deep net), without understanding either? https://twitter.com/Hidenori8Tanaka/status/1197624452281798656 …pic.twitter.com/9DK4BpImr8
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Hidenori Tanaka proslijedio/la je Tweet
Announcing our PHI lab’s collaboration with
@Caltech,@Cornell,@UMich,@MIT,@NASA,@Stanford,@Swinburne, and@1QB_IT. https://ntt-research.com/news/ntt-research-to-work-with-caltech-cornell-michigan-mit-nasa-stanford-swinburne-and-1qbit/ …#UpgradeReality@GlobalNTT@NTTPRHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Hidenori Tanaka proslijedio/la je Tweet
Can model reduction with deep learning produce a meaningful causal understanding of the retina? https://buff.ly/2r7v73V
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Hidenori Tanaka proslijedio/la je Tweet
When the system encourages most researchers to optimize for depth, there will be vast opportunities for the minority of researchers who are in a position to optimize for breadth.https://twitter.com/KordingLab/status/1189904873967345664 …
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Hidenori Tanaka proslijedio/la je Tweet
1/ SciTwitter: I'm very excited to share our new Perspective article out in Nature Neuroscience today!https://www.nature.com/articles/s41593-019-0520-2 …
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Quantum supremacy using a programmable superconducting processor https://www.nature.com/articles/s41586-019-1666-5#Ack1 …
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Hidenori Tanaka proslijedio/la je Tweet
Excited to announce our new paper Emergent properties of the local geometry of neural loss landscapes https://arxiv.org/abs/1910.05929 with my great advisor
@SuryaGanguli! We used a simple model to explain 4 surprising effects of local geometry of neural network landscapes.pic.twitter.com/pz37q2DU7L
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Hidenori Tanaka proslijedio/la je Tweet
What does deep learning bring to neuroscience? What is the role of theory in the age of deep learning? Answers (and more questions) in our workshop "Brain Against the Machine", Berlin, Sep 17&18 at
#BernsteinConference. Full schedule: https://www.bernstein-network.de/en/bernstein-conference/2019/satellite-workshops/brain-against-the-machine-266b-and-now-you-do-what-they-told-ya-266b …@NNCN_Germanypic.twitter.com/pXTJ7I7PAB
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Hidenori Tanaka proslijedio/la je Tweet
The last few months I have been at
@MILAMontreal working with@tangjianpku and Yoshua on problems in physics and machine learning. Riding the success of generative models in drug discovery, we encode and decode 3-D representations of crystal structures. https://arxiv.org/abs/1909.00949 pic.twitter.com/xj0hG1hqgpPrikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Hidenori Tanaka proslijedio/la je Tweet
New research shows how
#machinelearning can improve high-performance computing for solving partial differential equations, with potential applications that range from modeling#climatechange to simulating fusion reactions. Learn all about it here ↓https://goo.gle/2Grn9rjHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Hidenori Tanaka proslijedio/la je Tweet
#tweeprint Universality and individuality in neural dynamics across large populations of recurrent networks https://arxiv.org/abs/1907.08549 . With fantastic collaborators@niru_m,@ItsNeuronal,@MattGolub_Neuro,@SuryaGanguli.Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Hidenori Tanaka proslijedio/la je Tweet
1/ New in
@sciencemagazine w/@KarlDeisseroth lab: https://science.sciencemag.org/content/early/2019/07/17/science.aaw5202 …: 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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Hidenori Tanaka proslijedio/la je Tweet
Insects lay eggs of all shapes and sizes! This makes them a great system for exploring the way shape and size evolve. This non-technical thread highlights the findings of our new paper in
@nature by@shchurch@brunoasm@redmakeda and me. https://www.nature.com/articles/s41586-019-1302-4 …pic.twitter.com/e717I8wzbS
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Hidenori Tanaka proslijedio/la je Tweet
New work out on arXiv! Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics (https://arxiv.org/abs/1906.10720 ), with fantastic co-authors
@ItsNeuronal,@MattGolub_Neuro,@SuryaGanguli and@SussilloDavid.#tweetprint summary below!
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Link to the paper posted on my website. (Thankfully, Physical Review allows this!)https://drive.google.com/file/d/1nyBb2PmiEyRSO34qrMBjMkXi7w9OgBm7/view …
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Is there a theoretical analogy between ring attractor neural networks and Anderson localization in quantum systems? With David Nelson, we discovered a new class of random matrices whose eigenvectors are quasi-localized even with fully dense connections.https://twitter.com/PhysRevE/status/1139240020802449408 …
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Hidenori Tanaka proslijedio/la je Tweet
The schedule and papers for our ICML workshop "Theoretical Physics for Deep Learning" on Friday are now updated https://sites.google.com/corp/view/icml2019phys4dl/ …. We are looking forward to the discussions and hope you find the workshop fruitful!
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Hidenori Tanaka proslijedio/la je Tweet
Single-trial variability in spike timing, even in small amounts, can mask striking firing patterns. The raster plots below show the same data (a neuron from rat motor cortex). The only difference is that we’ve re-sorted the trial ids with the help of a simple model.pic.twitter.com/mnskISaETS
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