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Guy Gur-Ari is explaining how Feynman Diagrams can be used as a tool to simplify calculations needed to understand asymptotics of neural networks.
#NeurIPS2019pic.twitter.com/XXyd3OiWL5
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Come to our poster on "Observational Overfitting in Reinforcement Learning" at
#NeurIPS2019 "Optimization Foundations of Reinforcement Learning" and "Science Meets Engineering of Deep Learning" workshops. Joint work w Xingyou Song@yidingjiang & Yilun Du https://arxiv.org/abs/1912.02975 pic.twitter.com/usIHPGVI5f
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Joint work with
@yidingjiang@TheGradient@dilipkay and Samy BengioPrikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Come to our poster on "The intriguing role of module criticality in the generalization of deep networks" at
#NeurIPS2019 workshops "ML with Guarantees" and "Science Meets Engineering of DL" with w/ Niladri Chatterji and@HanieSedghipic.twitter.com/mNuD6Xrs3x
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Come to our poster on "Fantastic Generalization Measures and Where To Find Them" at
#NeurIPS2019 workshops "ML with Guarantees" and "Science Meets Engineering of DL".@yidingjiang will also give a spotlight talk at 5:40pm in "Science Meets Engineering of DL" workshop.pic.twitter.com/GF48nwytFB
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Will be attending
#NeurIPS2019! Ping me if you want to chat!Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Well-deserved! Congrats
@_vaishnavh and@zicokolter!https://twitter.com/_vaishnavh/status/1203839716899966977 …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Behnam Neyshabur proslijedio/la je Tweet
How does transfer learning for medical imaging affect performance, representations and convergence? Check out the blogpost below and our
#NeurIPS2019 paper https://arxiv.org/abs/1902.07208 for some of the surprising conclusions, new approaches and open questions!https://twitter.com/GoogleAI/status/1203026419883732992 …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Behnam Neyshabur proslijedio/la je Tweet
Fantastic Generalization Measures and Where to Find Them https://deepai.org/publication/fantastic-generalization-measures-and-where-to-find-them … by Yiding Jiang et al. including
@bneyshabur#Hyperparameter#ComputerScienceHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Behnam Neyshabur proslijedio/la je Tweet
Fantastic Generalization Measures and Where to Find Them “We present the first large scale study of generalization in deep networks. We train over 10,000 convolutional networks by systematically varying commonly used hyperparameters.” https://arxiv.org/abs/1912.02178 https://twitter.com/TheGradient/status/1202404270701600769 …pic.twitter.com/b0FAhjAReX
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Behnam Neyshabur proslijedio/la je Tweet
Been waiting for this paper to drop. It's here. I've got my NeurIPS flight reading sorted out. I think this is an important step towards gaining clarity on what it might mean to "explain generalization".https://twitter.com/TheGradient/status/1202404270701600769 …
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Behnam Neyshabur proslijedio/la je Tweet
One of the most comprehensive studies of generalization to date; ≈40 complexity measures over ≈10K deep models. Surprising observations worthy of further investigations. Fantastic Generalization Measures: https://bit.ly/34TqKZs w
@yidingjiang@bneyshabur@dilipkay S. Bengiopic.twitter.com/POG4DoNaAU
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Behnam Neyshabur proslijedio/la je Tweet
Excited to share our latest work on generalization in DL https://arxiv.org/abs/1912.00528 w/ Niladri Chatterji &
@bneyshabur We study the phenomenon that some modules of DNNs are more critical than others: rewinding their values back to initialization, strongly harms performance.(1/3)Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
The shape of the valleys that connect the initial and final parameter values of modules (eg. conv module) can tell you a lot about why some architectures generalize better! See our recent work w/ Niladri Chatterji &
@HanieSedghi : http://arxiv.org/abs/1912.00528 pic.twitter.com/v9aN5gIMyG
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Behnam Neyshabur proslijedio/la je Tweet
1/3 If you study dynamics of gradient descent, what properties of trajectory would be most useful for your research? Currently DEMOGEN https://bit.ly/2J6LHpW (dataset of 756 trained models) has final weights, but we plan to extend and include information on intermediate weights.
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Behnam Neyshabur proslijedio/la je Tweet
1/3 DEMOGEN is a dataset of 756 CNN/ResNet-32 models trained on CIFAR-10/100 w/ various regularization and hyperparameters, leading to wide range of generalization behaviors. Hope dataset can help the community w/ exploring generalization in
#deeplearninghttp://bit.ly/2L7R7n4Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Behnam Neyshabur proslijedio/la je Tweet
Nati Srebro giving an exciting talk about what are NOT the reasons for neural networks working well at
#DeepPhenomena workshop#icml19pic.twitter.com/ikM4GctSUf
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Organized with
@HanieSedghi@alirahimi0@aleks_madry@maithra_raghu@arimorcos@lschmidt3@kenjihata and Ying Xiao.Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Have you seen something interesting or curious or mysterious while training a deep neural network? Share these interesting and unusual deep learning phenomena here: https://deep-phenomena.web.app/
#icml2019Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Come to our
#icml2019 workshop on Identifying and Understanding Deep Learning Phenomena tomorrow! We have many super exciting talks tomorrow! See our schedule here: http://deep-phenomena.orgPrikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
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