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Tweets by ‎@mind_codec

  1. MindCodec Retweeted
    Brancher @pybrancher

    We are currently researching new methods for constructing variational distributions automatically (beyond the standard mean field approach). If you know some literature about this topic please let us know! (And retweet if you have followers who could know!)

  2. MindCodec Retweeted
    Ferenc Huszár🇪🇺 @fhuszar

    Is there a comprehensive review on the effect of SGD batchsize on generalization?

    I've seen papers arguing that large batch GD does not generalise, and also papers that argue the opposite or say the generalisation gap is not all that bad for large-batch SGD. So which one is it?

  3. MindCodec Retweeted
    Sander Bosch @bosch_se

    A new @artcogsys preprint! We present DeepRF: ultrafast population receptive field (pRF) mapping with .
    With similar performance in a fraction of the time, it enables modeling of more complex pRF models, resolving an important limitation of the conventional method. https://twitter.com/biorxivpreprint/status/1161307573146005504

  4. MindCodec Retweeted
    Tim Kietzmann @TimKietzmann

    Thrilled to announce that I just accepted an offer from the @DondersInst @AI_Radboud! I will join their faculty as Assistant Professor this fall.

    Get in touch if you would like to work with me in the area of (visual) computational neuroscience and machine learning!

  5. MindCodec Retweeted
    Brancher @pybrancher

    Check-out TaylorAlgebra. A small package for symbolic Taylor expansions. We are soon going to support and use it for perturbative machine learning in Brancher. https://github.com/LucaAmbrogioni/TaylorAlgebra …

  6. MindCodec Retweeted
    Brancher @pybrancher

    We are happy to share Brancher .35 with dedicated support for stochastic processes, infinite models and timeseries analysis. Try this on our new tutorial! https://colab.research.google.com/drive/1dqc-5DqJOrNw6CR96CDrarrKQ2KSabDH …

    If you like our work, please share it and star us on Github! :) https://github.com/AI-DI/Brancher 

  7. MindCodec Retweeted
    Artificial Cognitive Systems @artcogsys

    We are happy to share our latest work on generative modeling. k-GANs is a generalization of the well-known k-means algorithm in which every cluster is represented by a generative adversarial network. The mode collapse phenomenon is greatly reduced! https://arxiv.org/abs/1907.04050 

  8. MindCodec Retweeted
    Brancher @pybrancher

    Coming soon on Brancher: 1) A dedicated interface for stochastic processes and timeseries analysis, 2) automatic construction of variational distributions. https://github.com/AI-DI/Brancher  https://brancher.org/ 

  9. MindCodec Retweeted
    Artificial Cognitive Systems @artcogsys

    Check out our new preprint: a large fMRI dataset with ~23 hours of video stimuli (Dr Who)! https://twitter.com/biorxiv_neursci/status/1146083491664269314

  10. MindCodec Retweeted
    StackShare @stackshareio

    🗞 ICYMI (weekly newsletter)

    🛠 New Tools

    1. @OttomaticaLLC slim
    2. @pybrancher
    3. Entropic
    4. NeuronBlocks by @msdev
    5. CJSS by @xsanda

    Subscribe 👉 http://bit.ly/2PQYBKQ 

  11. MindCodec Retweeted
    Brancher @pybrancher

    Check out our new probabilistic deep learning tutorial:https://colab.research.google.com/drive/1YNwZpJgrsicK3Pz8igAktbdocm-gA9mV …

    You will learn how to build deep probabilistic models using Brancher and how to integrate existing networks in your Brancher models.

  12. MindCodec Retweeted
    Kriegeskorte Lab @KriegeskorteLab

    this effect is amazingly strong and continues to intrigue... https://twitter.com/DannyDutch/status/1142068351855943683

  13. MindCodec Retweeted
    Donders Institute BICGroup @DBicgroup

    Happy to announce that the paper "Computational Resource Demands of a Predictive Bayesian Brain" by @IrisVanRooij and @JohanKwisthout is online now in @CompBrainBeh and freely accessible: https://link.springer.com/article/10.1007%2Fs42113-019-00032-3 … 1/7

    Computational Resource Demands of a Predictive Bayesian Brain

    There is a growing body of evidence that the human brain may be organized according to principles of predictive processing. An important conjecture in neuroscience is that a brain organized in this...

    link.springer.com
  14. MindCodec Retweeted
    Sébastien Fréal-Saison @sfrealsaison

    Check GitHub - ⁦@pybrancher⁩ A user-centered package for differentiable probabilistic inference powered by @PyTorch. Example tutorials: TimeSeries Analysis / autocorrelation, Bayesian inference ... https://github.com/AI-DI/Brancher 

    AI-DI/Brancher

    A user-centered Python package for differentiable probabilistic inference - AI-DI/Brancher

    github.com
  15. MindCodec Retweeted
    Brancher @pybrancher

    1/3: Thank you for your interest and support! We are soon going to share a roadmap that will outline our plans for future developments. Among other things, we are going to support symbolic/analytic computations and discrete latent variables.

  16. MindCodec Retweeted
    Brancher @pybrancher

    We are excited to announce the release of Brancher, a module for deep probabilistic inference powered by @PyTorch: http://brancher.org 

    Check out our tutorials in @GoogleColab here: https://brancher.org/#examples  https://brancher.org/#tutorials 

  17. MindCodec @mind_codec

    Our latest paper on stochastic optimization is out! We use Taylor expansion and a pinch of complex analysis to get low variance gradient estimators for models with either discrete or continuous latent variables. https://arxiv.org/abs/1904.00469 

  18. MindCodec Retweeted
    Ferenc Huszár🇪🇺 @fhuszar

    highlight (jetlag special): A Contrastive Divergence for Combining Variational Inference and MCMChttps://www.inference.vc/icml-highlight-contrastive-divergence-for-variational-inference-and-mcmc/ …

    ICML Highlight: Contrastive Divergence for Combining Variational Inference and MCMC

    Welcome to my ICML 2019 jetlag special - because what else do you do when you wake up earlier than anyone than write a blog post. Here's a paper that was presented yesterday which I really liked.Ruiz...

    inference.vc
  19. MindCodec Retweeted
    Miles Brundage @Miles_Brundage

    StyleGAN trained on abstract art!

    Some of the training images are zoomed-in photos/digital art, and others are zoomed-out photos including the picture frame - hence the zooming/matting effect. Also, the data (from Flickr) includes some DeepDream samples, which it picked up on.

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  20. MindCodec Retweeted
    Ferenc Huszár🇪🇺 @fhuszar

    Perceptual Straightening of Natural Videos

    a short post on a recent paper from Eero Simoncelli's group.https://www.inference.vc/perceptual-streightening-of-natural-videos/ …

    Perceptual Straightening of Natural Videos

    Video is an interesting domain for unsupervised, or self-supervised, representation learning. But we still don't know what type of inductive biases will enable us to best exploit the information...

    inference.vc