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I'm going to give a talk on our recent work "Deep learning for natural image reconstruction from electrocorticography signals" at Workshop on Deep Learning in Bioinformatics, Biomedicine, and Healthcare Informatics (DLB2H), BIBM 2019. http://dataxlab.org/DLB2H/
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A Recipe for Training Neural Networkshttps://www.reddit.com/r/MachineLearning/comments/bhenys/d_a_recipe_for_training_neural_networks/ …
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Want to train your own BigGAN on just 4-8 GPUs? Today we're proud to release BigGAN-PyTorch, a full
@PyTorch reimplementation that uses gradient accumulation to get the benefits of big batches even on small hardware. https://github.com/ajbrock/BigGAN-PyTorch … Repo joint work with @_alexandonianpic.twitter.com/RK5H6EIecQ
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Happy that we could share
#AlphaStar progress with you all! Good Games@LiquidTLO and@Liquid_MaNa, and@Artosis and@RotterdaM08 for a great show! You can see all the details in the blog. https://deepmind.com/blog/alphastar-mastering-real-time-strategy-game-starcraft-ii/ …pic.twitter.com/51EG3fHUL1Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
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Sunday classic paper: Hamming (1986), You and Your Research. Enduringly popular, moving and thought-provoking. I haven't shared it before so thought it would make a good classic reading for a reflective summer Sunday.
http://www.paulgraham.com/hamming.html pic.twitter.com/bHWvvwHTw7
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#ICMLDebates great suggestions to improve rigor in the field.pic.twitter.com/dKw72oSoPu
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The proceedings of GECCO 2018 are now available on-line. Follow the link to know how to access. http://gecco-2018.sigevo.org/index.html/tiki-index.php?page=Proceedings …
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Every Pixel Counts: Unsupervised Geometry Learning with Holistic 3D Motion Understanding. The vision world really loves that Zhou et al paper from last year’s CVPR! https://arxiv.org/abs/1806.10556
#computervision#slam#DeepLearning#roboticspic.twitter.com/v6RaG1jX8f
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Efficient Neural Network Architecture Search, main idea: Softmax over "operations", such as convolution, max pooling, & zero (which means no connection), to make everything differentiable, thus jointly learn architecture & weights via gradient descent. https://arxiv.org/pdf/1806.09055.pdf …pic.twitter.com/UWxrq0oegm
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Meta-learning enables fast learning, but needs hand-engineered meta-training tasks. Can we get the tasks themselves automatically? Our first attempt at this for RL: unsupervised meta-reinforcement learning: https://arxiv.org/abs/1806.04640 w/ Abhishek Gupta, Ben Eysebach,
@chelseabfinnHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
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東京オフィスでAI研究に取組む仲間を募集します!Happy to see our
#GoogleAI efforts expanding w/ Google Brain now having a research presence in Tokyo. We’re hiring machine learning researchers there, if you’re interested in helping advance AI, apply here —> http://goo.gl/4oiVjZHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
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https://icml.cc/Conferences/2018/WorkshopsOverview … is an overview of 2018 ICML Workshops including links to workshop home pages.
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Reptile learns how to learn by adjusting the initial parameters towards the result of multiple SGD updates of each task. This surprisingly simple algorithm has the similar effect as MAML and archives the similar performance.https://blog.openai.com/reptile/
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Our new paper, with
@zicokolter and Vladlen Koltun, on extensively evaluating convolutional vs. recurrent approaches to sequence tasks is on arXiv now! http://arxiv.org/abs/1803.01271Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
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With
@c_tallec, we release pyvarinf, a#Python package for Bayesian#DeepLearning with Variational Inference for@PyTorch ! You can now make any neural network Bayesian in one line of code.http://github.com/ctallec/pyvarinf …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
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A brilliant lecture by Mike Jordan on optimization through the lens of variational analysis and conservation laws, some new developments, and many open problems:https://youtu.be/hl3SrPZ6B-8
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"Advances in Variational Inference" by Zhang et al. (2017) Good review on recent advances in Variational Inference! Contains basics, stochastic VI, black-box VI, and more... https://arxiv.org/abs/1711.05597
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Stochastic Hyperparameter Optimization through Hypernetworks. Use hypernetworks to parametrize a network’s weights as a function of its hyperparams, so SGD can be used directly to optimize for hyperparams on validation set! By
@JonLorraine9@DavidDuvenaud https://arxiv.org/abs/1802.09419 pic.twitter.com/F77JWQTiTR
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Been waiting for something like this - "Continual Lifelong Learning with Neural Networks: A Review," Parisi et al.: https://arxiv.org/abs/1802.07569
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