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fly51fly proslijedio/la je Tweet
Why yes, we did just train CIFAR10 ~2000% faster than the previous best on DAWNBench, using fastai and
@pytorch. :) Blog post on how we did it coming soon - but first we have some more experiments to run... https://dawn.cs.stanford.edu/benchmark/#cifar10-train-time …pic.twitter.com/iBhH7N7RDX
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fly51fly proslijedio/la je Tweet
Yet another paper using compression to get generalization bounds (on large neural networks). One difference, this paper does the hard work to actually turn them into nonvacuous bounds. I think this step is important. https://arxiv.org/abs/1804.05862
@ryan_p_adamspic.twitter.com/4ztLuly5A4
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The integration of TensorFlow with
@nvidia's TensorRT optimizes neural network models & speeds up inference across GPU-accelerated platforms. Users can expect high inference performance plus a near transparent workflow Learn more on the TensorFlow blog ↓https://goo.gl/m8qG44Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
fly51fly proslijedio/la je Tweet
Nice work from OpenAI on evolving loss functions to quickly master new tasks: https://blog.openai.com/evolved-policy-gradients/ …pic.twitter.com/81YrU2VJMG
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fly51fly proslijedio/la je Tweet
Fantastic new paper from FAIR folks: Mechanical Turker Descent or a procedure to collect high quality annotations from multiple Turkers. https://arxiv.org/abs/1711.07950 pic.twitter.com/pTa06VTbQU
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fly51fly proslijedio/la je Tweet
Self-driving cars are often used an example of how adversarial attacks can do harm in the real world. In our new preprint, @samfin55,
@zakkohane, and I argue that medicine is the perfect storm of incentive + opportunity for adversarial attacks: https://arxiv.org/abs/1804.05296 pic.twitter.com/7Jp70Lm1td
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fly51fly proslijedio/la je Tweet
Conda vs Pip: Myths and Misconceptions By
@jakevdp https://jakevdp.github.io/blog/2016/08/25/conda-myths-and-misconceptions/ …#PythonHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
fly51fly proslijedio/la je Tweet
“This result leads to a new way of initializing gate biases in LSTMs and GRUs. This new ‘Chrono Initialization’ is shown to greatly improve learning of long term dependencies, with minimal implementation effort.”https://twitter.com/hillbig/status/986044682013630464 …
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fly51fly proslijedio/la je Tweet
I am a huge fan of any attempts to SIMPLIFY existing approaches. We need more of this, rather than the overfitting circus that seems to be the norm. The unreasonable effectiveness of the forget gate https://arxiv.org/abs/1804.04849
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fly51fly proslijedio/la je Tweet
This week's
#KernelAwards winner looks at uses decision trees to study dementia outcomes https://www.kaggle.com/ruslankl/dementia-prediction-w-tree-based-models …pic.twitter.com/9rr0cyEoJq
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fly51fly proslijedio/la je Tweet
RNN's gating mechanisms can be derived from the invariance to general time transformations. Based on this insight they proposed a new initialization "chrono initialization" that adjust the biases of gates, that can deal with long-term dependencies.https://openreview.net/forum?id=SJcKhk-Ab …
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fly51fly proslijedio/la je Tweet
Released Chainer/CuPy v4.0.0!
#Chainer: Major performance improvements including TensorCore support and iDeep backend, NCCL2 support, Caffe export.#CuPy: CUDA 9.1 support, wheel package, FFT support, etc. More in the blog post and release notes.https://chainer.org/announcement/2018/04/17/released-v4.html …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
fly51fly proslijedio/la je Tweet
Causes of death - Reality vs. Google vs. Media.
#dataviz#health https://www.reddit.com/r/dataisbeautiful/comments/8cwcbu/cause_of_death_reality_vs_google_vs_media_oc/ …pic.twitter.com/gXZXaZZPuEHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
fly51fly proslijedio/la je Tweet
Summary of winning solution for
@kaggle Data Science Bowl 2018 https://www.kaggle.com/c/data-science-bowl-2018/discussion/54741 … - modified U-Net > Mask-RCNN - add borders btw cells as targets - heavy augmentations - deep encoders: DPN-92, Resnet-152, InceptionResnetV2 - watershed + morphology for postprocessing#dsb2018pic.twitter.com/L6deQg5dPU
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fly51fly proslijedio/la je Tweet
A step-by-step walkthrough (with Keras implementation) of
@hardmaru & Juergen's "World Models" paper:https://medium.com/applied-data-science/how-to-build-your-own-world-model-using-python-and-keras-64fb388ba459 …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
fly51fly proslijedio/la je Tweet
PlaneNet: Piece-wise Planar Reconstruction from a Single RGB Image https://eng.ucmerced.edu/people/jyang44/papers/cvpr2018_PlaneNet_camera_ready.pdf …
#computervision#robotics#CVPR2018pic.twitter.com/29LDTUS9cl
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fly51fly proslijedio/la je Tweet
The unreasonable effectiveness of the forget gate: they stripped LSTM of all but the forget gate https://arxiv.org/pdf/1804.04849.pdf …
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fly51fly proslijedio/la je Tweet
Much like boolean algebra isn't fundamental to computing & programming in an abstract sense, but rather is an arbitrary low-level design choice, I don't think brains are fundamental to intelligence. I'd posit the most compact substrate for defining cognition is mathematics.
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fly51fly proslijedio/la je Tweet
The major finding is: ”The forecasting accuracy of ML forecasting methods is lower than the worst of statistical ones while the accuracy of more than half the ML methods is lower than a random walk”. http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0194889 …pic.twitter.com/a3uCiM7yDO
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fly51fly proslijedio/la je Tweet
MUNIT: Multimodal unsupervised image-to-image translation. Learn to translate one input dog image to a distribution of cat images without paired training data. paper: https://arxiv.org/abs/1804.04732 code: https://github.com/nvlabs/munit by
@xunhuang1995@SergeBelongie@jankautz#DeepLearningpic.twitter.com/i3gUhCEbWMPrikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
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