Philipp Lang

@philipp_s_lang

I try.

UK
Vrijeme pridruživanja: lipanj 2016.
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  1. 20. stu 2019.

    Literally 90% of what I do as oh I see never mind.

    Literally 90% of what I do as a TMP expert: wtf? wtf? missing disambiguator. wtf? wtf? missing disambiguator. wtf? why does the compiler crash? wtf? wtf? that's not a merge conflict, I need that many angle brackets wtf? damn you alias flaw!!!
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  2. proslijedio/la je Tweet
    20. kol 2019.

    Design with as few moving parts as possible, write as little code as necessary. Raise the bar for code readability and testing. Measure and instrument everything. Own the full product. Know your customer, your ops, and your financials. Cross boundaries and do whatever it takes.

    93 proslijeđena tweeta 337 korisnika označava da im se sviđa
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  3. proslijedio/la je Tweet
    30. svi 2019.

    We published an extended version of our latest work “Parallax: Visualizing and Understanding the Semantics of Embedding Spaces via Algebraic Formulae” that got accepted at ACL on arXiv: The tool it describes is also available:

    23 proslijeđena tweeta 93 korisnika označavaju da im se sviđa
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  4. 28. svi 2019.

    More and more shops are ditching calibration in favor of ensemble generation and subsequent ruling out of non-behavioral models - if someone picks this up there might be a winning combination here.

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  5. 28. svi 2019.

    When domain knowledge and ML expertise meet: innovative approach by

    Very excited to share our most recent work "DeepFlow: History Matching in the Space of Generative Networks" We couple a GAN with a differentiable simulator to perform history matching in latent space. 🎇 1/n ➡️ Paper: ➡️ Code:
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  6. 2. tra 2019.

    Very relevant for numerical algorithms where every bit of improved readability matters

    Adding logging code for observability often results in low-level code adding cruft to core logic. describes a better pattern
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  7. proslijedio/la je Tweet
    2. tra 2019.

    We’re open sourcing PyTorch-BigGraph, a tool that makes it much faster and easier to produce graph embeddings for extremely large graphs. Quickly produce high-quality embeddings without specialized computing resources like GPUs or huge amounts of memory.

    9 replies 425 proslijeđenih tweetova 1.259 korisnika označava da im se sviđa
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  8. proslijedio/la je Tweet
    30. ožu 2019.

    Augmenting Physical Simulators with Stochastic Neural Networks: Case Study of Planar Pushing and Bouncing

    1 reply 6 proslijeđenih tweetova 29 korisnika označava da im se sviđa
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  9. proslijedio/la je Tweet
    14. velj 2019.

    Selling complexity will eventually cost you.

    18 replies 119 proslijeđenih tweetova 449 korisnika označava da im se sviđa
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  10. proslijedio/la je Tweet
    27. sij 2019.

    Group 16 in QTR have assembled a supermarket checkout system that automatically scans your items! Made with help from

    1 proslijeđeni tweet 2 korisnika označavaju da im se sviđa
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  11. proslijedio/la je Tweet
    27. sij 2019.

    Group 91 in JCR have built an augmented reality app to visualise animations in textbooks!

    1 proslijeđeni tweet 5 korisnika označava da im se sviđa
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  12. proslijedio/la je Tweet
    25. sij 2019.

    We've been hard at work moving everything out of storage in preparation for tomorrow...

    1 proslijeđeni tweet 2 korisnika označavaju da im se sviđa
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  13. proslijedio/la je Tweet
    24. sij 2019.

    Moving on to our Silver sponsors: we're very excited to be hosting Schlumberger, , , , and . Find out more about all of them here:

    4 proslijeđena tweeta 4 korisnika označavaju da im se sviđa
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  14. 5. sij 2019.

    IMHO unix-like pipes are the one design principle for data analytics applications. Really nice summary here from a pattern perspective.

    A great article from I like to share with developers when they are learning about iteration pattens on collections. Also a good reference when learning new languages/libraries where the operation names may be different.
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  15. proslijedio/la je Tweet
    4. sij 2019.

    "patterns"

    1 proslijeđeni tweet 3 korisnika označavaju da im se sviđa
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  16. proslijedio/la je Tweet
    7. pro 2018.

    Jay Thiagarajan’s perspective on ML for scientific data ( )

    3 proslijeđena tweeta 5 korisnika označava da im se sviđa
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  17. proslijedio/la je Tweet
    12. pro 2018.

    DGL (Deep Graph Library) - Clean and efficient library to build graph neural networks including GCN, TreeLSTM and graph generative models. Includes auto-batching and other tricks for speed.

    296 proslijeđenih tweetova 804 korisnika označavaju da im se sviđa
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  18. proslijedio/la je Tweet
    9. lis 2018.

    Physics Guided Recurrent Neural Networks For Modeling Dynamical Systems: Application to M...

    8 proslijeđenih tweetova 23 korisnika označavaju da im se sviđa
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  19. proslijedio/la je Tweet
    8. lis 2018.

    new post: bpftrace (DTrace 2.0) for Linux 2018. Repo just made public!

    13 replies 491 proslijeđeni tweet 865 korisnika označava da im se sviđa
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  20. proslijedio/la je Tweet
    5. lis 2018.

    This is important. Deep Learning Just Dipped into Exascale Territory. Barring any surprises this looks like this year's Gordon Bell Prize to us... -

    6 proslijeđenih tweetova 14 korisnika označava da im se sviđa
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