Melison

@Melison48436887

Major in ML DL and NLP😋.

Vrijeme pridruživanja: travanj 2019.

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  1. proslijedio/la je Tweet
    28. sij

    Open-domain conversation is an extremely difficult task for ML systems. Meena is a research effort at in this area. It's challenging, but we are making progress towards more fluent and sensible conversations. Nice work, Daniel, & everyone involved!

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  2. proslijedio/la je Tweet
    21. sij

    Let me highlight this amazing work I've read recently on in NLP, in which you'll find both: - a deep discussion of what it means for a neural model to be compositional - a deep and insightful comparison of LSTM, ConvNet & Transformers! 👉

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  3. proslijedio/la je Tweet
    30. sij
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  4. 26. sij
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  5. proslijedio/la je Tweet
    19. sij
    Odgovor korisniku/ci
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  6. 26. sij
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  7. 26. sij
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  8. proslijedio/la je Tweet
    26. sij
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  9. proslijedio/la je Tweet
    10. sij

    Very happy to share our latest work accepted at : we prove that a Self-Attention layer can express any CNN layer. 1/5 📄Paper: 🍿Interactive website : 🖥Code: 📝Blog:

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  10. proslijedio/la je Tweet

    If I was 16 again, here's what I'd do: *Learn Copywriting/media buying *Start working out *take up a martial art *learn NLP/hypnosis *get a job *save 50% *work on building a good social circle Focus on MASTERING social, and high-income skills. And, remember Assets > asses

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  11. proslijedio/la je Tweet
    3. lis 2019.

    Excited to see our DistilBERT paper accepted at NeurIPS 2019 ECM^2 wkshp! 40% smaller 60% faster than BERT => 97% of the performance on GLUE w. a triple loss signal 💥We also distilled GPT2 in an 82M params model 📖 Code&weights:

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  12. proslijedio/la je Tweet
    15. lis 2019.

    Rethinking Data Augmentation: Self-Supervision and Self-Distillation by Hankook Lee et al. including

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  13. proslijedio/la je Tweet
    29. ožu 2019.

    Does BERT make BiLSTMs obsolete? Not so fast, say , , et al. You can distill BERT knowledge into BiLSTMs. Gives up some prediction accuracy for 100x fewer parameters and 15x faster inference.

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  14. proslijedio/la je Tweet

    3/ Jiao et al distill BERT to a network that is 7.5X smaller at 9.4X faster with no loss in accuracy[1]. and the team achieve similar results and have open sourced their methods[2]. [1] [2]

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  15. proslijedio/la je Tweet
    2. lis 2019.
    Odgovor korisniku/ci

    Replace the word vectors or the featurizer (e.g., BiLSTM) with BERT in a graph-based parser? Need to pay some attention to wordpiece-to-word mapping though. Plug: we have a graph-based parser in PyTorch at !

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  16. proslijedio/la je Tweet
    14. kol 2019.

    I'll Keep Coming! A Snake, a Bird, and a Little Something Else. All made again using only Procedural Animations.

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  17. proslijedio/la je Tweet
    7. kol 2019.
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  18. proslijedio/la je Tweet
    25. srp 2019.

    SpanBERT: a new pre-training method for better span representation! (w/ ) Big gains on QA, SoTA on Coref and TACRED. Better pre-training tasks and objectives without any extra data/params. (1/6)

    , , i još njih 2
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  19. proslijedio/la je Tweet
    26. srp 2019.

    🦄 We ported 's GPT-2 to run on-device (using Swift and CoreML on iOS) 🔥🔥 Large transformers models can now live on the edge. 📱📲 The video below is GPT-2 running locally (no network) on the device! Code: Built w/ at

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