Bedabrata Choudhury

@BedabrataChoud1

CS Student Lover of linguistics, mathematics & art. Tea enthusiast. 🤖

Vrijeme pridruživanja: srpanj 2018.
Rođen/a 1991.

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  1. Interesting article about cold quark matter and possibilities in engineering with quark matter in condensed phases.

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  2. 4. velj

    Interesting work in natural language generation. Sentiment and bias analysis in emergent languages remains an evergreen problem for computational linguists and NLP-ers alike.

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  3. proslijedio/la je Tweet
    4. velj

    Generating trailers automatically by Orest Rehusevych from P.S. I am glad that more Ukrainian CV people started to wear traditional vyshyvankas :) Paper:

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    3. velj

    Help revolutionize the world with full self-driving by joining us at Tesla Autopilot: It is very hard to find other places where AI expertise makes as much of a difference on as big of a problem.

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    29. sij

    ML Fairness Gym is a set of components for building simple simulations that explore the potential long-run impacts of deploying machine learning-based decision systems in social environments, based on OpenAI Gym interface.

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  6. 28. sij
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  7. proslijedio/la je Tweet
    29. svi 2012.

    Transformation Electronics: Tailoring Electron's Effective Mass.

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    Physics Titan Edward Witten Still Thinks String Theory “On the Right Track”

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  9. 26. sij
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  10. 26. sij

    More generative models on reconstructing quantum states.

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  11. 26. sij

    Really interesting review of quantum phase transitions in dense QCD.

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  12. 26. sij

    Really interesting review. Would be interesting to generalise this approach to unsupervised cases as well as generative models as well. Lots of underlying physics to be uncovered(and tweaked ?)

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  13. proslijedio/la je Tweet
    11. srp 2019.
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  14. 25. sij

    Exciting work, with interesting implications for machine learning.

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  15. 25. sij

    BART: A denoising autoencoder for high dimensional sequence to sequence models.

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  16. 25. sij

    Interesting work in high-dimensional biological data. It's interesting how biological data can be represented as a concatenation of feature matrices, which is equivalent to dealing with pattern recognition of product topologies of high dimensional feature matrices.

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  17. 25. sij

    Zero-shot audio style-transfer with autoencoders.

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  18. proslijedio/la je Tweet
    17. sij

    Learning a Latent Space of Style-Aware Symbolic Music Representations by Adversarial Autoencoders pdf: abs: github:

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    16. sij

    The connection between NNs and digital filters has been noted many many times before. The main contributions here are (a) interpretable user controls and (b) it actually works! Bravo magenta team.

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