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  1. Prikvačeni tweet
    1. ruj 2018.

    Started with Course 1 on Completed Week 1 Lecture. Needless to say, it's AMAZING! Thanks, Sharing a slide from the lecture that I recently even felt personally If you are in such a position, you should shake things up a bit!

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  2. 24. sij

    I will be continuing my Research Internship here India got till July 31st 2019, got extended yesterday on my birthday :) Yay! P.S. I am open to new opportunities after July 31st.

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  3. 7. sij

    Just wondering, why not just make it a must to submit a notebook along with a paper. The authors can call APIs for the functions that they don't want to disclose and make everything else public. How does that sound?

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  4. 4. sij

    OneGAN: Simultaneous Unsupervised Learning of Conditional ImageGeneration, Foreground Segmentation, and Fine-Grained Clustering They get better results than networks that focus on single tasks.

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  5. 3. sij

    Learning By Cheating They train 2 networks at 2 stages, the first one "cheats" on the ground truth, and the 2nd one learns from imitating the first one. They benchmark on CARLA and NoCrash.

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  6. 1. sij

    Happy New Year, 2020! Looking forward to more crazy and new ideas in this decade, less competing for beating the SOTA and more appreciating the idea that is presented, more diversion and inclusion, what else, the VISA issues, I hope they get better. Below is my 2019 summarized.

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  7. 28. pro 2019.

    Vid2Game: Controllable Characters Extracted from Real-World Videos "The model generates novel image sequences of that person, according to arbitrary user-defined control signals, typically marking the displacement of the moving body."

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  8. 26. pro 2019.

    Hamiltonian Generative Networks "we introduce the Hamiltonian Generative Network (HGN), the first approach capable of consistently learning Hamiltonian dynamics from high-dimensional observations (such as images) without restrictive domain assumptions."

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  9. 24. pro 2019.
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  10. proslijedio/la je Tweet

    Facebook AI has released Libri-light, the largest open source data set for speech recognition to date. This new benchmark helps researchers pretrain acoustic models to understand speech, with few to no labeled examples.

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  11. 19. pro 2019.

    . achieved SOTA results on the collaborative card game of Hanabi. They use a real-time search method as used in Pluribus, which is an algorithm from the FAIR, Carnegie Mellon that won at a 6player poker game against pros

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  12. 9. pro 2019.

    Just completed the Computer Vision Nanodegree!! So Exciting!! Thanks to , and for this opportunity! :)

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  13. 4. pro 2019.
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  14. 2. pro 2019.

    A Simple yet Effective Way for Improving the Performance of GANs. The paper introduces a new CR module inside the Discriminator which supports the D in distinguishing between real and fake image, which in turn strengthens the Generator. Paper:

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  15. 3. stu 2019.

    EdgeFool: An Adversarial Image Enhancement Filter Compared against 6 other popular attacks and claim to be better than 5 of them. The sixth one which is SemanticAdv has a lower recongnizability score but suffers from natural image color

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  16. 30. lis 2019.

    Adversarial Feedback Loop. Paper from ICCV 2019 They use the Discriminator at the test time to get better results!

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  17. 28. lis 2019.

    Means a lot that you shared this . Thanks. :)

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  18. 17. lis 2019.

    New State of the Art perturbation technique called the Plush Giraffe Perturbation from . . . Oh, they also solve the Rubik's Cube with a Robot Hand using Deep Learning. 😛 Article:

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  19. 16. lis 2019.

    Got my first Article published. Titled: Introduction to Adversarial Machine Learning. Most exciting part is it uses my Library scratchai built on top of 🤩🤩 Link: Library:

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  20. 15. lis 2019.

    To all the new comers in Machine Learning, the places that you should go to are and pure googling and reading papers and implementing them. You really can't learn anything in 5 mins. You need to put in the time.

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  21. 27. ruj 2019.

    Memory-Augmented Neural Networks for Machine Translation Paper:

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