Andrei Bursuc

@abursuc

Research scientist at on computer vision and machine learning; Inria alumni; PhD from Mines ParisTech. Opinions are my own

Paris, France
abursuc.github.io
Vrijeme pridruživanja: studeni 2008.
248 fotografija ili videozapisa Fotografije i videozapisi

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  1. Prikvačeni tweet
    27. sij

    TRADI: Tracking deep neural network weight distributions -- work with G. Franchi We’re proposing a cheap method for getting ensembles of networks from a single network training 1/

    29 proslijeđenih tweetova 129 korisnika označava da im se sviđa
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  2. proslijedio/la je Tweet
    11. kol 2019.

    An awesome matplotlib cheatsheet from Nicolas Rougier on the matplotlib mailing list.

    233 proslijeđena tweeta 688 korisnika označava da im se sviđa
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  3. 3. velj

    A really fun summer school mixing talks from an all-star line-up followed by daily sports activities.

    The Vision and Sports Summer School is back: do science, do sports, register now!
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  4. proslijedio/la je Tweet
    1. velj

    This bears repeating: PhDs who end up working outside the academy (for whatever reason) are not failures. The academy has no monopoly on great and important work. There are great success to be had in other sectors. Enough already with the professional elitism.

    135 replies 765 proslijeđenih tweetova 6.017 korisnika označava da im se sviđa
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  5. 30. sij

    I can imagine that the job of ACs is really challenging and making sure all papers get high quality reviews is not easy. Although, I'm sure most ACs are highly qualified scientists, if the decision is ultimately taken by a single person feels weird for peer review.

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  6. 30. sij

    is quite a particular venue in terms of reviewing experience: submissions with all accepts ratings (score 6/8) can get rejected , while others with all rejects can get accepted 🙃

    3 proslijeđena tweeta 11 korisnika označava da im se sviđa
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  7. proslijedio/la je Tweet
    29. sij

    Virtual KITTI 2 is out! ***New release of synthetic dataset based on *** proxy virtual worlds , -object tracking, matching, estimation, ,

    70 proslijeđenih tweetova 154 korisnika označavaju da im se sviđa
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  8. 27. sij

    We evaluate our method on standard classification and regression benchmarks, and on out-of-distribution detection for classification and semantic segmentation, achieving competitive results. 4/

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  9. 27. sij

    Here, we track the trajectory of the weights during optimization using Kalman filters, allowing us to compute distributions of the weights. We can then sample an ensemble of networks for estimating model uncertainty. 3/

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  10. 27. sij

    Weights of a DNN are optimized from a random init towards an optimum value minimizing the loss. Only this final state of the weights is typically kept for testing, while the wealth of information on the geometry of the weight space, accumulated over the descent is discarded. 2/

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  11. proslijedio/la je Tweet
    22. sij

    Excited to share PCGrad, a super simple & effective method for multi-task learning & multi-task RL: project conflicting gradients On Meta-World MT50, PCGrad can solve *2x* more tasks than prior methods w/ Tianhe Yu, S Kumar, Gupta, ,

    5 replies 101 proslijeđeni tweet 403 korisnika označavaju da im se sviđa
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  12. 21. sij

    The recent demo from Mobileye on a 20 min continuous session on the streets of Jerusalem is quite impressive

    4 proslijeđena tweeta 9 korisnika označava da im se sviđa
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  13. 17. sij

    reviews: witnessing the commoditization of ImageNet experiments in submitted papers. The amount of experiments that some authors manage to squeeze-in their papers is dazzling.

    1 proslijeđeni tweet 2 korisnika označavaju da im se sviđa
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  14. 14. sij

    Join these fantastic lecturers in one of the most scenic places in the French Alps for 1 month. I wish I were a graduate student again :)

    Do you want to enjoy this view for a month while learning about Statistical Physics and Machine Learning from e.g. , , , , ? Applications just opened, more info at . Please help to spread the word.
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  15. proslijedio/la je Tweet
    10. sij

    Domain adaptation by enabling camera and lidar-based models to learn from each other. Work by et al.

    Check out our new work xMUDA! Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation - UDA approach that exploits multi-modality - 2D-3D cross-modal learning between images and point clouds - Works for many interesting adaptation scenarios
    5 proslijeđenih tweetova 5 korisnika označava da im se sviđa
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  16. 7. sij

    This new augmented visualization system for fencing games is mesmerizing. Great work by and collaborators

    0:40
    本日まで幕張メッセで開催された「高円宮杯 JAL PRESENTS FENCING WORLD CUP 2019」でライゾマティクスが開発した「Points of a sword visualization system」が披露されました。先月の全日本選手権に続いて二度目の実戦導入となりました。ご覧いただいた皆様、関係者の皆様、誠に有難うございました！
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    16 proslijeđenih tweetova 61 korisnik označava da mu se sviđa
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  17. 6. sij

    Oh yeah! (back from holidays with a 5yo and 1yo)

    being back at work after 2 weeks of vacation is the most luxurious thing in the world. you mean I can just sit here, at my desk? I only have to feed *myself* at lunch? I can go to the bathroom ALONE?
    5 korisnika označava da im se sviđa
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  18. proslijedio/la je Tweet
    4. sij

    Wow, there is huge revolution on happening right now. More and more competitions are "kernel-only-submisson". Which means you should upload your model to cloud and then all the inference is done in submission kernel with runtime limit. Farewell 100500 models ensembles!

    42 proslijeđena tweeta 253 korisnika označavaju da im se sviđa
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  19. 26. pro 2019.

    Don't miss this excellent defence by of the Bayesian approach for DNNs

    Bayesian methods are *especially* compelling for deep neural networks. The key distinguishing property of a Bayesian approach is marginalization instead of optimization, not the prior, or Bayes rule. This difference will be greatest for underspecified models like DNNs. 1/18
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    1 proslijeđeni tweet 10 korisnika označava da im se sviđa
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  20. 19. pro 2019.

    We've released code, pre-trained models and configs for all experiments in our boosting few-shot learning with self-supervision work:

    New work lead by S. Gidaris on boosting few-shot learning methods with self-supervision Few-shot learning and self-supervised learning address different facets of the same problem: how to train a model with little or no labeled data 1/
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    1 reply 8 proslijeđenih tweetova 26 korisnika označava da im se sviđa
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