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Prikvačeni tweet
I'm starting a professorship in the CS department at UNC in fall 2020 (!!) and am hiring students! If you're interested in doing a PhD
@unccs please get in touch. More info here: https://cs.unc.edu/admissions/graduate/graduate-programs/ …Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Colin Raffel proslijedio/la je Tweet
Fixmatch: code for training on Imagenet dataset is released and available here: https://github.com/google-research/fixmatch/tree/master/imagenet … https://arxiv.org/abs/2001.07685 by Kihyuk Sohn
@D_Berthelot_ML@chunliang_tw@ZizhaoZhang Nicholas Carlini@ekindogus@alexey2004@Han_Zhang_@colinraffelHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Thanks
@sleepinyourhat for hosting! Always a pleasure!!Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Had a great time talking about T5 and chatting with students
@NYUDataScience yesterday! I'm waiting to put slides online until I finish annotating them; in the meantime here is a recording of the same talk from when I gave it@allen_ai earlier this month: https://www.youtube.com/watch?v=eKqWC577WlI …https://twitter.com/sleepinyourhat/status/1222553470827405312 …
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Hot take: Mathiness [1] is like an adversarial patch [2] for ML conference reviewers: Mathiness causes a reviewer to classify the paper as "accept" regardless of whether the math is useful/valid and the paper is any good. [3] Fig. 6 has some empirical evidence of this. (refs
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Colin Raffel proslijedio/la je Tweet
FixMatch: focusing on simplicity for semi-supervised learning and improving state of the art (CIFAR 94.9% with 250 labels, 88.6% with 40). https://arxiv.org/abs/2001.07685 Collaboration with Kihyuk Sohn,
@chunliang_tw@ZizhaoZhang Nicholas Carlini@ekindogus@Han_Zhang_@colinraffelpic.twitter.com/BmeYvpEHzX
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Today, the T5 team competed against T5 in a "pub quiz" on (context-free) questions from the TriviaQA/NQ validation sets. We LOST! We only got 20% right; T5 got 35%. To see how to fine-tune T5 on context-free QA (or any other task) with a free TPU, check out our Colab tutorial
https://twitter.com/ada_rob/status/1204061067480977408 …
0:25Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Colin Raffel proslijedio/la je Tweet
I'm so excited about the program we've put together for Saturday's
#NeurIPS2019 ML for Creativity and Design Workshop 3.0. Aside from the amazing accepted talks and posters, we have a diverse set of invited speakers I want to highlight in this thread.https://neurips2019creativity.github.io/Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Me at the
#neurips poster session when I see a paper I reviewed and fought for acceptingpic.twitter.com/dQuPB6hDffHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
#neurips tips day 5 (h/t@chris_j_beckham)! Conferences are a parade of successes. Remember that for every impressive paper there are many (unpublished) ideas that didn't pan out. Take this opportunity to ask people about negative results!Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Our code is available here: https://github.com/google-research/mixmatch … MixMatch is joint work with
@d_berthelot_ml,@goodfellow_ian, Nicholas Carlini,@avitaloliver, and@nicolaspapernot. Thanks for coming to my Twitter poster presentation! 11/11Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
The MixMatch paper includes experiments on other datasets as well as a nice ablation study. In the past 6 months, our results have been beaten - including by us! Look out for more exciting SSL results soon. 10/11
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The combination of these ingredients produced SoTA results in the realistic SSL setting (https://arxiv.org/abs/1804.09170 ) when MixMatch came out. For example, we achieved an error rate of about 11% with only 250 labels on CIFAR-10. 9/11pic.twitter.com/0YCwIxkdtz
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For regularization, we use weight decay and MixUp (https://arxiv.org/abs/1710.09412 ) across both labeled and unlabeled examples. This is super important to get good performance. 8/11
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After we've obtained label guesses, we just proceed as if we're doing supervised learning, with label guesses as targets for unlabeled examples and the ground-truth labels as targets for labeled examples. 7/11
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Sharpening simply corresponds to lowering the distribution's temperature. The sharpened average prediction is then used as the label guess for the original unlabeled image. 6/11
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MixMatch is a *holistic* SSL algorithm which combines these ingredients in a simple recipe. MixMatch first feeds k augmented versions of an unlabeled image to the model to obtain k predictions. It then averages the k predictions and "sharpens" the result. 5/11pic.twitter.com/HSzXGaY4i3
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Another important ingredient when doing semi-supervised learning is regularization, because typically we only have a few labels and it's easy for the model to memorize them. 4/11
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Two common ingredients for producing label guesses are consistency regularization ("When I perturb the input or model, the model's prediction shouldn't change.") and entropy minimization ("The model should output low-entropy/confident predictions on unlabeled data.") 3/11
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The goal in semi-supervised learning (SSL) is to use unlabeled data to improve a model's performance. Many approaches do this by using the model to produce "label guesses" for unlabeled data, and then training the model to predict those guesses. 2/11
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