Eugene Vorontsov @VorontsovEugene
Recent PhD graduate from Mila. Montréal, Québec Joined June 2017-
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Some fun quick work for MICCAI DALI: "Label noise in segmentation networks : mitigation must deal with bias" arxiv.org/abs/2107.02189 youtu.be/iLLOwTtAfBc Segmentation models are: - Robust to unbiased errors. - Sensitive to biased errors.
Seems my advisor @chrisjpal went from (arcade) street fighting to fighting reviewer #2 in the Canadian AI mafia.
Seems my advisor @chrisjpal went from (arcade) street fighting to fighting reviewer #2 in the Canadian AI mafia.
Haven't used this for a while. Thinking maybe I should say I've graduated in the meantime ;)
Check out these big GANs at @BorealisAI
New: stable but more expressive than an orthogonal RNN, via Schur decomposition. arxiv.org/abs/1905.12080 Pleasure helping with this. Was surprised: independent idea by a new team in the same lab, 3 years after @chiheb_tr and I tried it without all the theory (and didn't pursue).
Not research related but gonna plug my new kitten instagram : instagram.com/cleoandthunder Kittens for when you need a break haha. So many more photos to sort!
We boost segmentation performance for applications such as medical imaging. Image translation for images with <--> without target. Plan to extend to multi-domain (>2): E.g. shadow segmentation. Code planned for release. arxiv.org/abs/1904.01636
I asked @ilyasut how to set neural network init. He accidentally replied with a poem: You want to be on the edge of chaos Too small, and the init will be too stable, with vanishing gradients Too large, and you'll be unstable, due to exploding gradients You want to be on the edge
We finally published this recently: SOTA auto liver cancer segmentation is poor but manual correction yields near expert-level results, significantly saving time. Thorough eval including intra- and inter-operator variability!
We finally published this recently: SOTA auto liver cancer segmentation is poor but manual correction yields near expert-level results, significantly saving time. Thorough eval including intra- and inter-operator variability!
I don't know if anybody's done this before, but I was curious to see if it was possible to train a 'binary GAN' where the prior p(z) is a Bernoulli(p). The only trick I had to do was scale z := (z-0.5) / 0.5 so that it was zero-centered. Here I'm mapping a 128-dim code to a face
We've achieved highest squat density at the Borealis booth.
An opportunity to join Mila as a tenure-track professor in AI at Polytechnique Montréal: polymtl.ca/carriere/en/of…
We just released code for our paper 'Unsupervised Depth Estimation, 3D Face Rotation and Replacement', in which we are able to infer depth from faces in a completely unsupervised manner arxiv: arxiv.org/abs/1803.09202 code: github.com/joelmoniz/Dept… video: youtube.com/watch?v=h_brJW…

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Irina Rish @irinarish
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DeepMind Health @DeepMind_Health
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Ian Goodfellow @goodfellow_ian
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