Discover and read the best of Twitter Threads about #unsupervised
Most recents (3)
The curation week is almost over and I would like to thank everyone for joining the discussions this week! It’s been a blast 🥳
If you enjoyed this week, feel free to reach out on Twitter (@cosima_meyer) or GitHub (github.com/cosimameyer/) ✨
If you enjoyed this week, feel free to reach out on Twitter (@cosima_meyer) or GitHub (github.com/cosimameyer/) ✨
@cosima_meyer I feel very honored that I had the chance to talk with you about the things I enjoy doing and I cannot wait to learn more from the upcoming curators - the lineup looks amazing! 💜
@cosima_meyer If you missed a Twitter thread this week, head over to @pilizalde's amazing thread where she collected all of them (I love the GitHub emoji 😺)
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Want to do #eyetracking in #fMRI without eye tracker? Check out our #preprint!
@CYHSM & I developed #DeepMReye, a #DeepLearning framework to decode gaze position from the MR-signal of the eyeballs. No camera needed! w/@doellerlab
biorxiv.org/content/10.110…
Thread below! 👇
@CYHSM & I developed #DeepMReye, a #DeepLearning framework to decode gaze position from the MR-signal of the eyeballs. No camera needed! w/@doellerlab
biorxiv.org/content/10.110…
Thread below! 👇
How does MR-based #eyetracking work?
The orientation of the eyeballs naturally reflects gaze direction. This & the strong MR-signal variations induced by eye movements serve as the basis for gaze decoding.
The orientation of the eyeballs naturally reflects gaze direction. This & the strong MR-signal variations induced by eye movements serve as the basis for gaze decoding.
What is #DeepMReye & what does it do?
It's a #CNN that uses the multi-voxel-pattern of the eyeballs to decode gaze position on the screen. It can perform #eyetracking even in existing #fMRI data.
We tested it on 268 participants scanned with 14 scan protocols on 5 MRI scanners!
It's a #CNN that uses the multi-voxel-pattern of the eyeballs to decode gaze position on the screen. It can perform #eyetracking even in existing #fMRI data.
We tested it on 268 participants scanned with 14 scan protocols on 5 MRI scanners!
New paper out!
scirate.com/arxiv/1910.015…
It's time to talk about #machinelearning, generative models, statistical physics and spin glasses
scirate.com/arxiv/1910.015…
It's time to talk about #machinelearning, generative models, statistical physics and spin glasses
First, I'm very happy to say this is the first purely-@ICFOnians collaboration I'm doing (although Przemek is now in @UAM_Poznan), and I've been lucky to work side-by-side with the great @gorka_mgm, who is also a @BecarioslaCaixa
But let's go to the matter. The problem we deal with has to do with #unsupervised #learning. Given some data, we want to learn its fundamental features, in order to generate more data. This is used in #generative modelling for image denoising, colorization... etc
