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Come to our talk on Gauge Equivariant Convolutional Networks and Icosahedral CNNs today at 14:40 @ Grand Ballroom,
#ICML2019. Happy to discuss more details and connections to physics at poster #76 @ Pacific Ballroom, 18:30. With@TacoCohen,@KicanaogluB and@wellingmax .pic.twitter.com/9AxLWko8RM
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Maurice Weiler proslijedio/la je Tweet
Quaternions and Euler angles are discontinuous and difficult for neural networks to learn. They show 3D rotations have continuous representations in 5D and 6D, which are more suitable for learning. i.e. regress two vectors and apply Graham-Schmidt (GS). https://arxiv.org/abs/1812.07035 pic.twitter.com/fXUF3sgkTT
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Maurice Weiler proslijedio/la je Tweet
We are organizing an
@ELLISforEurope Workshop on Geometric and Relational Deep Learning! Registration invites will be shared soon. Interested in participating? Consider submitting an abstract or get in touch: https://geometric-relational-dl.github.io/ w/@erikjbekkers@wellingmax@mmbronsteinpic.twitter.com/RGEHezfZIa
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Maurice Weiler proslijedio/la je Tweet
For those images aren't all taken in a standard orientation (all of us!), this is a super interesting library. Their GIF shows how their feature fields are invariant with respect to rotation, unlike a standard CNN.
@CShorten30 your kind of paper https://arxiv.org/abs/1911.08251 https://twitter.com/maurice_weiler/status/1217058168003612674 …pic.twitter.com/mfHRkbuk6GPrikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Our work on Gauge Equivariant CNNs made it into Quanta Magazine! The article gives a nice overview on coordinate independent convolutions and connections between theoretical physics and deep learning.https://twitter.com/wellingmax/status/1215348614937137159 …
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Maurice Weiler proslijedio/la je Tweet
Really exciting to have my first paper accepted at
@iclr_conf#ICLR2020! It provides the first group theoretical approach towards equivariant visual attention. Nice things coming up next!@erikjbekkers@jmtomczak@Mark. Co-Attentive Equivariant Nets: https://openreview.net/forum?id=r1g6ogrtDr …pic.twitter.com/jppwAD8NJy
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Maurice Weiler proslijedio/la je Tweet
Really happy with my paper being accepted
@iclr_conf#ICRL! It describes a flexible framework for building G-CNNs that are equivariant to a large class of transformation groups. B-Spline CNNs on Lie groups: https://openreview.net/forum?id=H1gBhkBFDH …pic.twitter.com/AYc0QsfAzM
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Check also
@deworrall92's work on scale equivariance. It makes the non-invertibility of dilations on pixel grids explicit via scale space theory and semi-groups. https://arxiv.org/abs/1905.11697Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
The formulation is very similar to our rotation equivariant Steerable Filter CNNs: both models use group convolutions and define kernels in the continuum to group-transform them exactly via steering. https://arxiv.org/abs/1711.07289
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Nonetheless the proposed models work very well. It seems like the gains due to equivariance are outbalancing the loss of energy from kernels running out of the scale range.
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A nice work on scale-equivariant CNNs via group convolutions. In contrast to e.g. SO(2), the dilation group (R^+,*) is non-compact. A conv over scales thus needs to be restricted to a certain range which introduces boundary effects similar to the zero padding artifacts of CNNs.https://twitter.com/isosnovik/status/1187767318908227586 …
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1st line: E(2)-equivariant CNN 2nd line: conventional CNN paper: https://arxiv.org/abs/1911.08251 code: https://github.com/QUVA-Lab/e2cnn docs: https://quva-lab.github.io/e2cnn/
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Check out our poster #143 on general E(2)-Steerable CNNs tomorrow, Thu 10:45AM. Our work solves for the most general isometry-equivariant convolutional mappings and implements a wide range of related work in a unified framework. With
@_gabrielecesa_#NeurIPS2019#NeurIPSpic.twitter.com/wEm3QOpuPDPrikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Maurice Weiler proslijedio/la je Tweet
Check out "Scale-Equivariant Steerable Networks" (https://arxiv.org/abs/1910.11093 ). It is joint work with Michał Szmaja and Arnold Smeulders. We build scale-equivariant CNNs which do not use image rescaling and do not limit the admissible scale factors.pic.twitter.com/3SjIrbmykq
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Maurice Weiler proslijedio/la je Tweet
Since I keep getting questions about the cotan formula for tet meshes, I wrote up a note about the n-dimensional cotan formula (including a nice expression for tet meshes). Hopefully this saves me infinitely many emails for n ≥ 4. http://www.cs.cmu.edu/~kmcrane/Projects/Other/nDCotanFormula.pdf …pic.twitter.com/M1DYtv9LeA
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Maurice Weiler proslijedio/la je Tweet
“You get away from coordinates, which is like the lowest level of number, and elevate the idea of number to incorporate geometry.” —Charlie Gunn on using geometric algebra for computer vision, graphics, and engineering.
@ICERM#IllustartingMathematics See http://bivector.net pic.twitter.com/FUBUZkrr3A
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Maurice Weiler proslijedio/la je Tweet
Please consider applying for this tenure track position on fairness, accountability and transparency in AI. https://www.uva.nl/en/content/vacancies/2019/08/19-541-tenure-track-position-in-fair-accountable-and-transparent-systems.html …
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Maurice Weiler proslijedio/la je Tweet
WFC running at interactive speed on that funky grid. Next up is to make it deterministicpic.twitter.com/OyXLflGet0
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Maurice Weiler proslijedio/la je Tweet
Gauge Equivariant Convolutional Networks and the Icosahedral CNNhttps://youtu.be/wZWn7Hm8osA
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Maurice Weiler proslijedio/la je Tweet
Very excited to share our work 'GEAR: Geometry-Aware Rényi Information' (w/ @nevitus, M.Schwarzer &
@SimonLacosteJ): http://arxiv.org/abs/1906.08325 TL;DR: A perspective on Information Theory which takes geometry into account with apps. in image barycenters, mode counting & gen. models.pic.twitter.com/NkZRjvZXUA
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