Ivan Sosnovik @isosnovik
Applied Scientist. GenAI Innovation Center. AWS Previously, PhD student at the University of Amsterdam. Deep Learning, Computer Vision. All views are my own isosnovik.xyz London, UK Joined April 2016-
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✨Amsterdam's exceptional scientist is one of the recently appointed ELLIS Scholars Congrats, @erikjbekkers 🚀 ivi.fnwi.uva.nl/ellis/2023/11/… #ELLISforEurope #ELLISunitAmsterdam #AI #ML
We proudly present our 524 page book on equivariant convolutional networks. Coauthored by Patrick Forré, @erikverlinde and @wellingmax. maurice-weiler.gitlab.io/#cnn_book [1/N]
Dr. Vera Sosnovik @Vsosnovik Congrats, dear sister!
Why do we need equivariant networks for symmetries? Can’t we just learn it all from data? Spoiler - it is challenging. Excited to share our paper “On genuine invariance learning without weight-tying”. Soon at @TAGinDS in @icmlconf. ! Paper: openreview.net/pdf?id=yYEgfbC… [1/5]
Congratulations dr. @isosnovik 🥳
📄 Paper accepted to CVPR workshop L3D-IVU! 📄 with MSc student(!) Tomasz and @jan_gemert. We find evidence that learned equivariance in image recognition models correlates with accuracy, then we show what affects said equivariance. Read it here: rjbruin.github.io/publication/le…
Do video-language models have a sense of time? If not, can we instil it in them without the expensive pre-training-from-scratch? We shed light on these questions in our @CVPR 2023 paper. Work done with @MakarandTapaswi and @cgmsnoek. Page: bpiyush.github.io/testoftime-web… 🧵 1/N (N=8)
I am happy to release our work "Patch Gradient Descent: Training Neural Networks on Very Large Images", a method that allows to train existing neural networks on images of very large size without the need to struggle with GPU memory bottleneck. See here: arxiv.org/abs/2301.13817
At TU Delft we have a PhD position on efficient computer vision: tudelft.nl/over-tu-delft/…
We use contrastive learning, differentiable top-K and mention the word "invariance" a couple of times. If I ever came across a paper with such a combo, I'd definitely read it.
We use contrastive learning, differentiable top-K and mention the word "invariance" a couple of times. If I ever came across a paper with such a combo, I'd definitely read it.
Yay! Happy to announce that our paper about generalisation of G-CNN is now online! arxiv.org/abs/2212.09507
A recording of @artemmoskalev's talk on Studying Learned Lie Group Generators is now available on youtube. If you couldn't make it to the session last Friday, check it out :) Very interesting presentation!👇 Youtube: youtube.com/watch?v=BUQ5VN…
Very proud of the best student paper award at BMVC (honorable mention) for the MSc thesis work of Liang Zeng. Wonderful to have helped him achieve this! @tudelft @EEMCS_TUD @tudelft_AI
Graphics is incredible but what about the GAMEPLAY. These days most AAA games have a gameplay more primitive than Simpson Hit&Run from 20 years ago. What about the AI of enemies? With all RL research the best we get in the games is slightly better (or not) than Half Life 2 (2004)
Graphics is incredible but what about the GAMEPLAY. These days most AAA games have a gameplay more primitive than Simpson Hit&Run from 20 years ago. What about the AI of enemies? With all RL research the best we get in the games is slightly better (or not) than Half Life 2 (2004)
Meet and greet several team members of the Video & Image Sense Lab at the #ECCV2022 conference from October 23-27 in Tel Aviv. We have six papers on #computer #vision by #learning, a workshop on #self-#supervised learning and a tutorial on #hyperbolic rep…lnkd.in/gP6bqdyV
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AK @_akhaliq
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Join us TOMORROW for the GRASP SFI seminar featuring Dr. Erik Bekkers who will be presenting "Fast, Expressive SE(n) Equivariant Networks through Weight-Sharing in Position-Orientation Space" from 3:00PM - 4:30PM. For more info, please visit: grasp.upenn.edu/events/spring-… #GRASPSFI
Accepted at ICLR! github.com/ebekkers/ponita We derived pair-wise invariants that capture all geometric info needed to build simple + efficient SE(n) equivariant networks 🐴🔥 SOTA on 5 benchmarks (incl generation of molecules). Oh right, and ✨convolution is still all you need!
✨Amsterdam's exceptional scientist is one of the recently appointed ELLIS Scholars Congrats, @erikjbekkers 🚀 ivi.fnwi.uva.nl/ellis/2023/11/… #ELLISforEurope #ELLISunitAmsterdam #AI #ML
Interested in doing a PhD on AI for the physical sciences at Cambridge? I am taking PhD students for 2024!! Please find information below, including a list of projects: astroautomata.com/prospectives/ (Deadline typically early December or January, depending on program)
We proudly present our 524 page book on equivariant convolutional networks. Coauthored by Patrick Forré, @erikverlinde and @wellingmax. maurice-weiler.gitlab.io/#cnn_book [1/N]
How can we simplify the designing of equivariant neural networks and make them more expressive? In this paper, recently accepted to NeurIPS 2023, we develop implicit steerable kernels to answer this question! 📰: arxiv.org/abs/2212.06096 💻: maxxxzdn.github.io/blog/implicit_… 🧵1/10
For decades particle physicists have built specialized ML models for distinct tasks. Now it's time to build a foundation model with wide-ranging capabilities. We're taking the first step with the Covariant Particle Transformer (CPT)! Find pre-trained weights at the end. 1/7
What makes CLIP work? The contrast with negatives via softmax? The more negatives, the better -> large batch-size? We'll answer "no" to both in our ICCV oral🤓 By introducing SigLIP, a simpler CLIP that also works better and is more scalable, we can study the extremes. Hop in🧶
Do we need equivariant networks to process symmetries? Come see our poster at @TAGinDS tomorrow (28 July) at 16:00 in Meeting Room 317 B! 🏄
Why do we need equivariant networks for symmetries? Can’t we just learn it all from data? Spoiler - it is challenging. Excited to share our paper “On genuine invariance learning without weight-tying”. Soon at @TAGinDS in @icmlconf. ! Paper: openreview.net/pdf?id=yYEgfbC… [1/5]
Excited to be presenting two papers at ICML! Come say hi! Latent Traversals in Generative Models as Potential Flows: Today @ 2pm, #807 with Yue Song Paper: arxiv.org/abs/2304.12944 Neural Wave Machines: Tomorrow @ 11am, #420 with @wellingmax Paper: proceedings.mlr.press/v202/keller23a…
Why do we need equivariant networks for symmetries? Can’t we just learn it all from data? Spoiler - it is challenging. Excited to share our paper “On genuine invariance learning without weight-tying”. Soon at @TAGinDS in @icmlconf. ! Paper: openreview.net/pdf?id=yYEgfbC… [1/5]
Thank you very much 😄
Congratulations dr. @THU_Shuo 🥳
Submit your amazing inductive bias work!
Working on prior knowledge in Computer Vision? Consider submitting to our workshop @ #ICCV2023! Topics include: geometric deep learning, data augmentation, biases that benefit data efficiency, and more! 📅 Deadline: July 16th 🌐 CfP: vipriors.github.io/call-for-papers
Had a blast hosting @capetorch at @tudelft for a super useful workshop! Hope everyone learned to use @weights_biases's awesome tooling! 😄
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