Andrei Atanov (@andrew_atanov) 's Twitter Profile
Andrei Atanov

@andrew_atanov

PhD student at @EPFL_en

ID: 917827470962946049

linkhttps://andrewatanov.github.io/ calendar_today10-10-2017 19:01:36

42 Tweet

226 Followers

609 Following

Arsenii Ashukha (@senya_ashuha) 's Twitter Profile Photo

Our paper on learning test-time augmentations has been accepted to #UAI2020! Greedy policy search (GPS) is a way to learn a data augmentation policy for a pre-trained model, and it boosts the performance of a test-time augmentation ensemble both in- and out-of-domain! 1/4

EPFL Computer and Communication Sciences (@icepfl) 's Twitter Profile Photo

Amir Zamir (Amir Zamir) has openings for #PhD students in his group. Find out more about his #research at: vilab.epfl.ch/zamir/, and learn more about our EPFL #EDIC #computerscience PhD program: go.epfl.ch/phd-edic

Amir Zamir (<a href="/zamir_ar/">Amir Zamir</a>) has openings for #PhD students in his group. Find out more about his #research at: vilab.epfl.ch/zamir/, and learn more about our <a href="/EPFL/">EPFL</a> #EDIC #computerscience PhD program: go.epfl.ch/phd-edic
Andrei Atanov (@andrew_atanov) 's Twitter Profile Photo

Happy our work on transfer evaluation is finally out! We highlight the importance of the choice of 1) the transfer dataset sizes, 2) control baselines, and 3) downstream tasks. We also suggest a metric that displays the transfer performance in an accessible and informative way.

Andrei Atanov (@andrew_atanov) 's Twitter Profile Photo

I’m at #NeurIPS2022 this week. DM me if you want to chat about deep learning generalization, transfer/meta-/self-/semi-supervised learning.

I’m at #NeurIPS2022 this week. DM me if you want to chat about deep learning generalization, transfer/meta-/self-/semi-supervised learning.
Amir Zamir (@zamir_ar) 's Twitter Profile Photo

We are releasing 4M-21 with a permissive license, including its source code and trained models. It's a pretty effective multimodal model that solves 10s of tasks & modalities. See the demo code, sample results, and the tokenizers of diverse modalities on the website. IMO, the

Andrei Atanov (@andrew_atanov) 's Twitter Profile Photo

Are you interested in designing the morphology of intelligent agents, or just want to learn something new? Join us today at the #CVPR2024 tutorial at 9:00 AM in Summit 344. comp-design.epfl.ch

Andrei Atanov (@andrew_atanov) 's Twitter Profile Photo

Scaling up is everywhere (and is effective), but what about scaling down? I am excited to share our new work on solving vision tasks with simple low-resolution vision sensors (1-pixel cameras).

Andrei Atanov (@andrew_atanov) 's Twitter Profile Photo

Standard zero-shot inference makes independent predictions for each task’s input. Instead, we propose joint inference, which makes predictions for multiple inputs simultaneously by maximizing the joint probability of their answers. We find that this approach boosts zero-shot

Standard zero-shot inference makes independent predictions for each task’s input. Instead, we propose joint inference, which makes predictions for multiple inputs simultaneously by maximizing the joint probability of their answers. We find that this approach boosts zero-shot