Robustness Transferability Model robustness and transferability are essential for adapting to data distribution shifts and new tasks We evaluated robustness using various ImageNet variants and found that while ViT and ConvNeXt models have comparable average performances supervised models generally outperformed CLIP on robustness except for ImageNetR and ImageNetSketch
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My name is Kirill and I am currently a Research Engineer for AI in Genomics at M42 a global techenabled healthcare company At M42 my main focus is on developing genomic foundation models At M42 my main focus is on developing genomic foundation models
Kirill Neklyudov Jannes Nys Luca Thiede Juan Carrasquilla Qiang Liu Max Welling Alireza Makhzani Action Matching Learning Stochastic Dynamics from Samples ICML 2023 Kirill Neklyudov Rob Brekelmans Daniel Severo Alireza Makhzani Orbital MCMC AISTATS 2022 oral Kirill Neklyudov Max Welling Involutive MCMC a Unifying Framework ICML 2020
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modeltotest The model to run the needle in a haystack test on Default is None evaluator An evaluator to evaluate the models response Default is None needle The statement or fact which will be placed in your context haystack haystackdir The directory which contains the text files to load as background context Only text files are supported
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This is likely the first time all estimators are on one graph The code is on github for you to use in any application But dont overlook the differences in assumptions properties amp interpretation of coefs eg which reference groups are used for causal effects amp pretrends
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Kirill Borusyak on Twitter This is likely the first time all
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About me Im an Assistant Professor at the University of Montreal and a Core Academic Member at Mila Quebec AI Institute developing novel methods in generative modelling Monte Carlo methods Optimal Transport and applying those to solve fundamental problems in natural sciences eg finding eigenstates of the manybody Schrodinger equation simulating molecular dynamics predicting the
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