no code implementations • 30 Apr 2024 • Samuel Lavoie, Polina Kirichenko, Mark Ibrahim, Mahmoud Assran, Andrew Gordon Wildon, Aaron Courville, Nicolas Ballas
Contrastive Language Pretraining (CLIP) on the other hand, works by mapping an image and its caption to a single vector -- limiting how well CLIP-like models can represent the diverse ways to describe an image.
no code implementations • 24 Jul 2023 • Megan Richards, Polina Kirichenko, Diane Bouchacourt, Mark Ibrahim
Second, we study model generalization across geographies by measuring the disparities in performance across regions, a more fine-grained measure of real world generalization.
no code implementations • 28 Nov 2022 • Wanqian Yang, Polina Kirichenko, Micah Goldblum, Andrew Gordon Wilson
Deep neural networks are susceptible to shortcut learning, using simple features to achieve low training loss without discovering essential semantic structure.
1 code implementation • 20 Oct 2022 • Pavel Izmailov, Polina Kirichenko, Nate Gruver, Andrew Gordon Wilson
Deep classifiers are known to rely on spurious features $\unicode{x2013}$ patterns which are correlated with the target on the training data but not inherently relevant to the learning problem, such as the image backgrounds when classifying the foregrounds.
4 code implementations • 6 Apr 2022 • Polina Kirichenko, Pavel Izmailov, Andrew Gordon Wilson
Neural network classifiers can largely rely on simple spurious features, such as backgrounds, to make predictions.
Ranked #1 on Out-of-Distribution Generalization on UrbanCars
no code implementations • ICML Workshop INNF 2021 • Polina Kirichenko, Mehrdad Farajtabar, Dushyant Rao, Balaji Lakshminarayanan, Nir Levine, Ang Li, Huiyi Hu, Andrew Gordon Wilson, Razvan Pascanu
Learning new tasks continuously without forgetting on a constantly changing data distribution is essential for real-world problems but extremely challenging for modern deep learning.
2 code implementations • NeurIPS 2021 • Samuel Stanton, Pavel Izmailov, Polina Kirichenko, Alexander A. Alemi, Andrew Gordon Wilson
Knowledge distillation is a popular technique for training a small student network to emulate a larger teacher model, such as an ensemble of networks.
1 code implementation • NeurIPS 2020 • Polina Kirichenko, Pavel Izmailov, Andrew Gordon Wilson
Detecting out-of-distribution (OOD) data is crucial for robust machine learning systems.
2 code implementations • ICML 2020 • Pavel Izmailov, Polina Kirichenko, Marc Finzi, Andrew Gordon Wilson
Normalizing flows transform a latent distribution through an invertible neural network for a flexible and pleasingly simple approach to generative modelling, while preserving an exact likelihood.
Semi-Supervised Image Classification Semi-Supervised Text Classification
1 code implementation • 17 Jul 2019 • Pavel Izmailov, Wesley J. Maddox, Polina Kirichenko, Timur Garipov, Dmitry Vetrov, Andrew Gordon Wilson
Bayesian inference was once a gold standard for learning with neural networks, providing accurate full predictive distributions and well calibrated uncertainty.
3 code implementations • 26 Apr 2019 • Guandao Yang, Tianyi Zhang, Polina Kirichenko, Junwen Bai, Andrew Gordon Wilson, Christopher De Sa
Low precision operations can provide scalability, memory savings, portability, and energy efficiency.