Search Results for author: Ethan Caballero

Found 6 papers, 4 papers with code

Broken Neural Scaling Laws

1 code implementation26 Oct 2022 Ethan Caballero, Kshitij Gupta, Irina Rish, David Krueger

Moreover, this functional form accurately models and extrapolates scaling behavior that other functional forms are incapable of expressing such as the non-monotonic transitions present in the scaling behavior of phenomena such as double descent and the delayed, sharp inflection points present in the scaling behavior of tasks such as arithmetic.

Adversarial Robustness Continual Learning +8

Scaling Laws for the Few-Shot Adaptation of Pre-trained Image Classifiers

no code implementations13 Oct 2021 Gabriele Prato, Simon Guiroy, Ethan Caballero, Irina Rish, Sarath Chandar

Empirical science of neural scaling laws is a rapidly growing area of significant importance to the future of machine learning, particularly in the light of recent breakthroughs achieved by large-scale pre-trained models such as GPT-3, CLIP and DALL-e.

Few-Shot Learning Image Classification

In Search of Robust Measures of Generalization

1 code implementation NeurIPS 2020 Gintare Karolina Dziugaite, Alexandre Drouin, Brady Neal, Nitarshan Rajkumar, Ethan Caballero, Linbo Wang, Ioannis Mitliagkas, Daniel M. Roy

A large volume of work aims to close this gap, primarily by developing bounds on generalization error, optimization error, and excess risk.

Generalization Bounds

Skip-Thought Memory Networks

no code implementations19 Nov 2015 Ethan Caballero

Question Answering (QA) is fundamental to natural language processing in that most nlp problems can be phrased as QA (Kumar et al., 2015).

Question Answering Sentence

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