Deep learning in electron microscopy

JM Ede - Machine Learning: Science and Technology, 2021 - iopscience.iop.org
Deep learning is transforming most areas of science and technology, including electron
microscopy. This review paper offers a practical perspective aimed at developers with …

REFORMS: Consensus-based Recommendations for Machine-learning-based Science

S Kapoor, EM Cantrell, K Peng, TH Pham, CA Bail… - Science …, 2024 - science.org
Machine learning (ML) methods are proliferating in scientific research. However, the
adoption of these methods has been accompanied by failures of validity, reproducibility, and …

No train no gain: Revisiting efficient training algorithms for transformer-based language models

J Kaddour, O Key, P Nawrot… - Advances in Neural …, 2024 - proceedings.neurips.cc
The computation necessary for training Transformer-based language models has
skyrocketed in recent years. This trend has motivated research on efficient training …

Talkin''Bout AI Generation: Copyright and the Generative-AI Supply Chain

K Lee, AF Cooper, J Grimmelmann - ar**, M Goldblum, PE Pope, M Moeller… - arxiv preprint arxiv …, 2021 - arxiv.org
It is widely believed that the implicit regularization of SGD is fundamental to the impressive
generalization behavior we observe in neural networks. In this work, we demonstrate that …

The worst of both worlds: A comparative analysis of errors in learning from data in psychology and machine learning

J Hullman, S Kapoor, P Nanayakkara… - Proceedings of the …, 2022 - dl.acm.org
Arguments that machine learning (ML) is facing a reproducibility and replication crisis
suggest that some published claims in research cannot be taken at face value. Concerns …