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Required Reading Example Prior Work: Example Follow-up Work:
Frankle, J., & Carbin, M. (2018). “The lottery ticket hypothesis: Finding sparse, trainable neural networks.” arXiv preprint arXiv:1803.03635. Optimal Brain Damage. Yann LeCun, John Denker, Sara Solla. NIPS 1989.

Learning both Weights and Connections for Efficient Neural Networks. Song Han, Jeff Pool, John Tran, William J. Dally. NIPS 2015.

Pruning Filters for Efficient ConvNets. Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, Hans Peter Graf. ICLR 2017.
The Lottery Ticket Hypothesis for Pre-trained BERT Networks. Tianlong Chen, Jonathan Frankle, Shiyu Chang, Sijia Liu, Yang Zhang, Zhangyang Wang, Michael Carbin. NeurIPS 2020.

Linear Mode Connectivity and the Lottery Ticket Hypothesis. Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, Michael Carbin. ICML 2020.

Drawing Early-Bird Tickets: Toward More Efficient Training of Deep Networks. Haoran You, Chaojian Li, Pengfei Xu, Yonggan Fu, Yue Wang, Xiaohan Chen, Richard G. Baraniuk, Zhangyang Wang, Yingyan Lin. ICLR 2020.

Proving the Lottery Ticket Hypothesis: Pruning is All You Need. Eran Malach, Gilad Yehudai, Shai Shalev-Schwartz, Ohad Shamir. ICML 2020.

The Early Phase of Neural Network Training. Jonathan Frankle, David J. Schwab, Ari S. Morcos. ICLR 2020.
Zhao, Jieyu, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang. (2017). "Men also like shopping: Reducing gender bias amplification using corpus-level constraints." arXiv preprint arXiv:1707.09457.
“Stress Test Evaluation for Natural Language Inference.” Aakanksha Naik, Abhilasha Ravichander, Norman Sadeh, Carolyn Rose, Graham Neubig. 27th International Conference on Computational Linguistics (COLING-2018)
Ken Holstein, Jennifer Wortman Vaughan, Hal Daumé III, Miro Dudík, Hanna Wallach, Improving fairness in machine learning systems: What do industry practitioners need?, in Proceedings of 2019 ACM CHI Conference on Human Factors in Computing Systems.
Peter Henderson, Jieru Hu, Joshua Romoff, Emma Brunskill, Dan Jurafsky, Joelle Pineau. Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning, JMLR 2020.
Emily M. Bender and Alexander Koller, Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data, Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5185–5198.
Kuchnik, M., Klimovic, A., Simsa, J., Smith, V., & Amvrosiadis, G. (2022). Plumber: Diagnosing and Removing Performance Bottlenecks in Machine Learning Data Pipelines. Proceedings of Machine Learning and Systems, 4, 33-51.