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Hattie Zhou
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2020 – today
- 2024
- [c5]Noam Razin, Hattie Zhou, Omid Saremi, Vimal Thilak, Arwen Bradley, Preetum Nakkiran, Joshua M. Susskind, Etai Littwin:
Vanishing Gradients in Reinforcement Finetuning of Language Models. ICLR 2024 - [c4]Hattie Zhou, Arwen Bradley, Etai Littwin, Noam Razin, Omid Saremi, Joshua M. Susskind, Samy Bengio, Preetum Nakkiran:
What Algorithms can Transformers Learn? A Study in Length Generalization. ICLR 2024 - [i9]Preetum Nakkiran, Arwen Bradley, Hattie Zhou, Madhu Advani:
Step-by-Step Diffusion: An Elementary Tutorial. CoRR abs/2406.08929 (2024) - [i8]Xinting Huang, Andy Yang, Satwik Bhattamishra, Yash Sarrof, Andreas Krebs, Hattie Zhou, Preetum Nakkiran, Michael Hahn:
A Formal Framework for Understanding Length Generalization in Transformers. CoRR abs/2410.02140 (2024) - 2023
- [i7]Pascal Tikeng Notsawo Jr., Hattie Zhou, Mohammad Pezeshki, Irina Rish, Guillaume Dumas:
Predicting Grokking Long Before it Happens: A look into the loss landscape of models which grok. CoRR abs/2306.13253 (2023) - [i6]Hattie Zhou, Arwen Bradley, Etai Littwin, Noam Razin, Omid Saremi, Josh M. Susskind, Samy Bengio, Preetum Nakkiran:
What Algorithms can Transformers Learn? A Study in Length Generalization. CoRR abs/2310.16028 (2023) - [i5]Noam Razin, Hattie Zhou, Omid Saremi, Vimal Thilak, Arwen Bradley, Preetum Nakkiran, Joshua M. Susskind, Etai Littwin:
Vanishing Gradients in Reinforcement Finetuning of Language Models. CoRR abs/2310.20703 (2023) - 2022
- [c3]Hattie Zhou, Ankit Vani, Hugo Larochelle, Aaron C. Courville:
Fortuitous Forgetting in Connectionist Networks. ICLR 2022 - [i4]Hattie Zhou, Ankit Vani, Hugo Larochelle, Aaron C. Courville:
Fortuitous Forgetting in Connectionist Networks. CoRR abs/2202.00155 (2022) - [i3]Hattie Zhou, Azade Nova, Hugo Larochelle, Aaron C. Courville, Behnam Neyshabur, Hanie Sedghi:
Teaching Algorithmic Reasoning via In-context Learning. CoRR abs/2211.09066 (2022)
2010 – 2019
- 2019
- [c2]Hattie Zhou, Janice Lan, Rosanne Liu, Jason Yosinski:
Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask. NeurIPS 2019: 3592-3602 - [c1]Janice Lan, Rosanne Liu, Hattie Zhou, Jason Yosinski:
LCA: Loss Change Allocation for Neural Network Training. NeurIPS 2019: 3614-3624 - [i2]Hattie Zhou, Janice Lan, Rosanne Liu, Jason Yosinski:
Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask. CoRR abs/1905.01067 (2019) - [i1]Janice Lan, Rosanne Liu, Hattie Zhou, Jason Yosinski:
LCA: Loss Change Allocation for Neural Network Training. CoRR abs/1909.01440 (2019)
Coauthor Index
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