1 code implementation • 31 Oct 2023 • Noam Razin, Hattie Zhou, Omid Saremi, Vimal Thilak, Arwen Bradley, Preetum Nakkiran, Joshua Susskind, Etai Littwin
Pretrained language models are commonly aligned with human preferences and downstream tasks via reinforcement finetuning (RFT), which refers to maximizing a (possibly learned) reward function using policy gradient algorithms.
no code implementations • 24 Oct 2023 • Hattie Zhou, Arwen Bradley, Etai Littwin, Noam Razin, Omid Saremi, Josh Susskind, Samy Bengio, Preetum Nakkiran
Large language models exhibit surprising emergent generalization properties, yet also struggle on many simple reasoning tasks such as arithmetic and parity.
no code implementations • 23 Jun 2023 • Pascal Jr. Tikeng Notsawo, Hattie Zhou, Mohammad Pezeshki, Irina Rish, Guillaume Dumas
In essence, by studying the learning curve of the first few epochs, we show that one can predict whether grokking will occur later on.
no code implementations • 15 Nov 2022 • Hattie Zhou, Azade Nova, Hugo Larochelle, Aaron Courville, Behnam Neyshabur, Hanie Sedghi
Large language models (LLMs) have shown increasing in-context learning capabilities through scaling up model and data size.
1 code implementation • ICLR 2022 • Hattie Zhou, Ankit Vani, Hugo Larochelle, Aaron Courville
Forgetting is often seen as an unwanted characteristic in both human and machine learning.
2 code implementations • NeurIPS 2019 • Janice Lan, Rosanne Liu, Hattie Zhou, Jason Yosinski
We propose a new window into training called Loss Change Allocation (LCA), in which credit for changes to the network loss is conservatively partitioned to the parameters.
6 code implementations • NeurIPS 2019 • Hattie Zhou, Janice Lan, Rosanne Liu, Jason Yosinski
The recent "Lottery Ticket Hypothesis" paper by Frankle & Carbin showed that a simple approach to creating sparse networks (keeping the large weights) results in models that are trainable from scratch, but only when starting from the same initial weights.