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Hattie Zhou: Lottery Tickets and Algorithmic Reasoning in LLMs

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Feb 16, 2023

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Hattie Zhou

01:42:59

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ANECDOTE

Hattie's Research Origin Story

Hattie Zhou transitioned from finance to machine learning after seeking to automate stock valuation.
A talk at Uber AI Labs inspired her focus on understanding neural networks deeply.

insights

INSIGHT

Lottery Ticket Hypothesis Explained

The lottery ticket hypothesis says lucky subnetworks exist in random initial networks that can train well alone.
Magnitude pruning reveals these subnetworks by selecting large-magnitude weights post-training for rewinding.

insights

INSIGHT

Masking Is Like Training

Masking weights can act like a training step, moving pruned weights towards values they'd reach if fully trained.
Pruned zeroed weights are not useless but complementary to the surviving subnetworks.

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What Got You Interested in Machine Learning?

01:45 • 3min

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Is It a Good Idea to Be a Scientist?

04:56 • 3min

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Empirical Theory, Understanding Deep Learning

07:34 • 2min

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The Lottery Ticket Hypothesis

09:48 • 3min

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Pre-Training and Initialization of a Lottery Ticket

13:16 • 3min

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Rewinding a Lottery Ticket

16:29 • 6min

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How Do You Set Weights to Zero?

22:04 • 4min

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Is There a Way to Do Random Pruning?

26:27 • 2min

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The Sign of Weights in the Super Mask

28:43 • 2min

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Is the Basin of Attraction Really Large?

30:49 • 2min

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How Much Precision Do You Need in the Weights?

32:46 • 5min

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Is There a Difference Between a Weight Change and a Mask Search?

38:15 • 3min

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Is Masking a Way to Stimulate Pre-Trained Large Language Models?

40:48 • 4min

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Using Masking in Multitask Learning

45:02 • 2min

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Can Fortuitous Remembering Be Used in Artificial Neural Networks?

47:28 • 3min

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Retraining the Later Layers to Improve Transfer Learning Performance?

50:39 • 3min

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The Relationship Between Training Loss and Grokking the Information Bottleneck Paper

53:48 • 4min

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The Inductive Bias Picture Is Really Interesting

58:06 • 2min

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Can We Develop Algorithms That Capture the Same Benefit Without Iterative Process?

59:43 • 4min

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Teaching Algorithmic Reasoning via in-Context Learning

01:03:19 • 5min

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The in-Context Learning Picture

01:08:22 • 5min

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Induction Head Hypothesis - What Is Happening in in-Context Learning?

01:13:32 • 3min

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In-Context Learning and Gradient Decading

01:16:25 • 5min

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Is There a Strategy for Discovering Novel Algorithms?

01:21:31 • 3min

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What's Next for You in Line With What You Began Investigating in This Paper?

01:24:41 • 2min

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What I Saw at the Math and AI Workshop at NeurIPS

01:26:14 • 2min

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Is There Still a Need for Scaling Regimens for Iterative Training?

01:27:45 • 5min

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Is the Human Model Interaction Really Important?

01:32:50 • 5min

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The Current Publishing Culture in ML

01:38:05 • 5min

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In episode 60 of The Gradient Podcast, Daniel Bashir speaks to Hattie Zhou.

Hattie is a PhD student at the Université de Montréal and Mila. Her research focuses on understanding how and why neural networks work, based on the belief that the performance of modern neural networks exceeds our understanding and that building more capable and trustworthy models requires bridging this gap. Prior to Mila, she spent time as a data scientist at Uber and did research with Uber AI Labs.

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Outline:

* (00:00) Intro

* (01:55) Hattie’s Origin Story, Uber AI Labs, empirical theory and other sorts of research

* (10:00) Intro to the Lottery Ticket Hypothesis & Deconstructing Lottery Tickets

* (14:30) Lottery tickets as lucky initialization

* (17:00) Types of masking and the “masking is training” claim

* (24:00) Type-0 masks and weight evolution over long training trajectories

* (27:00) Can you identify good masks or training trajectories a priori?

* (29:00) The role of signs in neural net initialization

* (35:27) The Supermask

* (41:00) Masks to probe pretrained models and model steerability

* (47:40) Fortuitous Forgetting in Connectionist Networks

* (54:00) Relationships to other work (double descent, grokking, etc.)

* (1:01:00) The iterative training process in fortuitous forgetting, scale and value of exploring alternatives

* (1:03:35) In-Context Learning and Teaching Algorithmic Reasoning

* (1:09:00) Learning + algorithmic reasoning, prompting strategy

* (1:13:50) What’s happening with in-context learning?

* (1:14:00) Induction heads

* (1:17:00) ICL and gradient descent

* (1:22:00) Algorithmic prompting vs discovery

* (1:24:45) Future directions for algorithmic prompting

* (1:26:30) Interesting work from NeurIPS 2022

* (1:28:20) Hattie’s perspective on scientific questions people pay attention to, underrated problems

* (1:34:30) Hattie’s perspective on ML publishing culture

* (1:42:12) Outro

Links:

* Hattie’s homepage and Twitter

* Papers

* Deconstructing Lottery Tickets: Zeros, signs, and the Supermask

* Fortuitous Forgetting in Connectionist Networks

* Teaching Algorithmic Reasoning via In-context Learning

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