Summary of Custom Gradient Estimators Are Straight-through Estimators in Disguise, by Matt Schoenbauer et al.
Custom Gradient Estimators are Straight-Through Estimators in Disguise
by Matt Schoenbauer, Daniele Moro, Lukasz Lew, Andrew Howard
First submitted to arxiv on: 8 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel insight is presented in this paper, which reveals that certain weight gradient estimators are equivalent to the straight-through estimator (STE) when the learning rate is sufficiently small. This equivalence holds for a large class of estimators and various adaptive learning rate algorithms, including Adam. The findings demonstrate that after swapping out the original gradient estimator with the STE and adjusting the weight initialization and learning rate in stochastic gradient descent (SGD), the model’s training behavior remains unchanged. The paper experimentally verifies these results using a small convolutional neural network trained on MNIST and a ResNet50 model trained on ImageNet. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research shows that when you’re teaching machines to learn, some ways of calculating how much to change their weights are actually the same if you’re going slow enough. This means that you can use a simpler method called the straight-through estimator (STE) and still get the same results as using more complex methods. The researchers tested this idea with two different types of machine learning models and found that it worked for both. |
Keywords
» Artificial intelligence » Machine learning » Neural network » Stochastic gradient descent
