Summary of Flexible and Efficient Surrogate Gradient Modeling with Forward Gradient Injection, by Sebastian Otte
Flexible and Efficient Surrogate Gradient Modeling with Forward Gradient Injection
by Sebastian Otte
First submitted to arxiv on: 31 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Neural and Evolutionary Computing (cs.NE)
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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 This paper presents a novel approach to formulating surrogate gradients, called Forward Gradient Injection (FGI), which allows for direct injection of arbitrary gradient shapes into the computational graph during the forward pass. This is particularly important for deep learning frameworks that typically provide custom ways to specify gradients within the computation graph, as seen in PyTorch’s ability to override the backward method. While these methods are common practice and usually work well, they have several disadvantages such as limited flexibility, additional source code overhead, poor usability, or a potentially strong negative impact on automatic model optimization procedures. FGI is demonstrated to be straightforward and convenient, with potential for significant model performance improvements in spiking neural networks (SNNs) using TorchScript. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows a new way to make deep learning models work better by changing how gradients are calculated. Gradients are important because they help train the model to do what we want it to do. The current ways of calculating gradients have some problems, like making the code more complicated or slowing down the training process. This new method, called Forward Gradient Injection (FGI), is a simple and effective way to calculate gradients that can make models work better and faster. |
Keywords
» Artificial intelligence » Deep learning » Optimization
