Summary of Revisit Micro-batch Clipping: Adaptive Data Pruning Via Gradient Manipulation, by Lun Wang
Revisit Micro-batch Clipping: Adaptive Data Pruning via Gradient Manipulation
by Lun Wang
First submitted to arxiv on: 29 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Sound (cs.SD); Audio and Speech Processing (eess.AS)
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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 explanation is provided for the observed enhancement in auto-speech recognition (ASR) model performance by micro-batch clipping, a gradient clipping method. By assuming specific training samples impede model convergence during certain phases, convergence analysis reveals that micro-batch clipping improves convergence rate asymptotically with an additional constant bias. This bias is dependent on factors and can be minimized at specific micro-batch sizes, elucidating the sweet-spot phenomenon. The effectiveness of micro-batch clipping is also demonstrated beyond ASR models on vision and language models, with promising performance gains. However, limitations are identified when training data originates from multiple distinct domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new study explains why a technique called micro-batch clipping can make speech recognition models better. It’s like finding the right temperature for your brain to learn new things. The researchers found that some training samples can slow down learning at certain points, but micro-batch clipping helps speed it back up. They also tested this technique on other types of models and saw improvements there too. However, they discovered that this technique doesn’t work as well when the training data comes from many different sources. |
Keywords
* Artificial intelligence * Temperature
