Summary of Less Is More: Rethinking Few-shot Learning and Recurrent Neural Nets, by Deborah Pereg et al.
Less is More: Rethinking Few-Shot Learning and Recurrent Neural Nets
by Deborah Pereg, Martin Villiger, Brett Bouma, Polina Golland
First submitted to arxiv on: 28 Sep 2022
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 machine learning framework assumes a joint probability distribution between input-output pairs based on the training dataset. The learner aims to output a prediction rule learned from these pairs. This work explores the asymptotic equipartition property (AEP) in machine learning, highlighting its implications for few-shot learning. It provides theoretical guarantees for reliable learning under AEP and generalization error with respect to sample size. The authors propose a reduced-entropy algorithm for few-shot learning using a highly efficient recurrent neural network (RNN) framework. They also provide a mathematical intuition for the RNN as an approximation of a sparse coding solver. Experimental results demonstrate significant potential for improving learning models’ sample efficiency, generalization, and time complexity, making it suitable for practical real-time applications like image deblurring and optical coherence tomography (OCT) speckle suppression. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how machine learning works better. It shows that some problems can be solved quickly by looking at just a few examples, which is important because computers are getting faster and we need them to learn fast too. The researchers came up with new ideas for making computer programs learn faster and more accurately using something called recurrent neural networks (RNNs). They tested these ideas on images and medical scans and found that they worked really well. |
Keywords
* Artificial intelligence * Few shot * Generalization * Machine learning * Neural network * Probability * Rnn
