Summary of Modl: Multilearner Online Deep Learning, by Antonios Valkanas et al.
MODL: Multilearner Online Deep Learning
by Antonios Valkanas, Boris N. Oreshkin, Mark Coates
First submitted to arxiv on: 28 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposes a hybrid multilearner approach to online deep learning, reconciling the opposing objectives of fast and deep learning. The existing work on pure deep learning solutions is inadequate for handling both aspects simultaneously. The authors develop a fast online logistic regression learner that uses closed-form recursive updates instead of backpropagation. They also analyze the existing online deep learning theory, showing that the widespread ODL approach can be implemented in linear complexity (O(L)) rather than quadratic complexity (O(L^2)). This leads to the cascaded multilearner design, where multiple shallow and deep learners are co-trained to solve the online learning problem. The authors demonstrate that this approach achieves state-of-the-art results on common online learning datasets while handling missing features gracefully. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Online deep learning solves a big problem: how to learn from lots of data quickly and deeply. Most people focus only on making sure it learns deeply, but not as fast. This paper suggests doing things differently by combining two different ways of learning into one. They make a new way of learning that uses closed-form recursive updates instead of backpropagation. This makes it faster to learn. Then they show how to use this idea to make online deep learning work better and more efficiently. The results are really good, and the code is available for anyone to use. |
Keywords
» Artificial intelligence » Backpropagation » Deep learning » Logistic regression » Online learning
