Summary of A Unified Framework For Neural Computation and Learning Over Time, by Stefano Melacci et al.
A Unified Framework for Neural Computation and Learning Over Time
by Stefano Melacci, Alessandro Betti, Michele Casoni, Tommaso Guidi, Matteo Tiezzi, Marco Gori
First submitted to arxiv on: 18 Sep 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 The proposed Hamiltonian Learning framework offers a novel unified approach for online learning with neural networks from an infinite stream of data, without access to future information. Building on optimal control theory, this method generalizes gradient-based learning in feed-forward and recurrent networks, allowing for novel perspectives. The framework is showcased by recovering gradient-based learning and comparing it to out-of-the-box optimizers, demonstrating its flexibility to switch between local and non-local computational schemes. This paper rethinks the problem of learning over time from scratch, leveraging tools from optimal control theory to yield a unifying view of neural computations and learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Hamiltonian Learning is a new way for computers to learn with neural networks over time, without knowing what’s coming next. It’s like learning how to ride a bike by balancing on two wheels, not looking at the road ahead. This method uses special tools from control theory to make it work. The researchers showed that this method can be used to recover old ideas about learning and even try new ones. They also tested it with different types of computations, like using many devices together. |
Keywords
» Artificial intelligence » Online learning
