Summary of Permutative Redundancy and Uncertainty Of the Objective in Deep Learning, by Vacslav Glukhov
Permutative redundancy and uncertainty of the objective in deep learning
by Vacslav Glukhov
First submitted to arxiv on: 11 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 explores the implications of uncertain objective functions and permutative symmetry on traditional deep learning architectures. It reveals that these architectures are plagued by an astronomical number of equivalent global and local optima, making local optima unattainable. As network size increases, the optimization landscape likely becomes a complex web of valleys and ridges. The authors propose several remedies to reduce or eliminate ghost optima, including forced pre-pruning, re-ordering, ortho-polynomial activations, and modular bio-inspired architectures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how traditional deep learning models can be affected by uncertainty in the objective function and symmetry in the architecture. The main idea is that these models have a huge number of possible solutions, which makes it hard to find the best one. As the model gets bigger, the landscape of possible solutions becomes really complicated. To fix this problem, the authors suggest some new ways to design models, like pruning parts of the network, rearranging how it works, using special activation functions, and building models that are inspired by nature. |
Keywords
» Artificial intelligence » Deep learning » Objective function » Optimization » Pruning
