Summary of Brownne: Brownian Nonlocal Neurons & Activation Functions, by Sriram Nagaraj and Truman Hickok
BrowNNe: Brownian Nonlocal Neurons & Activation Functions
by Sriram Nagaraj, Truman Hickok
First submitted to arxiv on: 21 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Numerical Analysis (math.NA)
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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 paper investigates the relationship between stochastic activation functions and the generalization abilities of deep learning models. Specifically, it aims to provide a theoretical foundation for the heuristic that using stochastic activation functions leads to superior generalization performance. The authors introduce a new notion of nonlocal directional derivative and analyze its properties, including existence and convergence. They also show that nonlocal derivatives are epsilon-sub gradients and derive sample complexity results for stochastic gradient descent-like methods. Additionally, they demonstrate that Brownian motion infused ReLU activation functions can be used to improve generalization performance in low-training data regimes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper explores how using random activation functions in deep learning models can help them generalize better, especially when there’s limited training data. The authors develop a new mathematical concept called nonlocal directional derivative and use it to understand why random activation functions are more effective. They also test their ideas on various deep learning architectures and find that they indeed perform better than traditional deterministic methods. |
Keywords
» Artificial intelligence » Deep learning » Generalization » Relu » Stochastic gradient descent
