CS 7641 Hypothesis | Questions and Answers Latest Updated
2026/2027 | Georgia Institute of Technology
CS7641: Machine Learning
Last Updated: 02/08/26
The following are curated answers generated by the Teaching Staff for the article, “Efficient Activation
Function Optimization through Surrogate Modeling” by Garrett Bingham and Risto Miikkulainen. I have
tried to highlight key details needed for each question. These answers are examples in no particular order.
Question 1
Much like Rule 6 in Ten simple rules for structuring papers by Konrad Kording and Brett Mensh, what is
the gap the authors provide? Please provide 400 words or less.
• For this question, we are really looking for what the author’s claim for the gap as well as some
explanation for why this is the gap. For full completeness, you need to give explanation or
evidence.
Responses:
There are several different problems to construct an optimal activation functions and the existing
activation functions are expensive. Most designs of activation functions are done manually, but this leads
to limitations of humans: design bias and knowledge gaps so small quantity evaluated. An automated
search solves some problems such as the ability to search thousands of possibilities and no bias. The paper
uses 3 steps to make automated activation function construction feasible: development of testing datasets
from scratch, surrogate performance measurement in Fisher Information matrix and activation function
distribution, and automated evaluation of new activation function. The three steps make automated
activation functions are viable and better alternative to manual construction by eliminating drawbacks of
automation.
---
The authors of this paper have identified a gap in existing methods for finding optimal activation functions
in neural networks. Traditional approaches rely on human-designed functions based on intuition or specific
characteristics, but they are limited by human biases and the number of functions that can be evaluated
manually. Automated search methods can evaluate a large number of functions, but they are often
computationally expensive and lack theoretical justification. This leads to inefficient algorithms that may
not find the best solutions as models and datasets become more complex. To address these issues, the
paper proposes a new surrogate-based method for activation function optimization. The authors establish
benchmark datasets containing results from training various neural network architectures with
systematically generated activation functions. They then develop a surrogate model based on the Fisher
, information matrix and activation function output distributions to efficiently predict the performance of
these functions. This surrogate model enables the discovery of new activation functions that outperform
existing ones, challenging the prevailing preference for activation functions like ReLU in deep learning. By
2026/2027 | Georgia Institute of Technology
CS7641: Machine Learning
Last Updated: 02/08/26
The following are curated answers generated by the Teaching Staff for the article, “Efficient Activation
Function Optimization through Surrogate Modeling” by Garrett Bingham and Risto Miikkulainen. I have
tried to highlight key details needed for each question. These answers are examples in no particular order.
Question 1
Much like Rule 6 in Ten simple rules for structuring papers by Konrad Kording and Brett Mensh, what is
the gap the authors provide? Please provide 400 words or less.
• For this question, we are really looking for what the author’s claim for the gap as well as some
explanation for why this is the gap. For full completeness, you need to give explanation or
evidence.
Responses:
There are several different problems to construct an optimal activation functions and the existing
activation functions are expensive. Most designs of activation functions are done manually, but this leads
to limitations of humans: design bias and knowledge gaps so small quantity evaluated. An automated
search solves some problems such as the ability to search thousands of possibilities and no bias. The paper
uses 3 steps to make automated activation function construction feasible: development of testing datasets
from scratch, surrogate performance measurement in Fisher Information matrix and activation function
distribution, and automated evaluation of new activation function. The three steps make automated
activation functions are viable and better alternative to manual construction by eliminating drawbacks of
automation.
---
The authors of this paper have identified a gap in existing methods for finding optimal activation functions
in neural networks. Traditional approaches rely on human-designed functions based on intuition or specific
characteristics, but they are limited by human biases and the number of functions that can be evaluated
manually. Automated search methods can evaluate a large number of functions, but they are often
computationally expensive and lack theoretical justification. This leads to inefficient algorithms that may
not find the best solutions as models and datasets become more complex. To address these issues, the
paper proposes a new surrogate-based method for activation function optimization. The authors establish
benchmark datasets containing results from training various neural network architectures with
systematically generated activation functions. They then develop a surrogate model based on the Fisher
, information matrix and activation function output distributions to efficiently predict the performance of
these functions. This surrogate model enables the discovery of new activation functions that outperform
existing ones, challenging the prevailing preference for activation functions like ReLU in deep learning. By
