Based on What We Can Control Artificial Neural Networks
Cheng Kang, Xujing Yao
TL;DR
This paper introduces a control systems approach to analyze and improve the stability and efficiency of artificial neural networks by simulating their system responses and understanding the impact of various factors.
Contribution
It presents a novel systematic analysis method for ANNs using control theory, enabling better design of optimizers and learning systems.
Findings
Analyzing ANNs as control systems reveals factors affecting stability.
Simulating system responses helps optimize hyperparameters.
The method can guide the development of new optimizers.
Abstract
How can the stability and efficiency of Artificial Neural Networks (ANNs) be ensured through a systematic analysis method? This paper seeks to address that query. While numerous factors can influence the learning process of ANNs, utilizing knowledge from control systems allows us to analyze its system function and simulate system responses. Although the complexity of most ANNs is extremely high, we still can analyze each factor (e.g., optimiser, hyperparameters) by simulating their system response. This new method also can potentially benefit the development of new optimiser and learning system, especially when discerning which components adversely affect ANNs. Controlling ANNs can benefit from the design of optimiser and learning system, as (1) all optimisers act as controllers, (2) all learning systems operate as control systems with inputs and outputs, and (3) the optimiser should…
Peer Reviews
Decision·Submitted to ICLR 2024
- I like that more recent neural network architectures such as forward-forward neural network are considered - In general the view of neural network training from the perspective of control and the respective methodology (like PID and fuzzy controllers) is of interest - In general the paper tries to adress different domains, such as GAN-like training, vision, and feedforward neural networks
- The paper is written very poorly. It is very difficult to understand what the intentions of the the authors are. There are countless examples, starting with the title: "Based on What We Can Control Artificial Neural Networks" Other examples include for instance "To analyze the learning progress of most ANNs, for example, CNN using backpropagation algorithm, FFNN using forward-forward algorithm, and GAN such a generative model using random noise to generate sample" (page 2) - I don't see how
+ Neural network training, or understanding why neural network training works is an interesting question. + The interpretation in the paper is interesting and uses control theoretic ideas that are well-known.
- The technical problem considered in the paper is too simple to represent training of neural networks in my opinion. In particular, it assumes there is a single equilibrium, $\theta^*$, and the feedback is in the error between $\theta$ and $\theta^*$. A better model would be that we have the output $y(\theta)$, for example, the loss on the training set. But there are many $\theta$'s that give the same loss. For example, suppose that we can make the training loss 0, and $y(\theta_1*)=y(\theta_2*
The control system perspective on the ANN training process is nice, although it is not new. Combining PID controller and fuzzy logic is novel to the best of my knowledge. There is a potential that the Fuzzy PID controller can actually improve the training process.
The presentation quality of the current manuscript can be further improved. The novelty of this paper is weak. The idea of interpreting ANN training as controller design is not new. The idea of Fuzzy PID is new but incremental compared to [Wang2020]. In addition, the authors did not show the advantage of Fuzzy PID compared to other optimizers in the numerical section. It would be great if the authors could provide a rigorous proof of the convergence of the training algorithms through the lens
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Taxonomy
TopicsNeural Networks and Applications · Fault Detection and Control Systems · Fuzzy Logic and Control Systems