Random Feature Models with Learnable Activation Functions
learnable activation function;random features;interpretability;statistical learning theory
Yes
No ethics review needed.
See weaknesses.
1. Generalization of Random Feature Models: The authors extend traditional random feature models by introducing learnable activation functions, increasing the model’s expressivity and flexibility.
2. Rigorous Mathematical Foundations: The paper provides a thorough theoretical analysis, including derivations of new kernels and bounds on approximation and generalization, which strengthens the model’s credibility.
3. Clear and Accessible Writing: The paper is well-written, with a logical structure and clear explanations that make complex concepts accessible to readers.
The authors propose the Random Feature Model with Learnable Activation Functions (RFLAF), a generalization of traditional random feature models. In RFLAF, the activation function is represented as a linear combination of radial basis functions (RBFs) with learnable weights, allowing the model to approximate arbitrary activation functions. The authors provide a closed-form kernel analysis for the case when the activation set consists of a single RBF, and they extend the theoretical analysis to scenarios with multiple RBFs. Experimental results on synthetic datasets demonstrate that RFLAF outperforms random feature models with fixed activation functions.
1. Lack of General Closed-Form Solution: While the introduction critiques spline-based models for lacking closed-form analytical kernels (see line 041-046 ), this paper also lacks a closed-form kernel for cases with multiple basis functions. Additionally, despite emphasizing theoretical rigor, the authors rely on the Adam optimizer for learning, suggesting an absence of closed-form solutions in practical application. (Please correct me if I am missing something)

2. Limited Experimental Validation: The model is tested only on synthetic data, with target functions that closely align with the proposed model’s structure. This limited evaluation raises questions about RFLAF’s applicability to real-world scenarios. Validation on more practical datasets, such as tabular data, would strengthen the paper’s claims.

3. Insufficient Exploration of Expressivity and Interpretability: The paper briefly mentions enhanced expressivity and interpretability as benefits of the proposed model, but it does not adequately demonstrate these gains. Showcasing realistic examples would better illustrate the practical advantages in these areas.
Yes
No ethics review needed.
1. Is the main experimental testbed set up in the attention or MLP setting ?
2. What is the impact on speed of inference and training of the newly introduced parameters ?
3. Can the Authors conduct comprehesive experiments with their models in the attention setting, including also various positive random feature map mechanisms for the unbiased softmax kernel estimation (also the variations with learnable parameters) ?
- detailed theoretical analysis of the model with a single radial basis function
- an elegant extension of the regular non-learnable random feature map mechanism
- generalization bounds and sample complexity of learning make the theoretical section even more complete
The paper presents an algorithm for learning activation functions in random feature map representations for neural network models. The authors demonstrate that their learnable variants outperform "static" counterparts, applying fixed nonlinear maps. They also provide theoretical analysis of some of the new random feature map mechanisms they introduce in the paper (Sec. 3: RANDOM FEATURE MODELS WITH A SINGLE RADIAL BASIS FUNCTION). Furthermore, the Authors show that the mechanisms introduced by them needs about twice the parameter number compared to a traditional RF model for substantial quality improvements.
There are several issues I am concerned about:

1. The paper in general is not very well written. The paper talks about learnable random feature map representations, but from reading the experimental section it is not clear whether the conducted experiments are for MLP layers or activation functions in the linear low-rank attention mechanisms for Transformers (that are mentioned in the related-work part). If the latter is true, I am confused why the comparison does not include also positive random feature map mechanisms that are applied to unbiasedly approximate softmax kernel.

2. The idea of the learnable random feature map representation is not new. In fact, there is a vast literature on positive random feature map mechanisms for the unbiased estimation of the softmax kernel and the most general of those mechanisms do indeed have learnable parameters.

3. The experimental section is very compact. It is really hard to draw any far-reaching conclusions regarding the performance of the method, based on the presented results. Besides, as mentioned above, they are poorly reported.

4. The Authors claim that the mechanisms presented by them introduces about twice the parameter number compared to a traditional RF model for substantial quality gains. This is actually a lot. In the paper it is not reported how this affects speed of training and inference. It is also not clear whether the comparison in the experimental section is with the models of approximately the same of two times smaller number of parameters.
Yes
No ethics review needed.
- The discussion after Theorem 5.1 mentions the question of tightness of the bounds on the number of features required in Rudi & Rosasco; the authors might find the corresponding commentary in https://arxiv.org/abs/2405.15699 to be of interest.
The paper is clearly written, and the mathematics appears correct (though mostly consisting of slight extensions of standard results). However, as I detail below under *Weaknesses*, I'm struggling to muster much enthusiasm for this paper because I think it fails to answer the central question of why one would use these models rather than a standard MLP.
This paper proposes a class of random feature models with activation functions formed by trainable superpositions of radial basis functions.
As mentioned above, I have one major concern with this paper, which overshadows everything else. I will thus be brief. In its current form, the manuscript does not provide any compelling reason why one would use the proposed class of RF models with adaptive activation functions rather than just training the weights (i.e., using an MLP). The experiments provide no comparison against an MLP baseline, and I'm not convinced by the authors' argument that their models will be more interpretable. To understand the nature of signal processing by these networks, one must understand the filtering properties of the random weights as well as the activation function. The theoretical results do not outweigh these concerns, as they are mostly minor modifications of standard RF model analyses. Without any clear comparison to an ordinary neural network, this is for me a clear reject, without further critique required.
We study the random feature model with learnable activation functions, demonstrate its enhanced expressivity and interpretability and derive theoretical results of learning.
@inproceedings{
anonymous2024random,
title={Random Feature Models with Learnable Activation Functions},
author={Anonymous},
booktitle={Submitted to The Thirteenth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=Pghg8dJnUe},
note={under review}
}
Current random feature models typically rely on fixed activation functions, limiting their ability to capture diverse patterns in data. To address this, we introduce the Random Feature model with Learnable Activation Functions (RFLAF), a novel model that significantly enhances the expressivity and interpretability of traditional random feature (RF) models. We begin by studying the RF model with a single radial basis function, where we discover a new kernel and provide the first theoretical analysis on it. By integrating the basis functions with learnable weights, we show that RFLAF can represent a broad class of random feature models whose activation functions belong in $C_c(\mathbb{R})$. Theoretically, we prove that the model requires only about twice the parameter number compared to a traditional RF model to achieve the significant leap in expressivity. Experimentally, RFLAF demonstrates two key advantages: (1) it performs better across various tasks compared to traditional RF model with the same number of parameters, and (2) the optimized weights offer interpretability, as the learned activation function can be directly inferred from these weights. Our model paves the way for developing more expressive and interpretable frameworks within random feature models.
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learnable activation function random features interpretability statistical learning theory
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learning theory
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Random Feature Models with Learnable Activation Functions
ICLR 2025 Conference Submission
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