Deep Linear Probe Generators for Weight Space Learning (2410.10811v1)

Abstract: Weight space learning aims to extract information about a neural network, such as its training dataset or generalization error. Recent approaches learn directly from model weights, but this presents many challenges as weights are high-dimensional and include permutation symmetries between neurons. An alternative approach, Probing, represents a model by passing a set of learned inputs (probes) through the model, and training a predictor on top of the corresponding outputs. Although probing is typically not used as a stand alone approach, our preliminary experiment found that a vanilla probing baseline worked surprisingly well. However, we discover that current probe learning strategies are ineffective. We therefore propose Deep Linear Probe Generators (ProbeGen), a simple and effective modification to probing approaches. ProbeGen adds a shared generator module with a deep linear architecture, providing an inductive bias towards structured probes thus reducing overfitting. While simple, ProbeGen performs significantly better than the state-of-the-art and is very efficient, requiring between 30 to 1000 times fewer FLOPs than other top approaches.

• The paper introduces ProbeGen, a deep linear probe generator that dynamically analyzes neural network outputs to reveal insights about weight space properties.
• It achieves significant efficiency gains by reducing FLOPs by 30 to 1000 times compared to traditional weight analysis techniques.
• Empirical results demonstrate that ProbeGen outperforms current methods on datasets like MNIST and CIFAR-10, validating its effectiveness in practical settings.

Deep Linear Probe Generators for Weight Space Learning

The paper "Deep Linear Probe Generators for Weight Space Learning" introduces an innovative approach to learning from neural network weights by employing a methodology known as probing, specifically advancing the technique with a proposal named Deep Linear Probe Generators (ProbeGen). Unlike traditional methods that statically analyze model weights, this paper favors probing strategies—dynamically evaluating models by observing their outputs when subjected to specific inputs.

Overview

Weight space learning is motivated by the need to infer essential attributes about a neural network, such as its generalization capacity or its training dataset, potentially without direct access to the training data itself. The research presented differentiates between mechanistic approaches, where model weights are analyzed in isolation, and probing, where the outputs generated by running certain inputs through the model are examined. Probing sidesteps one of the major issues of weight space learning: permutation symmetries among neurons, which could potentially obfuscate pattern recognition in mechanistic models.

Key Contributions

The key contribution of this work is the development of the ProbeGen technique, which enhances probing by introducing a shared generator module based on a deep linear architecture. The fundamental idea is to establish an inductive bias toward structured probes that naturally result in less overfitting, aiming to embed more meaningful information extraction from the neural networks’ reactions.

1. Deep Linear Architecture: Instead of solely relying on complex non-linear models, the generator adopts a deep linear network structure. This choice effectively regularizes the probes, balancing expressivity and overfitting.
2. Efficiency: One standout result presented is the substantial efficiency improvement, requiring 30 to 1000 times fewer FLOPs compared to competing methods, making ProbeGen computationally attractive.
3. Performance Comparison: The empirical results demonstrate that ProbeGen outperforms state-of-the-art methods in terms of accuracy across several datasets, such as MNIST and CIFAR-10. This is attributed to its ability to effectively capture the essential properties of neural networks by exploiting structured approaches to input generation.

Implications and Future Directions

The implications of successful probing strategies extend well beyond simple neural network analysis. The ability to derive meaningful insights about neural networks through minimal computational effort opens several avenues:

• Black-Box Model Analysis: Dynamic probing methods like ProbeGen can be applied to evaluate black-box models, potentially supporting domains where model internals cannot be disclosed due to privacy or proprietary restrictions.
• Potential for Various Modalities: While the current paper focuses on image-centric data, the application of similar methodologies across different data modalities—such as audio or text—could reduce the barriers to adopting advanced machine learning models across diverse fields.
• Adaptive Probing Techniques: There's potential to develop adaptive versions of probing methods that adjust inputs in real-time based on initial outputs, potentially increasing the accuracy and reducing resources required for probing.

The paper concludes by recognizing limitations, such as the constraints of probing within highly variable output spaces and the challenges of scaling probing methodologies to larger models, marking clear lines for future research.

Overall, "Deep Linear Probe Generators for Weight Space Learning" offers a substantive contribution to the field of dynamic methods for neural network analysis, emphasizing efficiency and effectiveness through structured, linear probe generators.