Deep Linear Probe Generators for Weight Space Learning

2024-10-14

International Conference on Learning Representations

AI Summary

Direct inference of training/generalization error from model weights remains challenging due to high dimensionality and neuron permutation symmetry in weight-space learning. Method: We propose ProbeGen—a deep linear probe generator that introduces a shared, deep linear generative module to inject structural inductive bias into input probes, thereby substantially mitigating overfitting inherent in conventional probe learning. By analyzing the output responses of structured probes via forward propagation, ProbeGen achieves efficient representation of the weight space. Contribution/Results: Across multiple benchmarks, ProbeGen outperforms state-of-the-art methods with 30–1000× lower computational cost (significantly reduced FLOPs) and enhanced robustness. To our knowledge, this is the first work to systematically integrate structured probe generation with weight-space representation learning, establishing a novel paradigm for model diagnosis and generalization analysis.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Generation
• Search and Optimization: Learning to Search

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Improving probe learning strategies for neural network analysis
• Addressing ineffectiveness of current weight space probing methods
• Reducing computational costs while maintaining model performance

Innovation

• Deep linear probe generators reduce overfitting
• Shared generator module creates structured probes
• Method requires significantly fewer computational operations