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Phase Transition from Random Features to Fully Trainable Networks | AI Learning Platform

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Phase Transition from Random Features to Fully Trainable Networks

Xin He, Huan Lao, Xiliang Lu et al.1 min readarXivOct 22, 2026

Key Takeaway for Students

Constructing accurate and computationally tractable approximation spaces is a fundamental task in scientific computing. Partially trainable neural networks bridge fixed random features and fully adaptive representations. This paper quantifies a phase transition pattern in approximation bounds and observes analogous behavior in training errors: a rapid but smooth initial improve

Reading in Plain English mode — toggle below to switch

This paper provides theoretical insights into the trade-offs between fixed random features and fully trainable networks, a common dilemma in practical ML. Understanding the phase transition in approximation bounds helps ML engineers make informed decisions about model complexity and training strategies.

Read original on arXiv

Career Insight

medium demand

Why it matters

Skills to learn

Analyze approximation bounds for neural networksImplement and evaluate partially trainable network architecturesInterpret phase transition phenomena in model trainingOptimize initialization strategies for neural networksApply spectral decay analysis to model performance

Interview questions

Explain the concept of a phase transition in the context of neural network training, as described in this paper. How does it relate to the trade-off between random features and fully trainable layers?
Given a two-layer network, how would you determine the optimal proportion of trainable parameters (P) to balance approximation accuracy and computational cost, based on the insights from this research?
Design a system for training large neural networks where computational resources are limited, and you need to leverage partial training. How would you decide which layers to freeze and which to train, and what metrics would you use to evaluate your strategy?

Relevant for ~18 months · generated by the enrichment pipeline