#neural-tangent-kernel News & Analysis

3 articles tagged with #neural-tangent-kernel. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

3 articles

AIBullisharXiv – CS AI · May 77/10

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Feature Identification via the Empirical NTK

Researchers demonstrate that eigenanalysis of the empirical neural tangent kernel (eNTK) can identify learned feature directions in neural networks, from simple MLPs to large language models like Gemma-3-270M. The method shows strong alignment with known algorithmic features in modular arithmetic tasks and grammatical features in language models, outperforming PCA-based approaches and offering a new mechanistic interpretability tool.

AINeutralarXiv – CS AI · Jun 86/10

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Learning to Execute Graph Algorithms Exactly with Graph Neural Networks

Researchers demonstrate that graph neural networks can learn to execute classical graph algorithms exactly through a two-step training process combining MLPs with NTK theory. The work establishes rigorous theoretical learnability results for distributed computing models and practical algorithms like breadth-first search and Bellman-Ford, advancing understanding of what GNNs can provably learn.

AIBullisharXiv – CS AI · Feb 276/107

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Parameter-Efficient Fine-Tuning for Continual Learning: A Neural Tangent Kernel Perspective

Researchers introduce NTK-CL, a new framework for parameter-efficient fine-tuning in continual learning that uses Neural Tangent Kernel theory to address catastrophic forgetting. The approach achieves state-of-the-art performance by tripling feature representation and implementing adaptive mechanisms to maintain task-specific knowledge while learning new tasks.