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Found 4 papers, 4 papers with code
Towards Measuring Representational Similarity of Large Language Models
Understanding the similarity of the numerous released large language models (LLMs) has many uses, e. g., simplifying model selection, detecting illegal model reuse, and advancing our understanding of what makes LLMs perform well.
Similarity of Neural Network Models: A Survey of Functional and Representational Measures
Measuring similarity of neural networks to understand and improve their behavior has become an issue of great importance and research interest.
On the Prediction Instability of Graph Neural Networks
Instability of trained models, i. e., the dependence of individual node predictions on random factors, can affect reproducibility, reliability, and trust in machine learning systems.
The Effects of Randomness on the Stability of Node Embeddings
We systematically evaluate the (in-)stability of state-of-the-art node embedding algorithms due to randomness, i. e., the random variation of their outcomes given identical algorithms and graphs.
