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Research#llm📝 BlogAnalyzed: Dec 27, 2025 17:32

Validating Validation Sets

Published:Dec 27, 2025 16:16
1 min read
r/MachineLearning

Analysis

This article discusses a method for validating validation sets, particularly when dealing with small sample sizes. The core idea involves resampling different holdout choices multiple times to create a histogram, allowing users to assess the quality and representativeness of their chosen validation split. This approach aims to address concerns about whether the validation set is effectively flagging overfitting or if it's too perfect, potentially leading to misleading results. The provided GitHub link offers a toy example using MNIST, suggesting the principle's potential for broader application pending rigorous review. This is a valuable exploration for improving the reliability of model evaluation, especially in data-scarce scenarios.
Reference

“This exploratory, p-value-adjacent approach to validating the data universe (train and hold out split) resamples different holdout choices many times to create a histogram to shows where your split lies.”

Permalinkr/MachineLearning
Research#llm📝 BlogAnalyzed: Jan 3, 2026 07:11

Is ChatGPT an N-gram model on steroids?

Published:Aug 15, 2024 05:42
1 min read
ML Street Talk Pod

Analysis

The article discusses a research paper analyzing transformer models, like those used in ChatGPT, through the lens of n-gram statistics. It highlights a method for understanding model predictions without delving into internal mechanisms, a technique for detecting overfitting, and observations on curriculum learning. The article also touches upon philosophical aspects of AI behavior description versus explanation.
Reference

“Dr. Timothy Nguyen discusses his recent paper on understanding transformers through n-gram statistics.”

PermalinkML Street Talk Pod