Gaussian mixture models (GMMs) are fundamental to machine learning due to their flexibility as approximating densities. However, uncertainty quantification of GMMs remains a challenge as differential entropy lacks a closed form.

The post-chatbot era has begun. Americans are living in parallel AI universes. For much of the country, AI has come to mean ChatGPT, Google's AI overviews, and the slop that now clogs social-media feeds. Meanwhile, tech hobbyists are becoming radicalized by bots that can work for hours on end, collapsing months of work into weeks, or weeks into an afternoon. Recently, more people have started to play around with tools such as Claude Code .

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Lack of samples per minute or per hour (metrics 1 and 2) are helping to understand 4G networking issues.

However, previous NF-based methods forcibly transform the distribution of all features into a single distribution (e.g., unit normal distribution), even when the features can have locally distinct semantic information and thus follow different

Fine-tuning services for Large Language Models (LLMs) have emerged as a new paradigm.