AINewsnow

Kernel Mean Embeddings: Turning Distributions into Points You Can Compute With

Most of machine learning is built around points. A data point is a vector, a model maps vectors to predictions, and distance between vectors tells you how similar two examples are. Distributions are harder. A distribution is not a point, it is a whole object: a function, a measure, a cloud of possi…

Read the full story at DEV Community — Machine Learning ↗

Timeline · 1 report

  1. 2026-10-31 05:16 · DEV Community — Machine Learning
    Kernel Mean Embeddings: Turning Distributions into Points You Can Compute With

More stories

  1. Malicious Claude Code extension on Cursor marketplace exposed up to 5,000 developers — Microsoft Research Blog
  2. Mistral releases weights for trillion-parameter 'Le Chonk' model — Mistral AI News
  3. Sam Altman says your laptop RAM is a 'terrible’ allocation of scarce resources: Why I think the AI bubble is about to prove him wrong — Tom's Guide AI
  4. S&P Revises Meta's Credit Outlook to Negative on AI Spending Concerns — Bloomberg AI
  5. OpenAI cuts funding target by $10 billion to $20 billion at $1.25 trillion valuation — Financial Times AI
  6. Anthropic files for blockbuster IPO as Blackstone flags likely junk rating — Fast Company AI
  7. Google unveils Gemini 4 Carbon, a cybersecurity-focused frontier model — Google DeepMind Blog
  8. OpenAI, Google, Microsoft and Anthropic to honor shared robots.txt opt-out for AI training — OpenAI News

Get the daily brief of stories like this at 6:30 every morning →