Why don't machine learning research agents overfit?
amazon.science
Why don't machine learning research agents overfit?
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Re: Why don't machine learning research agents overfit?
#2> Machine learning, at its core, is about generalization, not memorization.
Well they memorize the patterns.
memorization doesnt mean rote learning.
Re: Why don't machine learning research agents overfit?
#3Even tech giants are putting out articles seemingly fully written by Claude.
Re: Why don't machine learning research agents overfit?
#4Even tech giants are putting out articles seemingly fully written by Claude.
The animated graphic labeled "Occam's razor, formalized" is bizarre. Is that really visualizing "Occam's razor, formalized"?
Re: Why don't machine learning research agents overfit?
#5arXiv link: https://arxiv.org/abs/2606.11045
Re: Why don't machine learning research agents overfit?
#6Even tech giants are putting out articles seemingly fully written by Claude.
The animated graphic labeled "Occam's razor, formalized" is bizarre. Is that really visualizing "Occam's razor, formalized"?
Ah, over-the-top larger-than-life LLM-isms, they are really funny when you see them in a company blog, but they are vomitive when it's your coworker copy-pasting it and insisting you on reading it.
Re: Why don't machine learning research agents overfit?
#7[flagged]
Re: Why don't machine learning research agents overfit?
#8I always get annoyed when people misinterpret Occam’s razor. It’s not that the simplest is more likely to be correct, it’s that you should prefer it, because it’s simple.
It’s just like the Hopper quote. She said it’s better to ask for forgiveness during the fog of war, doing something you thought was right, not to do something you knew they were going to say no to and now you are trying to get away with something.
Re: Why don't machine learning research agents overfit?
#9They tend not to overfit ... when there are way more data points than parameters.
Re: Why don't machine learning research agents overfit?
#10> Machine learning, at its core, is about generalization, not memorization. Well they memorize the patterns. memorization doesnt mean rote learning.
That's a bit pedantic no. Memorization in ML refers to the model having the wrong level of capacity such that it's too hard to optimise it such that it doesn't memorize the _training examples_ themselves.