Do Machine Learning Models Memorize or Generalize?
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Do Machine Learning Models Memorize or Generalize?
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Re: Do Machine Learning Models Memorize or Generalize?
#2hierarchize would be a better term than generalize
Re: Do Machine Learning Models Memorize or Generalize?
#3hierarchize would be a better term than generalize
Anything would be better than "grokking".
From what I gather they're talking about double descent which afaik is the consequence of overparameterization leading to a smooth interpolation between the training data as opposed to what happens in traditional overfitting. Imagine a polynomial fit with the same degree as the number of data points (swinging up and down wildly away from the data) compared with a much higher degree fit that could smoothly interpolate between the points while still landing right on them.
None of this is what I would call generalization, it's good interpolation, which is what deep learning does in a very high dimensional space. It's notoriously awful at extrapolating, ie generalizing to anything without support in the training data.
Re: Do Machine Learning Models Memorize or Generalize?
#4[deleted]
Re: Do Machine Learning Models Memorize or Generalize?
#5It seems the take home is weight decay induces sparsity which helps learn the "true" representation rather than an overfit one. It's interesting the human brain has a comparable mechanism prevalent in development [1]. I would love to know from someone in the field if this was the inspiration for weight decay (or presumably just the more equivalent nn pruning [2]).
[1] https://en.wikipedia.org/wiki/Synaptic_pruning [2] https://en.wikipedia.org/wiki/Pruning_(artificial_neural_net...
Re: Do Machine Learning Models Memorize or Generalize?
#6Does anyone know how that charts are created ?
I bet that it's half generated by some sort of library and them manually improved but the generated animated SVG are beautiful.
Re: Do Machine Learning Models Memorize or Generalize?
#7hierarchize would be a better term than generalize
Anything would be better than "grokking". From what I gather they're talking about double descent which afaik is the consequence of overparameterization leading to a smooth interpolation between the training data as opposed to what happens in traditional overfitting. Imagine a polynomial fit with the same degree as the number of data points (swinging up and down wildly away from the data) compared with a much higher…
double descent is a different phenomenon from grokking
Re: Do Machine Learning Models Memorize or Generalize?
#8It seems the take home is weight decay induces sparsity which helps learn the "true" representation rather than an overfit one. It's interesting the human brain has a comparable mechanism prevalent in development [1]. I would love to know from someone in the field if this was the inspiration for weight decay (or presumably just the more equivalent nn pruning [2]). [1] https://en.wikipedia.org/wiki/Synaptic_pruning [2…
The inspiration for weight decay was to reduce the capacity to memorize of the model until it perfectly fits the complexity of the task, not more not less. A model more complex than the task is over-fitting, the other one is under-fitting. Got to balance them out.
But the best cure for over-fitting is to make the dataset larger and ensure data diversity. LLMs have datasets so large they usually train one epoch.
Re: Do Machine Learning Models Memorize or Generalize?
#9It seems the take home is weight decay induces sparsity which helps learn the "true" representation rather than an overfit one. It's interesting the human brain has a comparable mechanism prevalent in development [1]. I would love to know from someone in the field if this was the inspiration for weight decay (or presumably just the more equivalent nn pruning [2]). [1] https://en.wikipedia.org/wiki/Synaptic_pruning [2…
The human brain has synaptic pruning. The exact purpose of it is theorized but not actually understood, and it's a gigantic leap to assume some sort of analogous mechanism between LLMs and the human brain.
Re: Do Machine Learning Models Memorize or Generalize?
#10hierarchize would be a better term than generalize
Generalize is seeing common principles, patterns, between disparate instances of a phenomena. It's a proper word for this.