Do you want to open a PR? You can definitely add more sampling functions to `keras.random`
@fchollet
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Keras Compiles to JAX, TensorFlow, PyTorch with JAX Recommended
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Keras code compiles to JAX, TensorFlow, or PyTorch code, so anything you can do with one of these frameworks you can do with Keras. I recommend using JAX as your backend, as it is suitable for very-large scale computation and can be easily parallelized across many devices.
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GPU acceleration and XLA compilation limitations in framework integration
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But then you would not be able to run it on GPU, nor compile to XLA for a ~5x speedup, nor use it inside a TensorFlow codebase or a PyTorch codebase.
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Keras ops API: NumPy syntax across JAX PyTorch TensorFlow
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A neat feature of Keras is that its `keras.ops` API follows the NumPy API syntax exactly. And since it runs on JAX, PyTorch, TensorFlow, and NumPy itself, you can use Keras to write GPU-accelerated NumPy code that runs with any framework.
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Critique of flawed paper on model interpolation definition
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I've seen a paper that argued that, but it was utterly idiotic since it used "L2 interpolation in encoding space" as its definition for "interpolation". Effectively, it was saying, "models that aren't linear aren't linear".
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Regularization as a Prior: Simplicity Without Extrapolation
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Regularization is a prior towards simplicity. It is not a domain-specific prior and does not enable extrapolation on its own.
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Broken Control Mechanisms and Misaligned Incentives
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It will happen regardless, because the incentives are stacked this way, and because control mechanisms (peer review and social validation) are broken or non existent.
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LLM-Generated Papers Risk Slowing Scientific Progress
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What's holding back research isn't a lack of verbose, low-signal, high-noise papers. Using LLMs to automatically generate 100x more of those will not accelerate science, it will slow it down.
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Search vs Interpolation: Sampling from Actual Game Space
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You aren't sampling from a learned manifold of the game space, you are sampling from the *actual* game space. You aren't in any way limited by what you've previously learned. That's the difference between search and interpolation.
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LLM-generated training data differs from actual game data like AlphaGo
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No, I'm talking about using a LLM to generate LLM training data, ungrounded (with no selection by a non-DL system). AlphaGo game data is actual game data, it isn't generated by a LLM.