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Found 3 papers, 2 papers with code
Gradient Descent Fails to Learn High-frequency Functions and Modular Arithmetic
In the novel framework, the hardness of a class is usually quantified by the variance of the gradient with respect to a random choice of a target function.
Intractability of Learning the Discrete Logarithm with Gradient-Based Methods
The discrete logarithm problem is a fundamental challenge in number theory with significant implications for cryptographic protocols.
Long-Tail Theory under Gaussian Mixtures
We suggest a simple Gaussian mixture model for data generation that complies with Feldman's long tail theory (2020).
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