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Found 6 papers, 5 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).
A Central Asian Food Dataset for Personalized Dietary Interventions, Extended Abstract
Nowadays, it is common for people to take photographs of every beverage, snack, or meal they eat and then post these photographs on social media platforms.
A Central Asian Food Dataset for Personalized Dietary Interventions
Nowadays, it is common for people to take photographs of every beverage, snack, or meal they eat and then post these photographs on social media platforms.
Empirical Analysis of the AdaBoost's Error Bound
Understanding the accuracy limits of machine learning algorithms is essential for data scientists to properly measure performance so they can continually improve their models' predictive capabilities.
