AYLA: Architecting a loss landscape in shallow neural networks to accelerate feature recovery

arXiv:2504.01875 · cs.LG · Submitted 2025-04-02 · Read on arXiv

cs.LG

Submitted: 2025-04-02

Updated: 2026-09-28

Code: https://github.com/keslaki/AYLA

Terminology

• Adam — 30× in this paper · explained in 2 episodes
• Loss Function — 23× in this paper · explained in 2 episodes
• Accuracy — 11× in this paper · explained in 1 episode
• Deep Learning — 11× in this paper
• Robustness — 5× in this paper · explained in 5 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Model-Agnostic — 3× in this paper · explained in 2 episodes
• Stochastic Gradient Descent — 3× in this paper
• Adaptability — 2× in this paper · explained in 1 episode
• Benchmark — 2× in this paper · explained in 2 episodes
• Stochastic Gradient Descent (SGD) — 2× in this paper · explained in 5 episodes
• Activation Function — 1× in this paper · explained in 1 episode
• Adaptive Gradient Methods — 1× in this paper · explained in 1 episode
• Consistency — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Mean Squared Error — 1× in this paper

Sources

• Adam: A Method for Stochastic Optimization
• The Marginal Value of Adaptive Gradient Methods in Machine Learning
• Aggregated Momentum: Stability Through Passive Damping
• Gradient descent with momentum --- to accelerate or to super-accelerate?

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