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New research explores fractional optimizers and Adam's principles

By PulseAugur Editorial · [2 sources] ·

Two new research papers explore advancements in neural network optimization techniques. The first paper investigates the interaction between fractional optimizers and fractal activation functions, finding that certain pairings can improve neural network training, particularly when using regularization-style fractional scaling with specific fractal activations. The second paper provides a principled grounding for Adam, a widely used optimizer, by analyzing its convergence properties and its effectiveness with Transformers. This research also introduces Adam-mini, an optimizer that halves Adam's memory footprint while maintaining performance, and offers insights into other optimizers like Muon. AI

IMPACT These papers offer new theoretical and empirical insights into optimizing neural networks, potentially leading to more efficient training and improved model performance.

RANK_REASON Two academic papers published on arXiv detailing novel optimization techniques for neural networks.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New research explores fractional optimizers and Adam's principles

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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Sebastian Raubitzek, Georg Goldenits, Sebastian Schrittwieser, Philip K\"onig, Kevin Mallinger ·

    Fractional Optimizers Meet Fractal Activation Functions: An Empirical Study of Multi-Scale Optimization in Neural Network

    arXiv:2608.14636v1 Announce Type: cross Abstract: Fractional optimization methods and fractal activation functions are two independent directions for improving neural network training. Fractional optimizers extend first-order optimization through fractional derivatives and memory…

  2. arXiv cs.LG TIER_1 English(EN) · Yushun Zhang ·

    On the Principles Behind Neural Network Optimizers

    arXiv:2608.16760v1 Announce Type: new Abstract: Reliable optimization is central to neural network (NN) training, yet Adam, the default optimizer for modern LLMs, rests on a fragile foundation. This thesis develops a principled grounding for Adam and motivates new designs. First,…

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