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EvE optimizer offers faster alternative to Adam for neural network searches

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced EvE (Evolutionary Explorer), a novel optimizer designed to be a faster alternative to Adam for hyperparameter and architecture searches in neural networks. EvE utilizes a population-based differential evolution approach with a targeted Adam fallback, significantly reducing the cost per iteration while maintaining competitive performance. In evaluations across various benchmarks and neural network tasks, EvE demonstrated faster completion times for searches and comparable or slightly lower final quality compared to Adam, making it a promising tool for budget-constrained optimization scenarios. AI

IMPACT EvE could accelerate hyperparameter tuning and architecture search, potentially speeding up the development cycle for complex AI models.

RANK_REASON The item describes a new research paper detailing a novel optimization algorithm for neural networks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.NE (Neural & Evolutionary) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

EvE optimizer offers faster alternative to Adam for neural network searches

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The item describes a new research paper detailing a novel optimization algorithm for neural networks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.NE (Neural & Evolutionary) TIER_1 Deutsch(DE) · Kalyanmoy Deb ·

    EvE: An Alternate Optimizer to Adam

    Adam and its variants dominate neural network training, but a single run only reveals whether a configuration works well after most of its budget is spent, a poor fit for hyperparameter or architecture search, where configurations must be ranked cheaply and pruned early. We intro…

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