Abstract: In this paper, we introduce a novel approach for addressing the multi-objective optimization problem in large language model merging via black-box multi-objective optimization algorithms.
With reported 3x speed gains and limited degradation in output quality, the method targets one of the biggest pain points in production AI systems: latency at scale.
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RMSProp optimization from scratch in Python
Understand and implement the RMSProp optimization algorithm in Python. Essential for training deep neural networks efficiently. #RMSProp #Optimization #DeepLearning Denmark facing "decisive moment" ...
What if you could generate speech so lifelike, it’s almost indistinguishable from a human voice, all without relying on costly, proprietary software? Open source AI voice synthesis has reached a new ...
ABSTRACT: Multi-objective optimization remains a significant and realistic problem in engineering. A trade-off among conflicting objectives subject to equality and inequality constraints is known as ...
Abstract: Multi-party multi-objective optimization, which aims to find a solution set that satisfies multiple decision makers (DMs) as much as possible, has attracted the attention of researchers ...
This repository contains Evolutionary Algorithms that can be used for multi-objective optimization. Interactive optimization is supported. Methods such as RVEA and NSGA-III can be found here.
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