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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2405.16255 |
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| _version_ | 1866912404422524928 |
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| author | Eleh, Chinedu Mwanza, Masuzyo Aguegboh, Ekene van Wyk, Hans-Werner |
| author_facet | Eleh, Chinedu Mwanza, Masuzyo Aguegboh, Ekene van Wyk, Hans-Werner |
| contents | The Adam optimization method has achieved remarkable success in addressing contemporary challenges in stochastic optimization. This method falls within the realm of adaptive sub-gradient techniques, yet the underlying geometric principles guiding its performance have remained shrouded in mystery, and have long confounded researchers. In this paper, we introduce GeoAdaLer (Geometric Adaptive Learner), a novel adaptive learning method for stochastic gradient descent optimization, which draws from the geometric properties of the optimization landscape. Beyond emerging as a formidable contender, the proposed method extends the concept of adaptive learning by introducing a geometrically inclined approach that enhances the interpretability and effectiveness in complex optimization scenarios |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_16255 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GeoAdaLer: Geometric Insights into Adaptive Stochastic Gradient Descent Algorithms Eleh, Chinedu Mwanza, Masuzyo Aguegboh, Ekene van Wyk, Hans-Werner Machine Learning Optimization and Control The Adam optimization method has achieved remarkable success in addressing contemporary challenges in stochastic optimization. This method falls within the realm of adaptive sub-gradient techniques, yet the underlying geometric principles guiding its performance have remained shrouded in mystery, and have long confounded researchers. In this paper, we introduce GeoAdaLer (Geometric Adaptive Learner), a novel adaptive learning method for stochastic gradient descent optimization, which draws from the geometric properties of the optimization landscape. Beyond emerging as a formidable contender, the proposed method extends the concept of adaptive learning by introducing a geometrically inclined approach that enhances the interpretability and effectiveness in complex optimization scenarios |
| title | GeoAdaLer: Geometric Insights into Adaptive Stochastic Gradient Descent Algorithms |
| topic | Machine Learning Optimization and Control |
| url | https://arxiv.org/abs/2405.16255 |