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Maksis Knutins
Machine Learning Engineer
@
Imbue (formerly Generally Intelligent)
About Maksis Knutins
Maksis Knutins is a Machine Learning Engineer known for his contributions to hyperparameter optimization and model efficiency.
Known information
Maksis Knutins is a Machine Learning Engineer who has significantly contributed to the field of machine learning through his work on hyperparameter optimization. He played a key role in developing CARBS, a cost-aware hyperparameter optimizer, and co-authored the influential paper ‘Scaling Laws For Every Hyperparameter Via Cost-Aware HPO’. His research has demonstrated the crucial impact of hyperparameter tuning on both model performance and efficiency. Knutins was also involved in reproducing Chinchilla scaling laws for large language models (LLMs) using CARBS. Additionally, he worked on a project that effectively solved OpenAI’s ProcGen benchmark by tuning a simple baseline model, showcasing his expertise in practical machine learning applications.
About Imbue (formerly Generally Intelligent)
Imbue, previously known as Generally Intelligent, is a San Francisco-based company focused on developing AI systems that can reason and code, specifically targeting the B2B engineering, product, and design sectors.
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