4 citations · 6 across the 11 of their papers we have counts for
5 papers · 1 filter
Warmstarting for Scaling Language Models
Neeratyoy Mallik, Maciej Janowski, Johannes Hog +4
Scaling model sizes to scale performance has worked remarkably well for the current large language models paradigm. The research and empirical findings of various scaling studies l…
LMEMs for post-hoc analysis of HPO Benchmarking
Anton Geburek, Neeratyoy Mallik, Danny Stoll +2
The importance of tuning hyperparameters in Machine Learning (ML) and Deep Learning (DL) is established through empirical research and applications, evident from the increase in ne…
Don't Waste Your Time: Early Stopping Cross-Validation
Edward Bergman, Lennart Purucker, Frank Hutter
State-of-the-art automated machine learning systems for tabular data often employ cross-validation; ensuring that measured performances generalize to unseen data, or that subsequen…
In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization
Herilalaina Rakotoarison, Steven Adriaensen, Neeratyoy Mallik +3
With the increasing computational costs associated with deep learning, automated hyperparameter optimization methods, strongly relying on black-box Bayesian optimization (BO), face…
Fast Benchmarking of Asynchronous Multi-Fidelity Optimization on Zero-Cost Benchmarks
Shuhei Watanabe, Neeratyoy Mallik, Edward Bergman +1
While deep learning has celebrated many successes, its results often hinge on the meticulous selection of hyperparameters (HPs). However, the time-consuming nature of deep learning…