4 papers
Towards Easier-to-Tune Training Algorithms: New Results from AlgoPerf
Priya Kasimbeg, Ahmed Khaled, Aahlad Chandrabhatta +9
Evaluating deep learning optimizer is challenging because confounding factors such as hyperparameter tuning and optimizer-model co-design often obscure true algorithmic performance…
What do near-optimal learning rate schedules look like?
Hiroki Naganuma, Atish Agarwala, Priya Kasimbeg +1
A basic unanswered question in neural network training is: what is the best learning rate schedule shape for a given workload? The choice of learning rate schedule is a key factor…
How far away are truly hyperparameter-free learning algorithms?
Priya Kasimbeg, Vincent Roulet, Naman Agarwal +4
Despite major advances in methodology, hyperparameter tuning remains a crucial (and expensive) part of the development of machine learning systems. Even ignoring architectural choi…
Training neural networks faster with minimal tuning using pre-computed lists of hyperparameters for NAdamW
Sourabh Medapati, Priya Kasimbeg, Shankar Krishnan +2
If we want to train a neural network using any of the most popular optimization algorithms, we are immediately faced with a dilemma: how to set the various optimization and regular…