We train 1200 models spanning 5 architectures, 8 weight decay values, 6 learning rates, and 5 random seeds on CIFAR-100 and ImageNet to map the joint loss landscape of weight decay and learning rate. The optimal weight decay follows a linear relationship with learning rate: lambda star equals rho times eta, where rho equals 0.
Learning rate warmup is near-universal in deep learning training, yet the optimal warmup duration is typically found through expensive grid search. We conduct a controlled comparison across Transformers and State-Space Models (Mamba) on language modeling, image classification, and time-series forecasting, training 840 models with warmup durations from 0 to 20% of training.
Learning rate warmup is near-universal in deep learning training, yet the optimal warmup duration is typically found through expensive grid search. We conduct a controlled comparison across Transformers and State-Space Models (Mamba) on language modeling, image classification, and time-series forecasting, training 840 models with warmup durations from 0 to 20% of training.
Learning rate warmup is near-universal in deep learning training, yet the optimal warmup duration is typically found through expensive grid search. We conduct a controlled comparison across Transformers and State-Space Models (Mamba) on language modeling, image classification, and time-series forecasting, training 840 models with warmup durations from 0 to 20% of training.