8 citations · 9 across the 4 of their papers we have counts for
3 papers · 1 filter
Toto 2.0: Time Series Forecasting Enters the Scaling Era
Emaad Khwaja, Chris Lettieri, Gerald Woo +10
We show that time series foundation models scale: a single training recipe produces reliable forecast-quality improvements from 4M to 2.5B parameters. We release Toto 2.0, a family…
Rockmate: an Efficient, Fast, Automatic and Generic Tool for Re-materialization in PyTorch
Xunyi Zhao, Théotime Le Hellard, Lionel Eyraud +2
We propose Rockmate to control the memory requirements when training PyTorch DNN models. Rockmate is an automatic tool that starts from the model code and generates an equivalent m…
Survey on Large Scale Neural Network Training
Julia Gusak, Daria Cherniuk, Alena Shilova +8
Modern Deep Neural Networks (DNNs) require significant memory to store weight, activations, and other intermediate tensors during training. Hence, many models do not fit one GPU de…