6 citations · 10 across the 4 of their papers we have counts for
5 papers
Zephon: Elastic Determinism for Online, Stateful Foundation Model Data Loading Pipelines
Maximilian Böther, Josh Wills, Ties Robroek +14
Deterministic data loading is important for foundation model development: model researchers need confidence that differences they observe across costly ablations are caused by the…
20/20 Vision Language Models: A Prescription for Better VLMs through Data Curation Alone
DatologyAI, :, Siddharth Joshi +32
Data curation has shifted the quality-compute frontier for language-model and contrastive image-text pretraining, but its role for vision-language models (VLMs) is far less establi…
TensorSocket: Shared Data Loading for Deep Learning Training
Ties Robroek, Neil Kim Nielsen, Pınar Tözün
Training deep learning models is a repetitive and resource-intensive process. Data scientists often train several models before landing on a set of parameters (e.g., hyper-paramete…
Modyn: Data-Centric Machine Learning Pipeline Orchestration
Maximilian Böther, Ties Robroek, Viktor Gsteiger +4
In real-world machine learning (ML) pipelines, datasets are continuously growing. Models must incorporate this new training data to improve generalization and adapt to potential di…
An Analysis of Collocation on GPUs for Deep Learning Training
Ties Robroek, Ehsan Yousefzadeh-Asl-Miandoab, Pınar Tözün
Deep learning training is an expensive process that extensively uses GPUs, but not all model training saturates modern powerful GPUs. Multi-Instance GPU (MIG) is a new technology i…