no code implementations • 15 Oct 2023 • Wenqi Jiang, Marco Zeller, Roger Waleffe, Torsten Hoefler, Gustavo Alonso
The heterogeneity ensures efficient acceleration of both LM inference and retrieval, while the accelerator disaggregation enables the system to independently scale both types of accelerators to fulfill diverse RALM requirements.
no code implementations • 28 May 2023 • Patrik Okanovic, Roger Waleffe, Vasilis Mageirakos, Konstantinos E. Nikolakakis, Amin Karbasi, Dionysis Kalogerias, Nezihe Merve Gürel, Theodoros Rekatsinas
Methods for carefully selecting or generating a small set of training data to learn from, i. e., data pruning, coreset selection, and data distillation, have been shown to be effective in reducing the ever-increasing cost of training neural networks.
1 code implementation • 4 Feb 2022 • Roger Waleffe, Jason Mohoney, Theodoros Rekatsinas, Shivaram Venkataraman
We study training of Graph Neural Networks (GNNs) for large-scale graphs.
1 code implementation • 20 Jan 2021 • Jason Mohoney, Roger Waleffe, Yiheng Xu, Theodoros Rekatsinas, Shivaram Venkataraman
We propose a new framework for computing the embeddings of large-scale graphs on a single machine.
no code implementations • 23 Jun 2020 • Roger Waleffe, Theodoros Rekatsinas
Recent works show that overparameterized networks contain small subnetworks that exhibit comparable accuracy to the full model when trained in isolation.