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Found 5 papers, 2 papers with code
Chameleon: a heterogeneous and disaggregated accelerator system for retrieval-augmented language models
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.
Repeated Random Sampling for Minimizing the Time-to-Accuracy of Learning
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.
MariusGNN: Resource-Efficient Out-of-Core Training of Graph Neural Networks
We study training of Graph Neural Networks (GNNs) for large-scale graphs.
Marius: Learning Massive Graph Embeddings on a Single Machine
We propose a new framework for computing the embeddings of large-scale graphs on a single machine.
Principal Component Networks: Parameter Reduction Early in Training
Recent works show that overparameterized networks contain small subnetworks that exhibit comparable accuracy to the full model when trained in isolation.
