3 citations · 4 across the 3 of their papers we have counts for
3 papers
Is More Data Worth the Cost? Dataset Scaling Laws in a Tiny Attention-Only Decoder
Götz-Henrik Wiegand, Lorena Raichle, Rico Städeli +3
Training Transformer language models is expensive, as performance typically improves with increasing dataset size and computational budget. Although scaling laws describe this tren…
Efficient Neural Network Training via Subset Pretraining
Jan Spörer, Bernhard Bermeitinger, Tomas Hrycej +2
In training neural networks, it is common practice to use partial gradients computed over batches, mostly very small subsets of the training set. This approach is motivated by the…
Reducing the Transformer Architecture to a Minimum
Bernhard Bermeitinger, Tomas Hrycej, Massimo Pavone +2
Transformers are a widespread and successful model architecture, particularly in Natural Language Processing (NLP) and Computer Vision (CV). The essential innovation of this archit…