118 citations · 267 across the 13 of their papers we have counts for
10 papers
PaLI-X: On Scaling up a Multilingual Vision and Language Model
Xi Chen, Josip Djolonga, Piotr Padlewski +40
We present the training recipe and results of scaling up PaLI-X, a multilingual vision and language model, both in terms of size of the components and the breadth of its training t…
End-to-End Spatio-Temporal Action Localisation with Video Transformers
Alexey Gritsenko, Xuehan Xiong, Josip Djolonga +5
The most performant spatio-temporal action localisation models use external person proposals and complex external memory banks. We propose a fully end-to-end, purely-transformer ba…
Intersection of Parallels as an Early Stopping Criterion
Ali Vardasbi, Maarten de Rijke, Mostafa Dehghani
A common way to avoid overfitting in supervised learning is early stopping, where a held-out set is used for iterative evaluation during training to find a sweet spot in the number…
Scaling Laws vs Model Architectures: How does Inductive Bias Influence Scaling?
Yi Tay, Mostafa Dehghani, Samira Abnar +7
There have been a lot of interest in the scaling properties of Transformer models. However, not much has been done on the front of investigating the effect of scaling properties of…
Beyond Transfer Learning: Co-finetuning for Action Localisation
Anurag Arnab, Xuehan Xiong, Alexey Gritsenko +6
Transfer learning is the predominant paradigm for training deep networks on small target datasets. Models are typically pretrained on large ``upstream'' datasets for classification…
Confident Adaptive Language Modeling
Tal Schuster, Adam Fisch, Jai Gupta +5
Recent advances in Transformer-based large language models (LLMs) have led to significant performance improvements across many tasks. These gains come with a drastic increase in th…