22k citations · 26.7k across the 60 of their papers we have counts for
10 papers · 1 filter
Scaling Laws for Sparsely-Connected Foundation Models
Elias Frantar, Carlos Riquelme, Neil Houlsby +2
We explore the impact of parameter sparsity on the scaling behavior of Transformers trained on massive datasets (i.e., "foundation models"), in both vision and language domains. In…
From Sparse to Soft Mixtures of Experts
Joan Puigcerver, Carlos Riquelme, Basil Mustafa +1
Sparse mixture of expert architectures (MoEs) scale model capacity without significant increases in training or inference costs. Despite their success, MoEs suffer from a number of…
Patch n' Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution
Mostafa Dehghani, Basil Mustafa, Josip Djolonga +12
The ubiquitous and demonstrably suboptimal choice of resizing images to a fixed resolution before processing them with computer vision models has not yet been successfully challeng…
Scaling Open-Vocabulary Object Detection
Matthias Minderer, Alexey Gritsenko, Neil Houlsby
Open-vocabulary object detection has benefited greatly from pretrained vision-language models, but is still limited by the amount of available detection training data. While detect…
Image Captioners Are Scalable Vision Learners Too
Michael Tschannen, Manoj Kumar, Andreas Steiner +3
Contrastive pretraining on image-text pairs from the web is one of the most popular large-scale pretraining strategies for vision backbones, especially in the context of large mult…
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…
Scaling Vision Transformers to 22 Billion Parameters
Mostafa Dehghani, Josip Djolonga, Basil Mustafa +39
The scaling of Transformers has driven breakthrough capabilities for language models. At present, the largest large language models (LLMs) contain upwards of 100B parameters. Visio…
Dual PatchNorm
Manoj Kumar, Mostafa Dehghani, Neil Houlsby
We propose Dual PatchNorm: two Layer Normalization layers (LayerNorms), before and after the patch embedding layer in Vision Transformers. We demonstrate that Dual PatchNorm outper…
Massively Scaling Heteroscedastic Classifiers
Mark Collier, Rodolphe Jenatton, Basil Mustafa +3
Heteroscedastic classifiers, which learn a multivariate Gaussian distribution over prediction logits, have been shown to perform well on image classification problems with hundreds…
Adaptive Computation with Elastic Input Sequence
Fuzhao Xue, Valerii Likhosherstov, Anurag Arnab +3
Humans have the ability to adapt the type of information they use, the procedure they employ, and the amount of time they spend when solving problems. However, most standard neural…