We release Code Llama, a family of large language models for code based ...
In this work, we develop and release Llama 2, a collection of pretrained...
We introduce LLaMA, a collection of foundation language models ranging f...
We introduce submodel co-training, a regularization method related to
co...
A Vision Transformer (ViT) is a simple neural architecture amenable to s...
After their initial success in natural language processing, transformer
...
We show how to augment any convolutional network with an attention-based...
Pre-training models on large scale datasets, like ImageNet, is a standar...
The influential Residual Networks designed by He et al. remain the
gold-...
Following their success in natural language processing, transformers hav...
We present ResMLP, an architecture built entirely upon multi-layer
perce...
In this paper, we question if self-supervised learning provides new
prop...
We design a family of image classification architectures that optimize t...
Transformers have been recently adapted for large scale image classifica...
Convolutional architectures have proven extremely successful for vision
...
Recently, neural networks purely based on attention were shown to addres...
This paper tackles the problem of learning a finer representation than t...
We propose a simple architecture to address unpaired image-to-image
tran...
This note complements the paper "Fixing the train-test resolution
discre...
Data-augmentation is key to the training of neural networks for image
cl...