1.5k citations · 5.7k across the 49 of their papers we have counts for
52 papers
OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models
Anas Awadalla, Irena Gao, Josh Gardner +13
We introduce OpenFlamingo, a family of autoregressive vision-language models ranging from 3B to 9B parameters. OpenFlamingo is an ongoing effort to produce an open-source replicati…
On the Connection between Pre-training Data Diversity and Fine-tuning Robustness
Vivek Ramanujan, Thao Nguyen, Sewoong Oh +2
Pre-training has been widely adopted in deep learning to improve model performance, especially when the training data for a target task is limited. In our work, we seek to understa…
Objaverse-XL: A Universe of 10M+ 3D Objects
Matt Deitke, Ruoshi Liu, Matthew Wallingford +14
Natural language processing and 2D vision models have attained remarkable proficiency on many tasks primarily by escalating the scale of training data. However, 3D vision tasks hav…
Improving Multimodal Datasets with Image Captioning
Thao Nguyen, Samir Yitzhak Gadre, Gabriel Ilharco +2
Massive web datasets play a key role in the success of large vision-language models like CLIP and Flamingo. However, the raw web data is noisy, and existing filtering methods to re…
Are aligned neural networks adversarially aligned?
Nicholas Carlini, Milad Nasr, Christopher A. Choquette-Choo +8
Large language models are now tuned to align with the goals of their creators, namely to be "helpful and harmless." These models should respond helpfully to user questions, but ref…
Neural Priming for Sample-Efficient Adaptation
Matthew Wallingford, Vivek Ramanujan, Alex Fang +5
We propose Neural Priming, a technique for adapting large pretrained models to distribution shifts and downstream tasks given few or no labeled examples. Presented with class names…