research
∙
09/15/2023
Replacing softmax with ReLU in Vision Transformers
Previous research observed accuracy degradation when replacing the atten...
share
research
∙
08/02/2023
OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models
We introduce OpenFlamingo, a family of autoregressive vision-language mo...
research
∙
04/27/2023
DataComp: In search of the next generation of multimodal datasets
Large multimodal datasets have been instrumental in recent breakthroughs...
research
∙
04/25/2023
Stable and low-precision training for large-scale vision-language models
We introduce new methods for 1) accelerating and 2) stabilizing training...
research
∙
02/27/2023
The Role of Pre-training Data in Transfer Learning
The transfer learning paradigm of model pre-training and subsequent fine...
research
∙
12/14/2022
Reproducible scaling laws for contrastive language-image learning
Scaling up neural networks has led to remarkable performance across a wi...
research
∙
12/08/2022
Editing Models with Task Arithmetic
Changing how pre-trained models behave – e.g., improving their performan...
research
∙
10/22/2022
Exploring The Landscape of Distributional Robustness for Question Answering Models
We conduct a large empirical evaluation to investigate the landscape of ...
research
∙
10/19/2022
lo-fi: distributed fine-tuning without communication
When fine-tuning large neural networks, it is common to use multiple nod...
research
∙
10/16/2022
LAION-5B: An open large-scale dataset for training next generation image-text models
Groundbreaking language-vision architectures like CLIP and DALL-E proved...
research
∙
08/10/2022
Patching open-vocabulary models by interpolating weights
Open-vocabulary models like CLIP achieve high accuracy across many image...
research
∙
08/10/2022
Quality Not Quantity: On the Interaction between Dataset Design and Robustness of CLIP
Web-crawled datasets have enabled remarkable generalization capabilities...
research
∙
05/03/2022
Data Determines Distributional Robustness in Contrastive Language Image Pre-training (CLIP)
Contrastively trained image-text models such as CLIP, ALIGN, and BASIC h...
research
∙
03/20/2022
CLIP on Wheels: Zero-Shot Object Navigation as Object Localization and Exploration
Households across the world contain arbitrary objects: from mate gourds ...
research
∙
03/10/2022
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
The conventional recipe for maximizing model accuracy is to (1) train mu...
research
∙
09/04/2021
Robust fine-tuning of zero-shot models
Large pre-trained models such as CLIP offer consistent accuracy across a...
research
∙
02/20/2021
Learning Neural Network Subspaces
Recent observations have advanced our understanding of the neural networ...
research
∙
11/30/2020
Deconstructing the Structure of Sparse Neural Networks
Although sparse neural networks have been studied extensively, the focus...
research
∙
06/26/2020
Supermasks in Superposition
We present the Supermasks in Superposition (SupSup) model, capable of se...
research
∙
02/08/2020
Soft Threshold Weight Reparameterization for Learnable Sparsity
Sparsity in Deep Neural Networks (DNNs) is studied extensively with the ...
research
∙
11/29/2019
What's Hidden in a Randomly Weighted Neural Network?
Training a neural network is synonymous with learning the values of the ...
research
∙
06/03/2019
Discovering Neural Wirings
The success of neural networks has driven a shift in focus from feature ...
research
∙
12/03/2018