Search Results for author:
Found 14 papers, 10 papers with code
Language models scale reliably with over-training and on downstream tasks
We fit scaling laws that extrapolate in both the number of model parameters and the ratio of training tokens to parameters.
OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models
We introduce OpenFlamingo, a family of autoregressive vision-language models ranging from 3B to 9B parameters.
Ranked #14 on
Visual Question Answering (VQA)
on InfiMM-Eval
DataComp: In search of the next generation of multimodal datasets
Multimodal datasets are a critical component in recent breakthroughs such as Stable Diffusion and GPT-4, yet their design does not receive the same research attention as model architectures or training algorithms.
A Comparative Study on Generative Models for High Resolution Solar Observation Imaging
Starting from StyleGAN-based methods, we uncover severe deficits of this model family in handling fine-scale details of solar images when training on high resolution samples, contrary to training on natural face images.
Reproducible scaling laws for contrastive language-image learning
To address these limitations, we investigate scaling laws for contrastive language-image pre-training (CLIP) with the public LAION dataset and the open-source OpenCLIP repository.
Ranked #1 on
Zero-Shot Image Classification
on Country211
(using extra training data)
Towards prediction of turbulent flows at high Reynolds numbers using high performance computing data and deep learning
In this paper, deep learning (DL) methods are evaluated in the context of turbulent flows.
LAION-5B: An open large-scale dataset for training next generation image-text models
We show successful replication and fine-tuning of foundational models like CLIP, GLIDE and Stable Diffusion using the dataset, and discuss further experiments enabled with an openly available dataset of this scale.
LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs
Multi-modal language-vision models trained on hundreds of millions of image-text pairs (e. g.
JUWELS Booster -- A Supercomputer for Large-Scale AI Research
In this article, we present JUWELS Booster, a recently commissioned high-performance computing system at the J\"ulich Supercomputing Center.
Effect of Pre-Training Scale on Intra- and Inter-Domain Full and Few-Shot Transfer Learning for Natural and Medical X-Ray Chest Images
We then conduct supervised pre-training, while varying network size and source data scale and domain, being either large natural (ImageNet-1k/21k) or large medical chest X-Ray datasets, and transfer pre-trained models to different natural or medical targets.
Ranked #2 on
Image Classification
on Oxford-IIIT Pet Dataset
Generalization over different cellular automata rules learned by a deep feed-forward neural network
To test generalization ability of a class of deep neural networks, we randomly generate a large number of different rule sets for 2-D cellular automata (CA), based on John Conway's Game of Life.
Adversarial domain adaptation to reduce sample bias of a high energy physics classifier
We apply adversarial domain adaptation in unsupervised setting to reduce sample bias in a supervised high energy physics events classifier training.
Obstacle Tower Without Human Demonstrations: How Far a Deep Feed-Forward Network Goes with Reinforcement Learning
The Obstacle Tower Challenge is the task to master a procedurally generated chain of levels that subsequently get harder to complete.
Using Physics-Informed Super-Resolution Generative Adversarial Networks for Subgrid Modeling in Turbulent Reactive Flows
Reasons for this are the large amount of degrees of freedom in realistic flows, the high requirements with respect to accuracy and error robustness, as well as open questions, such as the generalization capability of trained neural networks in such high-dimensional, physics-constrained scenarios.
