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Found 10 papers, 5 papers with code
Multistep Consistency Models
By increasing the sample budget from a single step to 2-8 steps, we can train models more easily that generate higher quality samples, while retaining much of the sampling speed benefits.
Rolling Diffusion Models
Diffusion models have recently been increasingly applied to temporal data such as video, fluid mechanics simulations, or climate data.
Patch n' Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution
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 challenged.
Robustmix: Improving Robustness by Regularizing the Frequency Bias of Deep Nets
Deep networks have achieved impressive results on a range of well-curated benchmark datasets.
Scaling Vision Transformers to 22 Billion Parameters
The scaling of Transformers has driven breakthrough capabilities for language models.
Ranked #1 on
Zero-Shot Transfer Image Classification
on ObjectNet
Simple diffusion: End-to-end diffusion for high resolution images
Currently, applying diffusion models in pixel space of high resolution images is difficult.
Ranked #4 on
Conditional Image Generation
on ImageNet 128x128
Efficiently Scaling Transformer Inference
We study the problem of efficient generative inference for Transformer models, in one of its most challenging settings: large deep models, with tight latency targets and long sequence lengths.
Skillful Twelve Hour Precipitation Forecasts using Large Context Neural Networks
An emerging class of weather models based on neural networks represents a paradigm shift in weather forecasting: the models learn the required transformations from data instead of relying on hand-coded physics and are computationally efficient.
MetNet: A Neural Weather Model for Precipitation Forecasting
Weather forecasting is a long standing scientific challenge with direct social and economic impact.
Bayesian Inference for Large Scale Image Classification
We show that ATMC is intrinsically robust to overfitting on the training data and that ATMC provides a better calibrated measure of uncertainty compared to the optimization baseline.
