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Found 34 papers, 11 papers with code
Kilometer-Scale Convection Allowing Model Emulation using Generative Diffusion Modeling
Storm-scale convection-allowing models (CAMs) are an important tool for predicting the evolution of thunderstorms and mesoscale convective systems that result in damaging extreme weather.
Repulsive Score Distillation for Diverse Sampling of Diffusion Models
For constrained sampling we focus on inverse problems in the latent space that leads to an augmented variational formulation, that strikes a good balance between compute, quality and diversity.
Generative Data Assimilation of Sparse Weather Station Observations at Kilometer Scales
Data assimilation of observational data into full atmospheric states is essential for weather forecast model initialization.
Compositional Text-to-Image Generation with Dense Blob Representations
To leverage the compositionality of large language models (LLMs), we introduce a new in-context learning approach to generate blob representations from text prompts.
DiffObs: Generative Diffusion for Global Forecasting of Satellite Observations
This work presents an autoregressive generative diffusion model (DiffObs) to predict the global evolution of daily precipitation, trained on a satellite observational product, and assessed with domain-specific diagnostics.
AGG: Amortized Generative 3D Gaussians for Single Image to 3D
To overcome these challenges, we introduce an Amortized Generative 3D Gaussian framework (AGG) that instantly produces 3D Gaussians from a single image, eliminating the need for per-instance optimization.
SMRD: SURE-based Robust MRI Reconstruction with Diffusion Models
However, diffusion models require careful tuning of inference hyperparameters on a validation set and are still sensitive to distribution shifts during testing.
Residual Corrective Diffusion Modeling for Km-scale Atmospheric Downscaling
The model is trained to predict 2km data from a regional weather model over Taiwan, conditioned on a 25km global reanalysis.
AutoSamp: Autoencoding k-space Sampling via Variational Information Maximization for 3D MRI
Experiments on public 3D acquired MRI datasets show improved reconstruction quality of the proposed AutoSamp method over the prevailing variable density and variable density Poisson disc sampling for both compressed sensing and deep learning reconstructions.
A Variational Perspective on Solving Inverse Problems with Diffusion Models
To cope with this challenge, we propose a variational approach that by design seeks to approximate the true posterior distribution.
FourCastNet: Accelerating Global High-Resolution Weather Forecasting using Adaptive Fourier Neural Operators
Extreme weather amplified by climate change is causing increasingly devastating impacts across the globe.
GLEAM: Greedy Learning for Large-Scale Accelerated MRI Reconstruction
However, they require several iterations of a large neural network to handle high-dimensional imaging tasks such as 3D MRI.
Unraveling Attention via Convex Duality: Analysis and Interpretations of Vision Transformers
Vision transformers using self-attention or its proposed alternatives have demonstrated promising results in many image related tasks.
FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators
FourCastNet accurately forecasts high-resolution, fast-timescale variables such as the surface wind speed, precipitation, and atmospheric water vapor.
Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformers
AFNO is based on a principled foundation of operator learning which allows us to frame token mixing as a continuous global convolution without any dependence on the input resolution.
Greedy Learning for Large-Scale Neural MRI Reconstruction
Model-based deep learning approaches have recently shown state-of-the-art performance for accelerated MRI reconstruction.
Efficient Token Mixing for Transformers via Adaptive Fourier Neural Operators
AFNO is based on a principled foundation of operator learning which allows us to frame token mixing as a continuous global convolution without any dependence on the input resolution.
Hidden Convexity of Wasserstein GANs: Interpretable Generative Models with Closed-Form Solutions
In this work, we analyze the training of Wasserstein GANs with two-layer neural network discriminators through the lens of convex duality, and for a variety of generators expose the conditions under which Wasserstein GANs can be solved exactly with convex optimization approaches, or can be represented as convex-concave games.
Demystifying Batch Normalization in ReLU Networks: Equivalent Convex Optimization Models and Implicit Regularization
Batch Normalization (BN) is a commonly used technique to accelerate and stabilize training of deep neural networks.
Convex Regularization Behind Neural Reconstruction
Neural networks have shown tremendous potential for reconstructing high-resolution images in inverse problems.
Neural FFTs for Universal Texture Image Synthesis
The conventional CNNs, recently adopted for synthesis, require to train and test on the same set of images and fail to generalize to unseen images.
Learning to Sample MRI via Variational Information Maximization
Accelerating MRI scans requires optimal sampling of k-space data.
Risk Quantification in Deep MRI Reconstruction
Reliable medical image recovery is crucial for accurate patient diagnoses, but little prior work has centered on quantifying uncertainty when using non-transparent deep learning approaches to reconstruct high-quality images from limited measured data.
Spectral Decomposition in Deep Networks for Segmentation of Dynamic Medical Images
Dynamic contrast-enhanced magnetic resonance imaging (DCE- MRI) is a widely used multi-phase technique routinely used in clinical practice.
Wasserstein GANs for MR Imaging: from Paired to Unpaired Training
The reconstruction networks consist of a generator which suppresses the input image artifacts, and a discriminator using a pool of (unpaired) labels to adjust the reconstruction quality.
Degrees of Freedom Analysis of Unrolled Neural Networks
Leveraging the Stein's Unbiased Risk Estimator (SURE), this paper analyzes the generalization risk with its bias and variance components for recurrent unrolled networks.
Compressed Sensing: From Research to Clinical Practice with Data-Driven Learning
Data-driven learning provides a solution to address these challenges.
Uncertainty Quantification in Deep MRI Reconstruction
Reliable MRI is crucial for accurate interpretation in therapeutic and diagnostic tasks.
Neural Proximal Gradient Descent for Compressive Imaging
Recovering high-resolution images from limited sensory data typically leads to a serious ill-posed inverse problem, demanding inversion algorithms that effectively capture the prior information.
Recurrent Generative Adversarial Networks for Proximal Learning and Automated Compressive Image Recovery
Building effective priors is however challenged by the low train and test overhead dictated by real-time tasks; and the need for retrieving visually "plausible" and physically "feasible" images with minimal hallucination.
Deep Generative Adversarial Networks for Compressed Sensing Automates MRI
A multilayer convolutional neural network is then jointly trained based on diagnostic quality images to discriminate the projection quality.
Online Categorical Subspace Learning for Sketching Big Data with Misses
The deterministic Probit and Tobit models treat data as quantized values of an analog-valued process lying in a low-dimensional subspace, while the probabilistic Logit model relies on low dimensionality of the data log-likelihood ratios.
Tracking Tensor Subspaces with Informative Random Sampling for Real-Time MR Imaging
Alteranating majorization minimization is adopted to develop online algorithms that recursively procure the reconstruction upon arrival of a new undersampled $k$-space frame.
Subspace Learning and Imputation for Streaming Big Data Matrices and Tensors
In this context, the present paper permeates benefits from rank minimization to scalable imputation of missing data, via tracking low-dimensional subspaces and unraveling latent (possibly multi-way) structure from \emph{incomplete streaming} data.
