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Found 26 papers, 16 papers with code
InvertibleNetworks.jl: A Julia package for scalable normalizing flows
InvertibleNetworks. jl is a Julia package designed for the scalable implementation of normalizing flows, a method for density estimation and sampling in high-dimensional distributions.
Self-Consuming Generative Models Go MAD
Seismic advances in generative AI algorithms for imagery, text, and other data types has led to the temptation to use synthetic data to train next-generation models.
Martian time-series unraveled: A multi-scale nested approach with factorial variational autoencoders
This problem is inherently ill-posed and is further challenged by the variety of timescales exhibited by sources.
Refining Amortized Posterior Approximations using Gradient-Based Summary Statistics
We validate our method in a controlled setting by applying it to a stylized problem, and observe improved posterior approximations with each iteration.
Learned multiphysics inversion with differentiable programming and machine learning
We present the Seismic Laboratory for Imaging and Modeling/Monitoring (SLIM) open-source software framework for computational geophysics and, more generally, inverse problems involving the wave-equation (e. g., seismic and medical ultrasound), regularization with learned priors, and learned neural surrogates for multiphase flow simulations.
Amortized Normalizing Flows for Transcranial Ultrasound with Uncertainty Quantification
Our method combines physics-informed methods and data-driven methods to accelerate the reconstruction of the final image.
Unearthing InSights into Mars: Unsupervised Source Separation with Limited Data
Source separation involves the ill-posed problem of retrieving a set of source signals that have been observed through a mixing operator.
TITAN: Bringing The Deep Image Prior to Implicit Representations
We study the interpolation capabilities of implicit neural representations (INRs) of images.
Boomerang: Local sampling on image manifolds using diffusion models
As implied by its name, Boomerang local sampling involves adding noise to an input image, moving it closer to the latent space, and then mapping it back to the image manifold through a partial reverse diffusion process.
Reliable amortized variational inference with physics-based latent distribution correction
While generic and applicable to other inverse problems, by means of a linearized seismic imaging example, we show that our correction step improves the robustness of amortized variational inference with respect to changes in the number of seismic sources, noise variance, and shifts in the prior distribution.
Ultra-Low-Bitrate Speech Coding with Pretrained Transformers
Our numerical experiments show that supplementing the convolutional encoder of a neural speech codec with Transformer speech embeddings yields a speech codec with a bitrate of $600\,\mathrm{bps}$ that outperforms the original neural speech codec in synthesized speech quality when trained at the same bitrate.
Velocity continuation with Fourier neural operators for accelerated uncertainty quantification
As such, the main contribution of this work is a survey-specific Fourier neural operator surrogate to velocity continuation that maps seismic images associated with one background model to another virtually for free.
Learned coupled inversion for carbon sequestration monitoring and forecasting with Fourier neural operators
We show that we can accurately use a Fourier neural operator as a proxy for the fluid-flow simulator for a fraction of the computational cost.
Photoacoustic imaging with conditional priors from normalizing flows
For many ill-posed inverse problems, such as photoacoustic imaging, the uncertainty of the solution is highly affected by measurement noise and data incompleteness (due, for example, to limited aperture).
Deep Bayesian inference for seismic imaging with tasks
We propose to use techniques from Bayesian inference and deep neural networks to translate uncertainty in seismic imaging to uncertainty in tasks performed on the image, such as horizon tracking.
Low-memory stochastic backpropagation with multi-channel randomized trace estimation
Thanks to the combination of state-of-the-art accelerators and highly optimized open software frameworks, there has been tremendous progress in the performance of deep neural networks.
Learning by example: fast reliability-aware seismic imaging with normalizing flows
To arrive at this result, we train the NF on pairs of low- and high-fidelity migrated images.
Preconditioned training of normalizing flows for variational inference in inverse problems
Obtaining samples from the posterior distribution of inverse problems with expensive forward operators is challenging especially when the unknowns involve the strongly heterogeneous Earth.
Faster Uncertainty Quantification for Inverse Problems with Conditional Normalizing Flows
In inverse problems, we often have access to data consisting of paired samples $(x, y)\sim p_{X, Y}(x, y)$ where $y$ are partial observations of a physical system, and $x$ represents the unknowns of the problem.
Parameterizing uncertainty by deep invertible networks, an application to reservoir characterization
Uncertainty quantification for full-waveform inversion provides a probabilistic characterization of the ill-conditioning of the problem, comprising the sensitivity of the solution with respect to the starting model and data noise.
Transfer learning in large-scale ocean bottom seismic wavefield reconstruction
Because different frequency slices share information, we propose the use the method of transfer training to make our approach computationally more efficient by warm starting the training with CNN weights obtained from a neighboring frequency slices.
Weak deep priors for seismic imaging
The chief advantage of this approach is that the updates for the CNN weights do not involve the modeling operator, and become relatively cheap.
Geophysics Image and Video Processing
Uncertainty quantification in imaging and automatic horizon tracking: a Bayesian deep-prior based approach
In this paper, we focus on how UQ trickles down to horizon tracking for the determination of stratigraphic models and investigate its sensitivity with respect to the imaging result.
A deep-learning based Bayesian approach to seismic imaging and uncertainty quantification
Uncertainty quantification is essential when dealing with ill-conditioned inverse problems due to the inherent nonuniqueness of the solution.
Neural network augmented wave-equation simulation
One proxy of incomplete physics is an inaccurate discretization of Laplacian in simulation of wave equation via finite-difference method.
Learned imaging with constraints and uncertainty quantification
We outline new approaches to incorporate ideas from deep learning into wave-based least-squares imaging.
