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Found 15 papers, 5 papers with code
Progressive Monitoring of Generative Model Training Evolution
While deep generative models (DGMs) have gained popularity, their susceptibility to biases and other inefficiencies that lead to undesirable outcomes remains an issue.
A Plug-and-Play Method for Guided Multi-contrast MRI Reconstruction based on Content/Style Modeling
Furthermore, its practicality is demonstrated on the NYU fastMRI DICOM dataset and two in-house multi-coil raw datasets, obtaining up to 32. 6% more acceleration over learning-based non-guided reconstruction for a given SSIM.
NeRF-CA: Dynamic Reconstruction of X-ray Coronary Angiography with Extremely Sparse-views
These techniques enforce the separation of the coronary artery from the background by enforcing dynamic structure sparsity and scene smoothness.
EvolvED: Evolutionary Embeddings to Understand the Generation Process of Diffusion Models
To address these gaps, we introduce EvolvED, a method that presents a holistic view of the iterative generative process in diffusion models.
Unraveling the Temporal Dynamics of the Unet in Diffusion Models
We propose an analytical method to systematically assess the impact of time steps and core Unet components on the final output.
Class-constrained t-SNE: Combining Data Features and Class Probabilities
Data features and class probabilities are two main perspectives when, e. g., evaluating model results and identifying problematic items.
Image Quality Assessment for Magnetic Resonance Imaging
Moreover, the selection of these IQA metrics for a specific task typically involves intentionally induced distortions, such as manually added noise or artificial blurring; yet, the chosen metrics are then used to judge the output of real-life computer vision models.
MimicBot: Combining Imitation and Reinforcement Learning to win in Bot Bowl
This paper describe an hybrid agent trained to play in Fantasy Football AI which participated in the Bot Bowl III competition.
Active Deep Probabilistic Subsampling
Subsampling a signal of interest can reduce costly data transfer, battery drain, radiation exposure and acquisition time in a wide range of problems.
An Adaptive Intelligence Algorithm for Undersampled Knee MRI Reconstruction
In this work, we present the application of adaptive intelligence to accelerate MR acquisition.
Learning to Predict Error for MRI Reconstruction
In this paper, we demonstrate that predictive uncertainty estimated by the current methods does not highly correlate with prediction error by decomposing the latter into random and systematic errors, and showing that the former is equivalent to the variance of the random error.
Progressive Data Science: Potential and Challenges
We discuss these challenges and outline first steps towards progressiveness, which, we argue, will ultimately help to significantly speed-up the overall data science process.
Differentiable Image Parameterizations
Typically, we parameterize the input image as the RGB values of each pixel, but that isn’t the only way.
GPGPU Linear Complexity t-SNE Optimization
The t-distributed Stochastic Neighbor Embedding (tSNE) algorithm has become in recent years one of the most used and insightful techniques for the exploratory data analysis of high-dimensional data.
Approximated and User Steerable tSNE for Progressive Visual Analytics
Progressive Visual Analytics aims at improving the interactivity in existing analytics techniques by means of visualization as well as interaction with intermediate results.
