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Found 22 papers, 10 papers with code
Hierarchical Filtering with Online Learned Priors for ECG Denoising
Electrocardiographic signals (ECG) are used in many healthcare applications, including at-home monitoring of vital signs.
Efficient Out-of-Distribution Detection of Melanoma with Wavelet-based Normalizing Flows
In this work, we aim at using these biases with domain-level knowledge of melanoma, to improve likelihood-based OOD detection of malignant images.
SOM-CPC: Unsupervised Contrastive Learning with Self-Organizing Maps for Structured Representations of High-Rate Time Series
Continuous monitoring with an ever-increasing number of sensors has become ubiquitous across many application domains.
Ultrasound Signal Processing: From Models to Deep Learning
In this work we provide an overview of these methods from the recent literature, and discuss a wide variety of ultrasound applications.
Deep Task-Based Analog-to-Digital Conversion
In this work we design task-oriented ADCs which learn from data how to map an analog signal into a digital representation such that the system task can be efficiently carried out.
Accelerated Intravascular Ultrasound Imaging using Deep Reinforcement Learning
Intravascular ultrasound (IVUS) offers a unique perspective in the treatment of vascular diseases by creating a sequence of ultrasound-slices acquired from within the vessel.
Ultrasound Speckle Suppression and Denoising using MRI-derived Normalizing Flow Priors
We here propose a new unsupervised ultrasound speckle reduction and image denoising method based on maximum-a-posteriori estimation with deep generative priors that are learned from high-quality MRI images.
Deep Proximal Learning for High-Resolution Plane Wave Compounding
Plane Wave imaging enables many applications that require high frame rates, including localisation microscopy, shear wave elastography, and ultra-sensitive Doppler.
Unsupervised Learned Kalman Filtering
In this paper we adapt KalmanNet, which is a recently pro-posed deep neural network (DNN)-aided system whose architecture follows the operation of the model-based Kalman filter (KF), to learn its mapping in an unsupervised manner, i. e., without requiring ground-truth states.
Uncertainty in Data-Driven Kalman Filtering for Partially Known State-Space Models
Providing a metric of uncertainty alongside a state estimate is often crucial when tracking a dynamical system.
RTSNet: Learning to Smooth in Partially Known State-Space Models
However, this model-based algorithm is limited in systems that are only partially known, as well as non-linear and non-Gaussian.
A Review of the Gumbel-max Trick and its Extensions for Discrete Stochasticity in Machine Learning
The Gumbel-max trick is a method to draw a sample from a categorical distribution, given by its unnormalized (log-)probabilities.
DA-MUSIC: Data-Driven DoA Estimation via Deep Augmented MUSIC Algorithm
A popular multi-signal DoA estimation method is the multiple signal classification (MUSIC) algorithm, which enables high-performance super-resolution DoA recovery while being highly applicable in practice.
KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamics
State estimation of dynamical systems in real-time is a fundamental task in signal processing.
Automated Gain Control Through Deep Reinforcement Learning for Downstream Radar Object Detection
Cognitive radars are systems that rely on learning through interactions of the radar with the surrounding environment.
Deep Unfolding with Normalizing Flow Priors for Inverse Problems
Many application domains, spanning from computational photography to medical imaging, require recovery of high-fidelity images from noisy, incomplete or partial/compressed measurements.
Dynamic Probabilistic Pruning: A general framework for hardware-constrained pruning at different granularities
Relevantly, the non-magnitude-based nature of DPP allows for joint optimization of pruning and weight quantization in order to even further compress the network, which we show as well.
Deep probabilistic subsampling for task-adaptive compressed sensing
The field of deep learning is commonly concerned with optimizing predictive models using large pre-acquired datasets of densely sampled datapoints or signals.
Learning Sampling and Model-Based Signal Recovery for Compressed Sensing MRI
Compressed sensing (CS) MRI relies on adequate undersampling of the k-space to accelerate the acquisition without compromising image quality.
Learning Sub-Sampling and Signal Recovery with Applications in Ultrasound Imaging
Compressed sensing emerged as a popular framework for sparse signal reconstruction from a small set of compressed measurements.
Deep Unfolded Robust PCA with Application to Clutter Suppression in Ultrasound
We compare the performance of the suggested deep network on both simulations and in-vivo rat brain scans, with a commonly practiced deep-network architecture and the fast iterative shrinkage algorithm, and show that our architecture exhibits better image quality and contrast.
Super-resolution Ultrasound Localization Microscopy through Deep Learning
This method, which we term Deep Ultrasound Localization Microscopy (Deep-ULM), exploits modern deep learning strategies and employs a convolutional neural network to perform localization microscopy in dense scenarios.
