Search Results for author:
Found 17 papers, 6 papers with code
Improving Perceptual Quality, Intelligibility, and Acoustics on VoIP Platforms
In this paper, we present a method for fine-tuning models trained on the Deep Noise Suppression (DNS) 2020 Challenge to improve their performance on Voice over Internet Protocol (VoIP) applications.
TAPLoss: A Temporal Acoustic Parameter Loss for Speech Enhancement
We propose an objective for perceptual quality based on temporal acoustic parameters.
PAAPLoss: A Phonetic-Aligned Acoustic Parameter Loss for Speech Enhancement
We can add this criterion as an auxiliary loss to any model that produces speech, to optimize speech outputs to match the values of clean speech in these features.
Quantitative Planning with Action Deception in Concurrent Stochastic Games
In this setup, we investigate P1's strategic planning of action deception that decides when to deviate from the Nash equilibrium in P2's game model and employ a hidden action, so that P1 can maximize the value of action deception, which is the additional payoff compared to P1's payoff in the game where P2 has complete information.
What is the Solution for State-Adversarial Multi-Agent Reinforcement Learning?
Additionally, we propose a Robust Multi-Agent Adversarial Actor-Critic (RMA3C) algorithm to learn robust policies for MARL agents under state uncertainties.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Masked Autoencoders for Low dose CT denoising
A plethora of transformer models have been developed recently to improve LDCT image quality.
A Robust and Constrained Multi-Agent Reinforcement Learning Framework for Electric Vehicle AMoD Systems
We then propose a robust and constrained MARL algorithm (ROCOMA) that trains a robust EV rebalancing policy to balance the supply-demand ratio and the charging utilization rate across the whole city under state transition uncertainty.
Deep filter bank regression for super-resolution of anisotropic MR brain images
In 2D multi-slice magnetic resonance (MR) acquisition, the through-plane signals are typically of lower resolution than the in-plane signals.
Disentangling A Single MR Modality
Disentangling anatomical and contrast information from medical images has gained attention recently, demonstrating benefits for various image analysis tasks.
Coordinate Translator for Learning Deformable Medical Image Registration
The majority of deep learning (DL) based deformable image registration methods use convolutional neural networks (CNNs) to estimate displacement fields from pairs of moving and fixed images.
A Secure and Efficient Federated Learning Framework for NLP
In this work, we consider the problem of designing secure and efficient federated learning (FL) frameworks.
RCNN-SliceNet: A Slice and Cluster Approach for Nuclei Centroid Detection in Three-Dimensional Fluorescence Microscopy Images
Robust and accurate nuclei centroid detection is important for the understanding of biological structures in fluorescence microscopy images.
MR Slice Profile Estimation by Learning to Match Internal Patch Distributions
To super-resolve the through-plane direction of a multi-slice 2D magnetic resonance (MR) image, its slice selection profile can be used as the degeneration model from high resolution (HR) to low resolution (LR) to create paired data when training a supervised algorithm.
CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentation
The analysis shows that the performance of DL models for single modality (CT / MR) can show reliable volumetric analysis performance (DICE: 0. 98 $\pm$ 0. 00 / 0. 95 $\pm$ 0. 01) but the best MSSD performance remain limited (21. 89 $\pm$ 13. 94 / 20. 85 $\pm$ 10. 63 mm).
Center-Extraction-Based Three Dimensional Nuclei Instance Segmentation of Fluorescence Microscopy Images
Fluorescence microscopy is an essential tool for the analysis of 3D subcellular structures in tissue.
Three dimensional blind image deconvolution for fluorescence microscopy using generative adversarial networks
Due to image blurring image deconvolution is often used for studying biological structures in fluorescence microscopy.
Three Dimensional Fluorescence Microscopy Image Synthesis and Segmentation
Advances in fluorescence microscopy enable acquisition of 3D image volumes with better image quality and deeper penetration into tissue.
