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Found 20 papers, 7 papers with code
Statistical Linear Regression Approach to Kalman Filtering and Smoothing under Cyber-Attacks
Remote state estimation in cyber-physical systems is often vulnerable to cyber-attacks due to wireless connections between sensors and computing units.
Communication-Efficient Distributed Kalman Filtering using ADMM
The DKF problem is formulated within a distributed optimization framework, where coupling constraints require the exchange of local state and covariance updates between neighboring nodes to achieve consensus.
Gaussian Integral based Bayesian Smoother
This work introduces the Gaussian integration to address a smoothing problem of a nonlinear stochastic state space model.
High-Fidelity Audio Compression with Improved RVQGAN
Language models have been successfully used to model natural signals, such as images, speech, and music.
Tracking an Underwater Target with Unknown Measurement Noise Statistics Using Variational Bayesian Filters
Both adaptive filtering techniques are implemented using the traditional Gaussian approximate filters and are applied to a bearings-only tracking problem illustrated with moderately nonlinear and highly nonlinear scenarios to track a target following a nearly straight line path.
Parametric Scaling of Preprocessing assisted U-net Architecture for Improvised Retinal Vessel Segmentation
We validated the proposed method on retinal fundus images from the DRIVE database.
Application of Top-hat Transformation for Enhanced Blood Vessel Extraction
In the medical domain, different computer-aided diagnosis systems have been proposed to extract blood vessels from retinal fundus images for the clinical treatment of vascular diseases.
Pattern Based Multivariable Regression using Deep Learning (PBMR-DP)
We propose a deep learning methodology for multivariate regression that is based on pattern recognition that triggers fast learning over sensor data.
Wav2CLIP: Learning Robust Audio Representations From CLIP
We propose Wav2CLIP, a robust audio representation learning method by distilling from Contrastive Language-Image Pre-training (CLIP).
Chunked Autoregressive GAN for Conditional Waveform Synthesis
We show that simple pitch and periodicity conditioning is insufficient for reducing this error relative to using autoregression.
NU-GAN: High resolution neural upsampling with GAN
In this paper, we propose NU-GAN, a new method for resampling audio from lower to higher sampling rates (upsampling).
Blood Vessel Detection using Modified Multiscale MF-FDOG Filters for Diabetic Retinopathy
Blindness in diabetic patients caused by retinopathy (characterized by an increase in the diameter and new branches of the blood vessels inside the retina) is a grave concern.
MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis
In this paper, we show that it is possible to train GANs reliably to generate high quality coherent waveforms by introducing a set of architectural changes and simple training techniques.
Automated retinal vessel segmentation based on morphological preprocessing and 2D-Gabor wavelets
In this paper, we suggest a new unsupervised retinal blood vessel segmentation approach using top-hat transformation, contrast-limited adaptive histogram equalization (CLAHE), and 2-D Gabor wavelet filters.
On Difficulties of Probability Distillation
Probability distillation has recently been of interest to deep learning practitioners as it presents a practical solution for sampling from autoregressive models for deployment in real-time applications.
Large-Scale Neuromorphic Spiking Array Processors: A quest to mimic the brain
Neuromorphic engineering (NE) encompasses a diverse range of approaches to information processing that are inspired by neurobiological systems, and this feature distinguishes neuromorphic systems from conventional computing systems.
ObamaNet: Photo-realistic lip-sync from text
We present ObamaNet, the first architecture that generates both audio and synchronized photo-realistic lip-sync videos from any new text.
SampleRNN: An Unconditional End-to-End Neural Audio Generation Model
In this paper we propose a novel model for unconditional audio generation based on generating one audio sample at a time.
Ranked #1 on
Speech Synthesis
on Blizzard Challenge 2013
On Random Weights for Texture Generation in One Layer Neural Networks
Recent work in the literature has shown experimentally that one can use the lower layers of a trained convolutional neural network (CNN) to model natural textures.
PixelVAE: A Latent Variable Model for Natural Images
Natural image modeling is a landmark challenge of unsupervised learning.
