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Found 9 papers, 1 papers with code
WAVEQ: GRADIENT-BASED DEEP QUANTIZATION OF NEURAL NETWORKS THROUGH SINUSOIDAL REGULARIZATION
We show how WaveQ balance compute efficiency and accuracy, and provide a heterogeneous bitwidth assignment for quantization of a large variety of deep networks (AlexNet, CIFAR-10, MobileNet, ResNet-18, ResNet-20, SVHN, and VGG-11) that virtually preserves the accuracy.
WaveQ: Gradient-Based Deep Quantization of Neural Networks through Sinusoidal Adaptive Regularization
We show how SINAREQ balance compute efficiency and accuracy, and provide a heterogeneous bitwidth assignment for quantization of a large variety of deep networks (AlexNet, CIFAR-10, MobileNet, ResNet-18, ResNet-20, SVHN, and VGG-11) that virtually preserves the accuracy.
Block based refitting in $\ell_{12}$ sparse regularisation
This is done through the use of refitting block penalties that only act on the support of the estimated solution.
Machine learning in acoustics: theory and applications
Acoustic data provide scientific and engineering insights in fields ranging from biology and communications to ocean and Earth science.
Image denoising with generalized Gaussian mixture model patch priors
In this paper, we show that a generalized Gaussian mixture model (GGMM) captures the underlying distribution of patches better than a GMM.
Parameter Estimation in Finite Mixture Models by Regularized Optimal Transport: A Unified Framework for Hard and Soft Clustering
The proposed framework unifies hard and soft clustering methods for general mixture models.
Accelerating GMM-based patch priors for image restoration: Three ingredients for a 100$\times$ speed-up
Image restoration methods aim to recover the underlying clean image from corrupted observations.
Characterizing the maximum parameter of the total-variation denoising through the pseudo-inverse of the divergence
Though, it is of importance when tuning the regularization parameter as it allows fixing an upper-bound on the grid for which the optimal parameter is sought.
