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Found 10 papers, 3 papers with code
Deconvolution in Fluorescence Lifetime imaging microscopy (FLIM)
Fluorescence lifetime imaging microscopy (FLIM) is an important technique to understand the chemical micro-environment in cells and tissues since it provides additional contrast compared to conventional fluorescence imaging.
Towards Understanding Why Lookahead Generalizes Better Than SGD and Beyond
Specifically, we prove that lookahead using SGD as its inner-loop optimizer can better balance the optimization error and generalization error to achieve smaller excess risk error than vanilla SGD on (strongly) convex problems and nonconvex problems with Polyak-{\L}ojasiewicz condition which has been observed/proved in neural networks.
Boosting the Confidence of Near-Tight Generalization Bounds for Uniformly Stable Randomized Algorithms
We further substantialize these generic results to SGD to derive improved high probability generalization bounds for convex or non-convex optimization with natural time decaying learning rates, which have not been possible to prove with the existing uniform stability results.
Deep learning-based super-resolution fluorescence microscopy on small datasets
However, using the new approach, a network can be trained to achieve super-resolution images from this small dataset.
Convolutional Neural Network Denoising in Fluorescence Lifetime Imaging Microscopy (FLIM)
By integrating image denoising using the trained deep learning model on the FLIM data, provide accurate FLIM phasor measurements are obtained.
Hyperspectral image classification using spectral-spatial LSTMs
Specifically, for each pixel, we feed its spectral values in different channels into Spectral LSTM one by one to learn the spectral feature.
A New Theory for Matrix Completion
To break through the limits of random sampling, this paper introduces a new hypothesis called \emph{isomeric condition}, which is provably weaker than the assumption of uniform sampling and arguably holds even when the missing data is placed irregularly.
Learning Additive Exponential Family Graphical Models via \ell_{2,1}-norm Regularized M-Estimation
We investigate a subclass of exponential family graphical models of which the sufficient statistics are defined by arbitrary additive forms.
Exact Recovery of Hard Thresholding Pursuit
In this paper, we bridge this gap by showing, for the first time, that exact recovery of the global sparse minimizer is possible for HTP-style methods under restricted strong condition number bounding conditions.
Information-Theoretic Measures for Objective Evaluation of Classifications
This work presents a systematic study of objective evaluations of abstaining classifications using Information-Theoretic Measures (ITMs).
