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Found 9 papers, 1 papers with code
Manifold Graph Signal Restoration using Gradient Graph Laplacian Regularizer
For manifold graphs without explicit latent coordinates, we propose a fast parameter-free spectral method to first compute latent space coordinates for graph nodes based on generalized eigenvectors.
Generating Authentic Adversarial Examples beyond Meaning-preserving with Doubly Round-trip Translation
Generating adversarial examples for Neural Machine Translation (NMT) with single Round-Trip Translation (RTT) has achieved promising results by releasing the meaning-preserving restriction.
Fast Computation of Generalized Eigenvectors for Manifold Graph Embedding
Experiments show that our embedding is among the fastest in the literature, while producing the best clustering performance for manifold graphs.
Graph-Based Depth Denoising & Dequantization for Point Cloud Enhancement
Specifically, we model depth formation as a combined process of signal-dependent noise addition and non-uniform log-based quantization.
FocusNet: Classifying Better by Focusing on Confusing Classes
We also introduce a novel focus-picking loss function to improve classification accuracy by encouraging FocusNet to focus on the most confusing classes.
Battery characterization via eddy-current imaging with nitrogen-vacancy centers in diamond
Sensitive and accurate diagnostic technologies with magnetic sensors are of great importance for identifying and localizing defects of rechargeable solid batteries in a noninvasive detection.
Applied Physics
Fast & Robust Image Interpolation using Gradient Graph Laplacian Regularizer
In the graph signal processing (GSP) literature, it has been shown that signal-dependent graph Laplacian regularizer (GLR) can efficiently promote piecewise constant (PWC) signal reconstruction for various image restoration tasks.
DeepHE: Accurately Predicting Human Essential Genes based on Deep Learning
Results: We proposed a deep learning based method, DeepHE, to predict human essential genes by integrating features derived from sequence data and protein-protein interaction (PPI) network.
How to Train Triplet Networks with 100K Identities?
However, as the number of identities becomes extremely large, the training will suffer from bad local minima because effective hard triplets are difficult to be found.
