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Found 9 papers, 2 papers with code
Proceedings of the third "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'16)
The third edition of the "international - Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST) took place in Aalborg, the 4th largest city in Denmark situated beautifully in the northern part of the country, from the 24th to 26th of August 2016.
The evolution of the 3D shape of the broad-lined type Ic SN 2014ad
It is difficult to reconcile the geometry of the deeper ejecta with a jet driven explosion, but the high ejecta velocities of SN 2014ad are typical of those observed in SNe Ic-bl with GRBs, and the behaviour of the oxygen and calcium line-forming regions is consistent with fully jet-driven models.
High Energy Astrophysical Phenomena
Photometric redshifts for the Kilo-Degree Survey. Machine-learning analysis with artificial neural networks
The second dataset, optimized for low-redshift studies such as galaxy-galaxy lensing, is limited to r<20, and provides photo-zs of much better quality than in the full-depth case thanks to incorporating optical magnitudes, colours, and sizes in the GAMA-calibrated photo-z derivation.
Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies Instrumentation and Methods for Astrophysics
Angular Learning: Toward Discriminative Embedded Features
The margin-based softmax loss functions greatly enhance intra-class compactness and perform well on the tasks of face recognition and object classification.
Self-Supervised Domain Adaptation with Consistency Training
We consider the problem of unsupervised domain adaptation for image classification.
Multi-task single channel speech enhancement using speech presence probability as a secondary task training target
Simulation results show that the dereverberation and noise reduction performance of a single-task DNN trained to directly estimate the Wiener gain is higher than the performance of single-task DNNs trained to estimate the desired signal magnitude, the interference power spectral density, or the signal-to-interference ratio.
Boosting-GNN: Boosting Algorithm for Graph Networks on Imbalanced Node Classification
Traditional methods such as resampling, reweighting, and synthetic samples that deal with imbalanced datasets are no longer applicable in GNN.
CSI Feedback with Model-Driven Deep Learning of Massive MIMO Systems
In order to achieve reliable communication with a high data rate of massive multiple-input multiple-output (MIMO) systems in frequency division duplex (FDD) mode, the estimated channel state information (CSI) at the receiver needs to be fed back to the transmitter.
Review of medical data analysis based on spiking neural networks
Medical data mainly includes various types of biomedical signals and medical images, which can be used by professional doctors to make judgments on patients' health conditions.
