Convolutional neural networks (CNNs), which automatically learn ... and on the use of post-hoc techniques to explain network decisions. The proposed design, named MS-EEGNet, learned temporal features ...

MambaTraffic employs a two-branch spatial–temporal network, combining Mamba and a spatial–temporal convolutional network, with a contrastive learning paradigm to enhance feature extraction. Extensive ...

EEG signals are temporal signals, and solely capturing PSD features may not sufficiently acquire the spatiotemporal features of the signal. In the study, we employed the Conv-LSTM spatiotemporal ...

Abstract: Network motifs provide a deep insight into the network functional abilities, and have proven useful in various practical applications. Existing studies reveal that different definitions of ...

Nvidia's DLSS 4 Ray Reconstruction tackles the issue of image noise with ray tracing. By improving stability and texture preservation, it minimizes the visual trade-offs and ...

config_frequentist.py: Hyperparameters for main_frequentist file. @article{shridhar2019comprehensive, title={A comprehensive guide to bayesian convolutional neural network with variational inference}, ...

TinyEKF is a simple, header-only C/C++ implementation of the Extended Kalman Filter that is general enough to use on different projects. It supports both single- and double-precision floating-point ...

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Network models are a computer architecture, implementable in either hardware or software, meant to simulate biological populations of interconnected neurons. These models, also known as ...

The authors instead suggest that the hippocampus and the perirhinal cortex both play a role in recollection and familiarity, and that these two regions of the medial temporal lobe differ mainly in ...

This important study shows that the activity of hypothalamic hypocretin/orexin neurons (HONs) correlates with body movement over multiple behaviors. Sophisticated techniques and analyses showcase this ...