A new objective metric to predict image quality using deep neural networks

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In this paper, we study sampling from a posterior derived from a neural network. We propose a new probabilistic model consisting of adding noise at every pre- and post-activation in the network, arguing that the resulting posterior can be sampled using an ...

Turning pass-through network architectures into iterative ones, which use their own output as input, is a well-known approach for boosting performance. In this paper, we argue that such architectures offer an additional benefit: The convergence rate of the ...

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The present work proposes a framework for nonlinear model order reduction based on a Graph Convolutional Autoencoder (GCA-ROM). In the reduced order modeling (ROM) context, one is interested in obtaining real -time and many-query evaluations of parametric ...

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Many feedforward neural networks (NNs) generate continuous and piecewise-linear (CPWL) mappings. Specifically, they partition the input domain into regions on which the mapping is affine. The number of these so-called linear regions offers a natural metric ...

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A new objective metric to predict image quality using deep neural networks

Chat with Graph Search

Gibbs sampling the posterior of neural networks

Enabling Uncertainty Estimation in Iterative Neural Networks

Fast and Future: Towards Efficient Forecasting in Video Semantic Segmentation

Partial discharge localization in power transformer tanks using machine learning methods

The neural correlates of topographical disorientation-a lesion analysis study

Robust NAS under adversarial training: benchmark, theory, and beyond

A graph convolutional autoencoder approach to model order reduction for parametrized PDEs

Exploring High-Performance and Energy-Efficient Architectures for Edge AI-Enabled Applications

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

33.3 MiBMI: A 192/512-Channel 2.46mm² Miniaturized Brain-Machine Interface Chipset Enabling 31-Class Brain-to-Text Conversion Through Distinctive Neural Codes

On the number of regions of piecewise linear neural networks

Towards improving full-length ribosome density prediction by bridging sequence and graph-based representations