Deep Unfolding for Communications Systems: A Survey and Some New Directions

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Despite the widespread empirical success of ResNet, the generalization properties of deep ResNet are rarely explored beyond the lazy training regime. In this work, we investigate scaled ResNet in the limit of infinitely deep and wide neural networks, of wh ...

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Proprioception tells the brain the state of the body based on distributed sensors in the body. However, the principles that govern proprioceptive processing from those distributed sensors are poorly understood. Here, we employ a task-driven neural network ...

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 ...

We present a finite elements-neural network approach for the numerical approximation of parametric partial differential equations. The algorithm generates training data from finite element simulations, and uses a data -driven (supervised) feedforward neura ...

Fluorescence lifetime imaging (FLI) has been receiving increased attention in recent years as a powerful diagnostic technique in biological and medical research. However, existing FLI systems often suffer from a tradeoff between processing speed, accuracy, ...

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In humans and animals, surprise is a physiological reaction to an unexpected event, but how surprise can be linked to plausible models of neuronal activity is an open problem. We propose a self-supervised spiking neural network model where a surprise signa ...

Deep learning has revolutionized the field of computer vision, a success largely attributable to the growing size of models, datasets, and computational power. Simultaneously, a critical pain point arises as several computer vision applications are deploye ...

Deep Unfolding for Communications Systems: A Survey and Some New Directions

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Generalization of Scaled Deep ResNets in the Mean-Field Regime

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

Gibbs sampling the posterior of neural networks

Enabling Uncertainty Estimation in Iterative Neural Networks

Error assessment of an adaptive finite elements-neural networks method for an elliptic parametric PDE

Coupling a recurrent neural network to SPAD TCSPC systems for real-time fluorescence lifetime imaging

The neural correlates of topographical disorientation-a lesion analysis study

Decoding the brain: From neural representations to mechanistic models

A Generalized Adjusted Min-Sum Decoder for 5G LDPC Codes: Algorithm and Implementation

Digital Twins of Stone Masonry Buildings for Damage Assessment

Sparse autoregressive neural networks for classical spin systems

Optimizing Dynamic Aperture Studies with Active Learning

Fast adaptation to rule switching using neuronal surprise

Fast and Future: Towards Efficient Forecasting in Video Semantic Segmentation