Improving the Training of Compact Neural Networks for Visual Recognition

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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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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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Neurons in the human amygdala and hippocampus are classically thought to encode a person’s identity invariant to visual features. However, it remains largely unknown how visual information from higher visual cortical areas is translated into such a semanti ...

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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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 ...

FDTD/FEM simulations, strengthened by machine learning, achieve 95.44% optical absorption efficiency in TiO 2 /TiN plasmonic nanocavities for solar energy harvesting. ...

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 ...

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Integrating low-power wearable systems into routine health monitoring is an ongoing challenge. Recent advances in the computation capabilities of wearables make it possible to target complex scenarios by exploiting multiple biosignals and using high-perfor ...

Photometric stereo, a computer vision technique for estimating the 3D shape of objects through images captured under varying illumination conditions, has been a topic of research for nearly four decades. In its general formulation, photometric stereo is an ...

Improving the Training of Compact Neural Networks for Visual Recognition

Chattez avec Graph Search

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

Self-supervised and few-shot learning for robust bioaerosol monitoring

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

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

Feature-based encoding of face identity by single neurons in the human amygdala and hippocampus

Enabling Uncertainty Estimation in Iterative Neural Networks

Advances on Real Time M/EEG Neural Feature Extraction

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

Machine-learning-empowered FDTD/FEM simulations for predictive solar energy absorption in plasmonic metamaterial nanocavity arrays

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

Integrating Physics and Topology in Neural Networks for Learning Rigid Body Dynamics

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

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Optimizing Dynamic Aperture Studies with Active Learning

Robust machine learning for neuroscientific inference

Incorporating Projective Geometry into Deep Learning

M2SKD: Multi-to-Single Knowledge Distillation of Real-Time Epileptic Seizure Detection for Low-Power Wearable Systems

Aggregating Spatial and Photometric Context for Photometric Stereo