Power Efficient Hardware Implementation of a Fuzzy Neural Network

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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 ...

Modern deep networks are trained with stochastic gradient descent (SGD) whose key hyperparameters are the number of data considered at each step or batch size B, and the step size or learning rate n. For small B and large n, SGD corresponds to a stochastic ...

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In the rapidly evolving landscape of machine learning research, neural networks stand out with their ever-expanding number of parameters and reliance on increasingly large datasets. The financial cost and computational resources required for the training p ...

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 ...

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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In the past few years, Machine Learning (ML) techniques have ushered in a paradigm shift, allowing the harnessing of ever more abundant sources of data to automate complex tasks. The technical workhorse behind these important breakthroughs arguably lies in ...

Topographical disorientation refers to the selective inability to orient oneself in familiar surroundings. However, to date its neural correlates remain poorly understood. Here we use quantitative lesion analysis and a lesion network mapping approach in or ...

Magnonics is a research field that has gained an increasing interest in both the fundamental and applied sciences in recent years. This field aims to explore and functionalize collective spin excitations in magnetically ordered materials for modern informa ...

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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 ...

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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Modern neuroscience research is generating increasingly large datasets, from recording thousands of neurons over long timescales to behavioral recordings of animals spanning weeks, months, or even years. Despite a great variety in recording setups and expe ...

Power Efficient Hardware Implementation of a Fuzzy Neural Network

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

On the different regimes of stochastic gradient descent

Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange

Fast learning without synaptic plasticity in spiking neural networks

Random matrix methods for high-dimensional machine learning models

Fast adaptation to rule switching using neuronal surprise

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Topics in statistical physics of high-dimensional machine learning

The neural correlates of topographical disorientation-a lesion analysis study

The 2024 Magnonics Roadmap

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

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

Driving and suppressing the human language network using large language models

On the number of regions of piecewise linear neural networks

Fast and Future: Towards Efficient Forecasting in Video Semantic Segmentation

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

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

Safe Deep Neural Networks

Robust machine learning for neuroscientific inference