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Liquid Neural Networks: Definition, Applications, & Challenges

Unite.AI

A neural network (NN) is a machine learning algorithm that imitates the human brain's structure and operational capabilities to recognize patterns from training data. Despite being a powerful AI tool, neural networks have certain limitations, such as: They require a substantial amount of labeled training data.

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Efficient Continual Learning for Spiking Neural Networks with Time-Domain Compression

Marktechpost

Furthermore, many applications now need AI algorithms to adapt to individual users while ensuring privacy and reducing internet connectivity. One new paradigm that has emerged to meet these problems is continuous learning or CL. This algorithm has proven to reach state-of-the-art classification accuracy on CNNs.

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A Brain-Inspired Learning Algorithm Enables Metaplasticity in Artificial and Spiking Neural Networks

Marktechpost

Credit assignment in neural networks for correcting global output mistakes has been determined using many synaptic plasticity rules in natural neural networks. Methods of biological neuromodulation have inspired several plasticity algorithms in models of neural networks.

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Mathematical Foundations of Backpropagation in Neural Network

Pickl AI

Summary: Backpropagation in neural network optimises models by adjusting weights to reduce errors. Despite challenges like vanishing gradients, innovations like advanced optimisers and batch normalisation have improved their efficiency, enabling neural networks to solve complex problems.

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Artificial Neural Network: A Comprehensive Guide

Pickl AI

Summary: Artificial Neural Network (ANNs) are computational models inspired by the human brain, enabling machines to learn from data. Introduction Artificial Neural Network (ANNs) have emerged as a cornerstone of Artificial Intelligence and Machine Learning , revolutionising how computers process information and learn from data.

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Continual Learning: Methods and Application

The MLOps Blog

TL;DR: In many machine-learning projects, the model has to frequently be retrained to adapt to changing data or to personalize it. Continual learning is a set of approaches to train machine learning models incrementally, using data samples only once as they arrive. What is continual learning?

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The Critical Nuances of Today’s AI — and the Frontiers That Will Define Its Future

Towards AI

Liquid Neural Networks: Research focuses on developing networks that can adapt continuously to changing data environments without catastrophic forgetting. These networks excel at processing time series data, making them suitable for applications like financial forecasting and climate modeling.