Neural network machine learning
Pro Research Analysisby 
Searched over 200M research papers
Neural Network Machine Learning: An Overview
Introduction to Neural Networks in Machine Learning
Neural networks (NNs) are a cornerstone of modern machine learning, inspired by the structure and function of the human brain. These models consist of interconnected neurons that process data in layers, enabling the learning and recognition of complex patterns. The development of artificial neural networks (ANNs) has revolutionized various fields, including computer vision, natural language processing, and more1 2 4.
Deep Learning and Spiking Neural Networks
Deep learning, a subset of machine learning, involves training deep (multilayer) neural networks to perform tasks such as image and speech recognition. Traditional ANNs use continuous-valued activations, whereas spiking neural networks (SNNs) use discrete spikes, making them more biologically realistic. SNNs are particularly efficient in terms of power consumption and are better suited for processing spatio-temporal data. However, training SNNs is challenging due to their non-differentiable transfer functions, which complicates the use of backpropagation1.
Neural Networks in Natural Language Processing
Neural networks have significantly advanced the field of natural language processing (NLP). Techniques such as feed-forward neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs) are employed to handle various NLP tasks. These models leverage vector-based representations of words and computation-graph abstractions to define and train complex networks. Advanced architectures like attention-based models and conditioned-generation models have further improved the performance of machine translation and syntactic parsing2.
Applications Across Various Domains
Neural networks are applied in numerous fields beyond NLP and computer vision. They are used in control engineering, automation, aerospace, psychology, economics, healthcare, and energy science. The versatility of NNs allows them to tackle a wide range of problems, from face recognition to muscle activity control, demonstrating their broad applicability and effectiveness4.
Stochastic Computing in Neural Networks
Stochastic computing (SC) offers a hardware-efficient approach to implementing neural networks. SC reduces hardware requirements and power consumption by sacrificing some inference accuracy and computation speed. Recent advancements in SC techniques have improved the performance of SC NNs, making them comparable to conventional binary designs while utilizing less hardware. This approach is particularly beneficial for applications requiring high hardware efficiency5.
Overcoming Catastrophic Forgetting
One of the challenges in neural network training is catastrophic forgetting, where a model forgets previously learned tasks when trained sequentially on new tasks. Recent research has proposed methods to mitigate this issue by protecting the weights important for previous tasks, inspired by synaptic consolidation in neuroscience. This approach allows neural networks to maintain expertise on old tasks while learning new ones, enabling more robust and scalable models8.
Conclusion
Neural networks and deep learning have transformed machine learning, offering powerful tools for a wide range of applications. While traditional ANNs continue to excel in many areas, emerging models like SNNs and SC NNs provide promising alternatives with unique advantages. Ongoing research aims to address existing challenges, such as training efficiency and catastrophic forgetting, to further enhance the capabilities and applicability of neural networks in machine learning.
Sources and full results
Most relevant research papers on this topic
Deep Learning in Spiking Neural Networks
Spiking neural networks (SNNs) are more biologically realistic than artificial neural networks (ANNs) and can improve accuracy and computational cost in machine learning, but still lag behind ANNs on some tasks.
Highly CitedNeural Network Methods for Natural Language Processing
This book introduces various neural network models for natural language processing, enabling state-of-the-art algorithms for machine translation, syntactic parsing, and structured prediction.
Highly CitedNeural network models and deep learning
Deep neural network models can approximate functions and dynamics in biology, and their use may help understand brain computation.
Very Rigorous JournalHighly CitedNeural Networks And Machine Learning
Artificial neural networks (ANNs) and machine learning techniques have revolutionized computational tasks in various industries, enabling computers to independently learn and improve.
Highly CitedA Survey of Stochastic Computing Neural Networks for Machine Learning Applications
Stochastic computing neural networks achieve hardware efficiency and performance improvements while maintaining inference accuracy and computation speed, making them comparable to conventional binary designs while using less hardware.
Highly CitedMachine learning, artificial neural networks and social research
Machine learning, particularly based on artificial neural networks, has potential applications in social sciences, particularly in data analysis procedures.
Highly CitedDeep learning in neural networks: An overview
Deep artificial neural networks have revolutionized pattern recognition and machine learning by deepening credit assignment paths, enabling more complex and accurate models.
Highly CitedOvercoming catastrophic forgetting in neural networks
Our approach enables neural networks to learn multiple tasks sequentially, overcoming catastrophic forgetting and improving AI performance in various tasks.
Highly CitedModern Artificial Neural Networks: Is Evolution Cleverer?
Modern artificial neural networks (ANN) have evolved beyond their biological counterparts, with refined topologies and powerful training algorithms, but their biological counterparts remain unresolved.
Application of Machine Learning Algorithm Based on Neural Network Technology
LSTM outperforms NTM in replication tasks, but its performance in addition and multiplication tasks is lower than NTM's.
Literature Review