An unsupervised learning method is provided implemented in an artificial neural network based on memristive devices. It consists notably in producing an increase in the conductance of a synapse when there is temporal overlap between a pre-synaptic pulse and a post-synaptic pulse and in decreasing ...
Artificial neural networks suffer from catastrophic forgetting. Unlike humans, when these networks are trained on something new, they rapidly forget what was learned before. In the brain, a mechanism thought to be important for protecting memories is the
[13] An Overview of Normalization Methods in Deep Learning[14] Facebook AI Proposes Group Normalization Alternative to Batch Normalization[15] What are the main normalization layers in artificial neural networks? 作者:Steven 链接:zhuanlan.zhihu.com/p/14 来源:知乎 如有侵权请联系删除。
尝试去探究Artificial Intelligent究竟在学什么。蹚入Deep Learning的人越来越多了,直接上手写Image Classification、Speech Recognition甚至搭一个完整的Machine Translation System也不再是一个难事了,但也因为嵌套的非线性结构使得Neural Network框架更像是一个黑盒子,我们该如何解释究竟是什么因素使得有这样的预测结果。
Neural network, a computer program that operates in a manner inspired by the natural neural network in the brain. The objective of such artificial neural networks is to perform such cognitive functions as problem solving and machine learning. The theoret
While the first artificial neural network was theorized in 1958, deep learning requires substantial computing power that was not available until the 2000s. Now, researchers have access to computing resources that make it possible to build and train networks with hundreds of connections and neurons. ...
Deep learning has brought revolutionary changes duo to effectively train large neural networks. Different types of deep learning methods are applied in different areas, such as CNN, long short-term memory (LSTM), recurrent neural network (RNN), and generative adversarial networks (GAN) [107]. Dee...
It has been proven that the dropout method can improve the performance of neural networks onsupervised learningtasks in areas such asspeech recognition, document classification and computational biology. Deep learning neural networks A type of advancedML algorithm, known as anartificial neural network, ...
A neural architecture for designing truthful and efficient auctions. Preprint at https://arxiv.org/abs/1907.05181 (2019). Weissteiner, J. & Seuken, S. Deep learning-powered iterative combinatorial auctions. In Proc. AAAI Conference on Artificial Intelligence Vol. 34, 2284–2293 (AAAI, 2020)....
Adaptive transfer learning-based multiscale feature fused deep convolutional neural network for EEG MI multiclassification in brain–computer interface 2022, Engineering Applications of Artificial Intelligence Show abstract Explainable deep learning for efficient and robust pattern recognition: A survey of recen...