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"Going Deeper with Recursive Convolutional Layers" by Johan Chagnon, Markus Hagenbuchner et al.

The development of Convolutional Neural Networks (CNNs) trends towards models with an ever growing number of Convolutional Layers (CLs) and increases the number of trainable parameters significantly. Such models are sensitive to these structural parameters, which implies that large models have to be carefully tuned using hyperparameter optimisation, a process that can be very time consuming. In this paper, we study the usage of Recursive Convolutional Layers (RCLs), a module relying on an algebraic feedback loop wrapped around a CL, which can replace any CL in CNNs. Using three publicly available datasets, CIFAR10, CIFAR100 and SVHN, and a simple model comprised of 4 RCLs, we compare its performances with those obtained by its feedforward counterpart, and exhibit some core properties and use-cases of RCLs. In particular, we show that RCLs can lead to models of better performances, and that reducing the number of modules from four to one lead to a decrease in accuracy of 3.5% on average ....

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Demystifying Deep Learning Algorithms

Demystifying Deep Learning Algorithms
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Paul Youngblood , Convolutional Neural Networks Cnns , Generative Adversarial Networks Gans , Recurrent Neural Networks Rnns , Artificial Neural Networks Anns , Adversarial Networks , Neural Networks , Connected Networks ,

From Pixels to Patterns: Image Analysis Techniques

In the age of visual information, understanding images is no longer just the domain of the human eye. With rapid advancements in technology, image analysis ....

Convolutional Neural Networks Cnns , Gray Level Co Occurrence Matrix , Convolutional Neural Networks ,

Machine Learning vs Deep Learning vs Foundation Models

Learn more about the relationship between Machine Learning vs Deep Learning vs Foundation Models and how they effect AI models working as a ....

Recurrent Neural Networks Rnns , Convolutional Neural Networks Cnns , Foundation Models , Generative Adversarial Networks Gans , Machine Learning , Deep Learning , Artificial Intelligence , Neural Networks , Adversarial Networks , Task Learning , All Deep Learning , Language Models ,

Efficient FIR filtering with Bit Layer Multiply Accumulator

Bit Layer Multiplier Accumulator (BLMAC) is an efficient method to perform dot products without multiplications that exploits the bit level sparsity of the weights. A total of 1,980,000 low, high, band pass and band stop type I FIR filters were generated by systematically sweeping through the cut off frequencies and by varying the number of taps from 55 to 255. ....

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