I am new to deep learning and want to understand CNNs from scratch. Can someone explain: 1. What makes convolution different from fully connected layers? 2. How does backpropagation work through conv
I am self-studying nonlinear control theory and struggling with Lyapunov stability. Looking for: - Good textbook recommendations (beyond Khalil) - Online lectures covering LaSalle invariance principl
I am building a chest X-ray abnormality classification system for a hospital project. Currently comparing: - ResNet-50 (pretrained on ImageNet) - DenseNet-121 (pretrained on CheXNet) - EfficientNet-B
A stepwise "growth" process, which divides a fractal cluster into branches of sequentially connected particles, helps us to show the following: A reversible growth of a cluster is dominated by an add
When surface gravity waves in deep water are driven to a balance between energy input and dissipation, it has been observed that the spectrum of wave-height correlations exhibits power-law behavior c
The multifractal properties of a class of one parameter generalized Fibonacci sequences are studied. This class of recursion relations, which is defined by an infinite set of sequences similar to the
River models are reviewed with emphasis on the power-law nature of basin size distributions. From a general point of view, the whole river pattern on a surface can be regarded as a kind of tiling by
Recently, a general organizing principle has been reported connecting 1/f-noise with the self-similar scale-invariant ‘fractal’ properties in space, hence reflecting two sides of a coin, the so-calle
