algorithm – by Brian Busemeyer

Compressed sensing

Blog of Feeling Responsive Compressed sensing is a way of extracting a full signal out of a sparse sampling. It's only requirement is that the signal has a sparse representation in some basis, which is actually true for most interesting signals that we encounter. Read More ›

algorithm – by Dima Kochkov

Boltzmann Machines

Blog of Feeling Responsive Boltzmann Machines represent a class of Neural Networks that can be used for unsupervised learning. Inspired by ideas from physics and neuroscience these nets allow a simple, genuine learning rule. The learning is based on minimization of Kullback–Leibler divergence between learned probability distribution and the dataset. Read More ›

algorithm – by Yubo "Paul" Yang

Automatic Differentiation

Blog of Feeling Responsive Automatic Differentiation exploits the fact that any algebraic function implemented on a computer can be compiled into a long list of elementary operations and elementary functions. Using this observation, exact differentiation can be carried out efficiently by exploiting the chain rule. Read More ›

algorithm – by Eli Chertkov

Kalman Filter

Blog of Feeling Responsive Kalman filter is an algorithm that filters out the noise in noisy measurement to extract signal. Explicit form of the signal must be known. Read More ›