Covers the theory and applications of graph coloring, focusing on disassortative stochastic block models and planted coloring.
Covers generative models with a focus on self-attention and transformers, discussing sampling methods and empirical means.
Covers the importance of sampling, signal reconstruction, and aliasing in digital representation.
Explores explicit stabilised Runge-Kutta methods and their application to Bayesian inverse problems, covering optimization, sampling, and numerical experiments.
Explores research process, variable types, causality vs correlation, and sampling strategies.
Covers determinantal point processes, sine-process, and their extrapolation in different spaces.
Covers generative models, focusing on Boltzmann machines and constrained maximization using Lagrange multipliers.
Covers the concept of sampling, the sampling theorem, signal reconstruction, and the conversion of analogue signals to digital signals.
Explores ideal sampling, Fourier transformation, spectral repetition, and analog signal reconstruction.
Explores signals, instruments, and systems, covering ADC, Fourier Transform, sampling, signal reconstruction, aliasing, and anti-alias filters.
