The Kalman Filtering Process| Dilith Jayakody
Sharpness-Aware Minimization (SAM) is an optimization technique that minimizes both the loss and sharpness of a given objective function. It was proposed by P. Foret et al. in their paper titled “Sharpness-Aware Minimization for Efficiently Improving Generalization” during their time at Google. The technique exhibits several benefits such as improved efficiency, generalization, and robustness to local noise. Further, the algorithm is easier to implement due to the absence of 2nd order der...| Dilith Jayakody
The reparameterization trick is an ingenious method to sidestep the challenge of backpropagating through a random or stochastic node within a neural network. This has found prominence, particularly in the context of Variational Autoencoders (VAEs). In this blog post, we will discuss what the reparameterization trick is and what it solves.| Dilith Jayakody
Importance Sampling is a tool that helps us tackle a common challenge: calculating expectations. While that might sound like a straightforward task, it often becomes a formidable problem, especially when dealing with high-dimensional data. In this blog post, we’ll delve into the intricacies of this technique and explore its significance.| Dilith Jayakody