When you start using a new video or music service, the first recommendations often miss the mark, and it can be surprisingly hard to settle in. This is the cold start problem. Traditionally, we treat it as something the provider should solve, while the user can only “keep using it and wait until the system […]
Break the Lock-In: Carry Your Preferences Anywhere Read Post »
Real-world data often live in a high-dimensional ambient space, but the points themselves concentrate near a much lower-dimensional manifold. The “visible” (ambient) dimension is easy to read off from the feature vector length, while the “intrinsic” dimension (the effective degrees of freedom) is much harder to estimate. Two Nearest Neighbors (TwoNN) is a simple yet
TwoNN Intrinsic Dimension Explained: Python and Visual Illustrations Read Post »
Definition of Fisher Information The Fisher information is defined as $$\mathrm{FisherInformation}(\theta_0)\stackrel{\text{def}}{=}-\mathbb{E}_{X\sim p(x\mid\theta_0)}\left[\frac{d^2}{d\theta^2}\log p(x\mid\theta)\bigg|_{\theta=\theta_0}\right].$$ Fisher information quantifies how precisely a model parameter can be estimated.A larger Fisher information means the parameter can be estimated more accurately,while a smaller Fisher information indicates that estimation is more difficult. Fisher information admits several equivalent interpretations. Equivalent Expressions $$\begin{align}&\mathrm{FisherInformation}(\theta_0) \\&\stackrel{\text{def}}{=}-\mathbb{E}_{X
Fisher Information Explained: Python and Visual Illustrations Read Post »
