https://hendersontrent.github.io/posts/2024/05/gaussian-process-time-series Interpretable time-series modelling using Gaussian processesTrent HendersonMay 03, 2024 “…Gaussian processes (GP)… are an insanely powerful tool that can model an absurd range of data (including continuous and discrete)
What I Read: Smooth Noisy Data
https://towardsdatascience.com/the-perfect-way-to-smooth-your-noisy-data-4f3fe6b44440?gi=a6f62aaf2818 The Perfect Way to Smooth Your Noisy DataAndrew BowellOct 25, 2023 “Insanely fast and reliable smoothing and interpolation with the Whittaker-Eilers method.”
What I Read: Density Kernel Depth for Outlier Detection
https://www.kdnuggets.com/density-kernel-depth-for-outlier-detection-in-functional-data Density Kernel Depth for Outlier Detection in Functional DataKulbir SinghNovember 8, 2023 “The Density Kernel Depth (DKD) method provides a nuanced approach to detect outliers in functional data…”
What I Read: Geometric Deep Learning
https://thegradient.pub/towards-geometric-deep-learning/ Towards Geometric Deep LearningMichael Bronstein18.Feb.2023 “Geometric Deep Learning is an umbrella term for approaches considering a broad class of ML problems from the perspectives of symmetry and invariance.”
What I Read: Anomaly Detection Metrics
https://medium.com/@katser/a-review-of-anomaly-detection-metrics-with-a-lot-of-related-information-736d88774712 A Review of Anomaly Detection MetricsIurii KatserJul 12 “Anomaly detection… is a problem… to identify unusual patterns that do not conform to expected behavior…. we will talk about anomaly
What I Read: cross-validation
https://robjhyndman.com/hyndsight/crossvalidation/ Why every statistician should know about cross-validationHyndsightRob J Hyndman4 October 2010 “Why every statistician should know about cross-validation”