Probabilistic

The Ultimate Guide to Understanding and Implementing Bloom Filters and Count Min Sketch in PythonPhoto by Google DeepMind: https://www.pexels.com/photo/an-artist-s-illustration-of-artificial-intelligence-ai-this-image-visualises-the-input-and-output-of-neural-networks-and-how-ai-systems-perceive-data-it-was-created-by-rose-pilkington-17485706/ContentsIntroductionWhat is a Probabilistic Data Structure?Bloom Filters3.1 How Do They Work 3.2 Implementing Bloom Filters in Python 3.3 Bloom Filters: Time & Space Complexity…

by KeAi Communications Co. The Salop circles with probabilistic selling products in a market with full and partial coverage. Credit: Tongyuan Shen et al In today's digital age, the travel industry is undergoing a significant transformation, with online platforms becoming central hubs for a variety of travel services. These platforms allow travelers to search, compare, and make purchases, moving…

Photorealistic Generation of Anything You Can Think ofFigure 1. Text-to-Image Generation. Image made by author.If you are an avid follower of the newest CV papers, you would be surprised at the stunning results of generative networks in creating images. Many of the previous literature were based on the groundbreaking generative adversarial network (GAN) idea, but that’s no longer the case for recent papers. In fact, if you look closely at the newest papers such as ImageN and Staple Diffusion, you will constantly see a…

Transmission line right-of-way. Credit: Scientific Reports (2023). DOI: 10.1038/s41598-023-30802-w Spanning long distances across variable terrains, electric power systems can spark wildfires in the event of dry weather and high winds. This may occur when conductor cables oscillate in such a way to become close to the surrounding vegetation. …

Forecasting the probability of extreme values with the cumulative distribution functionPhoto by Silas Baisch on UnsplashIn this article, we’ll explore the probabilistic forecasting of binary events in time series. The goal is to predict the probability that the time series will exceed a critical threshold.You’ll learn how (and why) to use a regression model to compute binary probabilities.First of all, why would you use regression to compute binary probabilities instead of a classifier?The probabilistic forecasting of…

A deep dive into the motivation behind the denoising diffusion model and detailed derivations for the loss functionPhoto by Chaozzy Lin on UnsplashThe Denoising Diffusion Probabilistic Models by Jonathan Ho et. al. is a great paper. But I had difficulty understanding it. My colleagues told me they were also left confused after reading it. So I decided to dive into the model and worked out all the derivations. In this article, I will focus on the two main obstacles to understand the paper:why the denoising diffusion model…

Probabilistic Deep LearningThis article belongs to the series “Probabilistic Deep Learning”. This weekly series covers probabilistic approaches to deep learning. The main goal is to extend deep learning models to quantify uncertainty, i.e., know what they do not know.In this article, we will introduce the concept of probabilistic logistic regression, a powerful technique that allows for the inclusion of uncertainty in the prediction process. We will explore how this approach can lead to more robust and accurate…

Probabilistic Deep LearningThis article belongs to the series “Probabilistic Deep Learning”. This weekly series covers probabilistic approaches to deep learning. The main goal is to extend deep learning models to quantify uncertainty, i.e., know what they do not know.This article covers the main differences between Deterministic and Probabilistic deep learning. Deterministic deep learning models are trained to optimize a scalar-valued loss function, while probabilistic deep learning models are trained to optimize a…

Probabilistic deep learningThis article belongs to the series “Probabilistic Deep Learning”. This weekly series covers probabilistic approaches to deep learning. The main goal is to extend deep learning models to quantify uncertainty, i.e. know what they do not know.This article will explore the main differences between deterministic and probabilistic regression. In general, deterministic regression is practical when the relationship between the independent and dependent variables is well understood and relatively stable.…

Point forecasts are good for making decisions. With probabilistic forecasts, you can also make the right ones.Photo by Aron Visuals on UnsplashIn Data Science, forecasting often involves creating the best possible model for predicting future events. Usually, the “best” model is one that minimizes a given error metric such as the Mean-Squared Error (MSE). The end result is then a list of values that depicts the predicted trajectory of the time-series. A statistician or econometrician would call this a point forecast.More…