Networks

has been in production two months. Accuracy is 92.9%. Then transaction patterns shift quietly. By the point your dashboard turns red, accuracy has collapsed to 44.6%. Retraining takes six hours—and wishes labeled data you won’t have...

Abstract datasets are extremely imbalanced, with positive rates below 0.2%. Standard neural networks trained with weighted binary cross-entropy often achieve high ROC-AUC but struggle to discover suspicious transactions under threshold-sensitive metrics. I propose a...

Introduction within the period of 2017-2019, physics-informed neural networks (PINNs) have been a very talked-about area of research within the scientific machine learning (SciML) community . PINNs are used to unravel atypical and partial...

audio embeddings for music advice? Streaming platforms (Spotify, Apple Music, etc.) must have the power to recommend recent songs to their users. The higher the recommendations, the higher the listening experience. There are various ways...

in supply-chain planning has traditionally been treated as a time-series problem. Each SKU is modeled independently. A rolling time window (say, last 14 days) is used to predict tomorrow’s sales. Seasonality is captured, promotions are added,...

Even networks long considered “untrainable” can learn effectively with a little bit...

computers and Artificial Intelligence, we had established institutions designed to reason systematically about human behavior — the court. The legal system is one in all humanity’s oldest reasoning engines, where facts and evidence...

as a black box. We all know that it learns from data, however the query is it truly learns. In this text, we are going to construct a tiny Convolutional Neural Network (CNN)...