Databricks' KARL agent uses reinforcement learning to generalize across six enterprise search behaviors — the problem that breaks most RAG pipelines.

Learn how to understand and compute line integrals in vector fields using both Python and traditional paper methods! This video walks you step by step through the concepts of line integrals, ...

Abstract: Anomaly detection in multivariate time series (MTS) is crucial in domains such as industrial monitoring, cybersecurity, healthcare, and autonomous driving. Deep learning approaches have ...

Matt Katz (left) and Jake Welty (right) reveal the one-of-one Vector M12 racecar. Video by Drew Manley (Cooled Collective) In a nondescript storage facility on the outskirts of Los Angeles, a small ...

├── src/ # Source code │ ├── data_utils.py # Data generation and loading utilities │ ├── models.py # Time series forecasting models │ ├── visualization.py # Visualization utilities │ ├── main.py # ...

Bootstrap procedures for local projections typically rely on assuming that the data generating process (DGP) is a finite order vector autoregression (VAR), often taken to be that implied by the local ...

This paper proposes a “quasi-agnostic” sign restriction procedure to identify structural shocks in frequentist structural vector autoregression (SVAR) models. It argues that low acceptance rates, ...

Objective: This study aimed to develop depression incidence forecasting models and compare the performance of autoregressive integrated moving average (ARIMA) and vector-ARIMA (VARIMA) and temporal ...

Abstract: As an efficient recurrent neural network (RNN), reservoir computing (RC) has achieved various applications in time-series forecasting. Nevertheless, a poorly explained phenomenon remains as ...

ABSTRACT: To improve the efficiency of air quality analysis and the accuracy of predictions, this paper proposes a composite method based on Vector Autoregressive (VAR) and Random Forest (RF) models.