Giovanni Pagliarini

PhD Student in Mathematics, Logic & Computer Science @ University of Ferrara & Parma, Italy.

Developing, studying and testing new symbolic learning methods.

Checkout ModalDecisionTrees.jl at: https://github.com/giopaglia/ModalDecisionTrees.jl

#AI, #Interpretability, #ModalDecisionTrees!

Talks:

14:30 UTC

ModalDecisionTrees: Decision Trees, meet Modal Logics

07/27/2022, 2:30 PM2:40 PM UTC
Red

ModalDecisionTrees.jl offers a set of symbolic machine learning algorithms that extend classical decision tree learning algorithms, and are able to natively handle time series and image data. Modal Decision Trees leverage modal logics to perform a primitive-but-powerful form of entity-relation reasoning; this allows them to capture temporal and spatial patterns, and makes them suitable to natively deal (= no need for feature extraction) with data such as multivariate time-series and images.

Platinum sponsors

Julia ComputingRelational AIJulius Technology

Gold sponsors

IntelAWS

Silver sponsors

Invenia LabsBeacon BiosignalsMetalenzASMLG-ResearchConningPumas AIQuEra Computing Inc.Jeffrey Sarnoff

Media partners

Packt PublicationGather TownVercel

Community partners

Data UmbrellaWiMLDS

Fiscal Sponsor

NumFOCUS