Alessandro Flaborea (aleflabo)

aleflabo

Geek Repo

Company:Sapienza University of Rome

Location:Rome, Italy

Home Page:https://aleflabo.github.io

Twitter:@AlessandroFlabo

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Alessandro Flaborea's repositories

MoCoDAD

The official PyTorch implementation of the IEEE/CVF International Conference on Computer Vision (ICCV) '23 paper Multimodal Motion Conditioned Diffusion Model for Skeleton-based Video Anomaly Detection.

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HypAD

The official PyTorch implementation of the IEEE/CVF CVPR Visual Anomaly and Novelty Detection (VAND) Workshop paper Are we certain it's anomalous?.

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PREGO

The official PyTorch implementation of the IEEE/CVF Computer Vision and Pattern Recognition (CVPR) '24 paper PREGO: online mistake detection in PRocedural EGOcentric videos.

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Scraping-Immobiliare.it

Scraping the Immobiliare.it site and clustering the information

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Yellow-Taxis-Anlysis-in-NY-city

This analysis tries to point out some interresting informations about all the rides made in the first semester of 2018. The work concentrats both in the whole NY city and in the single zones.

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BestPractices2Body

The official PyTorch implementation of the 5th IEEE/CVF CVPR Precognition Workshop paper Best Practices for 2-Body Pose Forecasting.

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Wikipedia-Hyperlinks-Analysis

The goal of this project is to perform an analysis of the Wikipedia Hyperlink graph and, in particular, to rank the articles within the categories according to some criteria and using NetworkX.

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find_duplicate_bibtex_ref

Jupyter notebook that serves as a tool for identifying duplicate BibTeX items within a reference file.

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AirBnb-Search-Engine

Analyzing the text of Airbnb property listings and building search engines.

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Bayesian-Linear-Regression

Bayesian analysis on some real data, with the use of MCMC simulations within the OpenBUGS software.

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CloserLookFewShot

source code to ICLR'19, 'A Closer Look at Few-shot Classification'

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PaperBadger

Issuing badges to credit authors for their work on academic papers

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Stats-Under-the-Stars-2019

Partecipation at SUS2019 Hackathon in Milan at the Bocconi University. Awarded as the team which best performed using SAS software and platform.

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Street-Classification

Statistical Learning project. The team followed all the processes, from the data collection to the data preprocessing and the classification.

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