Search Results for author: Erik Altman

Found 5 papers, 2 papers with code

Tabular Transformers for Modeling Multivariate Time Series

1 code implementation3 Nov 2020 Inkit Padhi, Yair Schiff, Igor Melnyk, Mattia Rigotti, Youssef Mroueh, Pierre Dognin, Jerret Ross, Ravi Nair, Erik Altman

This results in two architectures for tabular time series: one for learning representations that is analogous to BERT and can be pre-trained end-to-end and used in downstream tasks, and one that is akin to GPT and can be used for generation of realistic synthetic tabular sequences.

Fraud Detection Synthetic Data Generation +2

Realistic Synthetic Financial Transactions for Anti-Money Laundering Models

1 code implementation NeurIPS 2023 Erik Altman, Jovan Blanuša, Luc von Niederhäusern, Béni Egressy, Andreea Anghel, Kubilay Atasu

To this end, this paper contributes a synthetic financial transaction dataset generator and a set of synthetically generated AML (Anti-Money Laundering) datasets.

Understanding AI Data Repositories with Automatic Query Generation

no code implementations20 Apr 2018 Erik Altman

The proposed techniques also allow ingested knowledge to be extended naturally.

Provably Powerful Graph Neural Networks for Directed Multigraphs

no code implementations20 Jun 2023 Béni Egressy, Luc von Niederhäusern, Jovan Blanusa, Erik Altman, Roger Wattenhofer, Kubilay Atasu

This paper analyses a set of simple adaptations that transform standard message-passing Graph Neural Networks (GNN) into provably powerful directed multigraph neural networks.

Graph Feature Preprocessor: Real-time Extraction of Subgraph-based Features from Transaction Graphs

no code implementations13 Feb 2024 Jovan Blanuša, Maximo Cravero Baraja, Andreea Anghel, Luc von Niederhäusern, Erik Altman, Haris Pozidis, Kubilay Atasu

In this paper, we present "Graph Feature Preprocessor", a software library for detecting typical money laundering and fraud patterns in financial transaction graphs in real time.

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