Zobrazeno 1 - 10
of 223
pro vyhledávání: '"Altman, Erik"'
Autor:
Blanuša, Jovan, Baraja, Maximo Cravero, Anghel, Andreea, von Niederhäusern, Luc, Altman, Erik, Pozidis, Haris, Atasu, Kubilay
In this paper, we present "Graph Feature Preprocessor", a software library for detecting typical money laundering patterns in financial transaction graphs in real time. These patterns are used to produce a rich set of transaction features for downstr
Externí odkaz:
http://arxiv.org/abs/2402.08593
Autor:
Altman, Erik, Blanuša, Jovan, von Niederhäusern, Luc, Egressy, Béni, Anghel, Andreea, Atasu, Kubilay
With the widespread digitization of finance and the increasing popularity of cryptocurrencies, the sophistication of fraud schemes devised by cybercriminals is growing. Money laundering -- the movement of illicit funds to conceal their origins -- can
Externí odkaz:
http://arxiv.org/abs/2306.16424
Autor:
Egressy, Béni, von Niederhäusern, Luc, Blanusa, Jovan, Altman, Erik, Wattenhofer, Roger, Atasu, Kubilay
This paper analyses a set of simple adaptations that transform standard message-passing Graph Neural Networks (GNN) into provably powerful directed multigraph neural networks. The adaptations include multigraph port numbering, ego IDs, and reverse me
Externí odkaz:
http://arxiv.org/abs/2306.11586
Autor:
Padhi, Inkit, Schiff, Yair, Melnyk, Igor, Rigotti, Mattia, Mroueh, Youssef, Dognin, Pierre, Ross, Jerret, Nair, Ravi, Altman, Erik
Tabular datasets are ubiquitous in data science applications. Given their importance, it seems natural to apply state-of-the-art deep learning algorithms in order to fully unlock their potential. Here we propose neural network models that represent t
Externí odkaz:
http://arxiv.org/abs/2011.01843
Autor:
Altman, Erik R.
Two elements have been essential to AI's recent boom: (1) deep neural nets and the theory and practice behind them; and (2) cloud computing with its abundant labeled data and large computing resources. Abundant labeled data is available for key domai
Externí odkaz:
http://arxiv.org/abs/1910.03033
Autor:
Altman, Erik
We describe a set of techniques to generate queries automatically based on one or more ingested, input corpuses. These queries require no a priori domain knowledge, and hence no human domain experts. Thus, these auto-generated queries help address th
Externí odkaz:
http://arxiv.org/abs/1804.07819
Autor:
Segal, Gerald1
Publikováno v:
Communications of the ACM. May2016, Vol. 59 Issue 5, p11-22. 12p.
Autor:
Kapoor, Mehak, Rahaniotis, Stephanie, Sharma, Abim, Spinelli, Michael A., Germano, Joseph J., Ibrahim, Bassiema B., Bender, Seth R., Parekh, Sameer P., Altman, Erik J., Gopal, Aasha, Undavia, Manish B.
Publikováno v:
In Heart Rhythm May 2024 21(5) Supplement:S328-S329
Publikováno v:
IEEE Micro. Sep2014, Vol. 34 Issue 5, p3-3. 1p.