no code implementations • 25 Jan 2024 • Andrei Tomut, Saeed S. Jahromi, Abhijoy Sarkar, Uygar Kurt, Sukhbinder Singh, Faysal Ishtiaq, Cesar Muñoz, Prabdeep Singh Bajaj, Ali Elborady, Gianni Del Bimbo, Mehrazin Alizadeh, David Montero, Pablo Martin-Ramiro, Muhammad Ibrahim, Oussama Tahiri Alaoui, John Malcolm, Samuel Mugel, Roman Orus
Traditional compression methods such as pruning, distillation, and low-rank approximation focus on reducing the effective number of neurons in the network, while quantization focuses on reducing the numerical precision of individual weights to reduce the model size while keeping the number of neurons fixed.
no code implementations • 6 Dec 2022 • Lucas Leclerc, Luis Ortiz-Guitierrez, Sebastian Grijalva, Boris Albrecht, Julia R. K. Cline, Vincent E. Elfving, Adrien Signoles, Loïc Henriet, Gianni Del Bimbo, Usman Ayub Sheikh, Maitree Shah, Luc Andrea, Faysal Ishtiaq, Andoni Duarte, Samuel Mugel, Irene Caceres, Michel Kurek, Roman Orus, Achraf Seddik, Oumaima Hammammi, Hacene Isselnane, Didier M'tamon
In this work we propose a quantum-enhanced machine learning solution for the prediction of credit rating downgrades, also known as fallen-angels forecasting in the financial risk management field.