no code implementations • SemEval (NAACL) 2022 • Fadi Hassan, Wondimagegnhue Tufa, Guillem Collell, Piek Vossen, Lisa Beinborn, Adrian Flanagan, Kuan Eeik Tan
This paper presents our system used to participate in task 11 (MultiCONER) of the SemEval 2022 competition.
no code implementations • 2 Apr 2024 • Alberto Blanco-Justicia, Najeeb Jebreel, Benet Manzanares, David Sánchez, Josep Domingo-Ferrer, Guillem Collell, Kuan Eeik Tan
The objective of digital forgetting is, given a model with undesirable knowledge or behavior, obtain a new model where the detected issues are no longer present.
no code implementations • 12 Dec 2020 • Alberto Blanco-Justicia, Josep Domingo-Ferrer, Sergio Martínez, David Sánchez, Adrian Flanagan, Kuan Eeik Tan
In contrast with centralized ML approaches, FL saves computation to the server and does not require the clients to outsource their private data to the server.
no code implementations • 8 Apr 2020 • Adrian Flanagan, Were Oyomno, Alexander Grigorievskiy, Kuan Eeik Tan, Suleiman A. Khan, Muhammad Ammad-Ud-Din
We introduce the federated multi-view matrix factorization method that extends the federated learning framework to matrix factorization with multiple data sources.
no code implementations • 20 Jan 2020 • Tinghuai Wang, Guangming Wang, Kuan Eeik Tan, Donghui Tan
Specifically, we design an architecture to encode the multiple spectral contextual information in the form of spectral pyramid of multiple embedding spaces.
1 code implementation • 29 Jan 2019 • Muhammad Ammad-Ud-Din, Elena Ivannikova, Suleiman A. Khan, Were Oyomno, Qiang Fu, Kuan Eeik Tan, Adrian Flanagan
In the Federated Learning paradigm, a master machine learning model is distributed to user clients, the clients use their locally stored data and model for both inference and calculating model updates.