1 code implementation • 5 Oct 2023 • Taraneh Younesian, Thiviyan Thanapalasingam, Emile van Krieken, Daniel Daza, Peter Bloem
Graph neural networks (GNNs) learn the representation of nodes in a graph by aggregating the neighborhood information in various ways.
no code implementations • 4 Aug 2021 • Cosmin Octavian Pene, Amirmasoud Ghiassi, Taraneh Younesian, Robert Birke, Lydia Y. Chen
Multi-label learning is an emerging extension of the multi-class classification where an image contains multiple labels.
no code implementations • 13 Nov 2020 • Taraneh Younesian, Chi Hong, Amirmasoud Ghiassi, Robert Birke, Lydia Y. Chen
Furthermore, relabeling only 10% of the data using the expert's results in over 90% classification accuracy with SVM.
no code implementations • 27 Oct 2020 • Taraneh Younesian, Dick Epema, Lydia Y. Chen
Labeling data correctly is an expensive and challenging task in machine learning, especially for on-line data streams.
no code implementations • 13 Jul 2020 • Amirmasoud Ghiassi, Taraneh Younesian, Robert Birke, Lydia Y. Chen
Based on the insights, we design TrustNet that first adversely learns the pattern of noise corruption, being it both symmetric or asymmetric, from a small set of trusted data.
no code implementations • 28 Jan 2020 • Taraneh Younesian, Zilong Zhao, Amirmasoud Ghiassi, Robert Birke, Lydia Y. Chen
A central feature of QActor is to dynamically adjust the query limit according to the learning loss for each data batch.
no code implementations • 28 Feb 2019 • Taraneh Younesian, Saeed Masoudnia, Reshad Hosseini, Babak N. Araabi
We benefit from transfer learning using a pre-trained CNN for feature learning.