5 citations · 5 across the 1 of their papers we have counts for
7 papers
Learned Gradient Compression for Distributed Deep Learning
Lusine Abrahamyan, Yiming Chen, Giannis Bekoulis +1
Training deep neural networks on large datasets containing high-dimensional data requires a large amount of computation. A solution to this problem is data-parallel distributed tra…
Solving Arithmetic Word Problems by Scoring Equations with Recursive Neural Networks
Klim Zaporojets, Giannis Bekoulis, Johannes Deleu +2
Solving arithmetic word problems is a cornerstone task in assessing language understanding and reasoning capabilities in NLP systems. Recent works use automatic extraction and rank…
Graph Convolutional Neural Networks with Node Transition Probability-based Message Passing and DropNode Regularization
Tien Huu Do, Duc Minh Nguyen, Giannis Bekoulis +2
Graph convolutional neural networks (GCNNs) have received much attention recently, owing to their capability in handling graph-structured data. Among the existing GCNNs, many metho…
Zero-Shot Cross-Lingual Transfer with Meta Learning
Farhad Nooralahzadeh, Giannis Bekoulis, Johannes Bjerva +1
Learning what to share between tasks has been a topic of great importance recently, as strategic sharing of knowledge has been shown to improve downstream task performance. This is…
Sub-event detection from Twitter streams as a sequence labeling problem
Giannis Bekoulis, Johannes Deleu, Thomas Demeester +1
This paper introduces improved methods for sub-event detection in social media streams, by applying neural sequence models not only on the level of individual posts, but also direc…
Adversarial training for multi-context joint entity and relation extraction
Giannis Bekoulis, Johannes Deleu, Thomas Demeester +1
Adversarial training (AT) is a regularization method that can be used to improve the robustness of neural network methods by adding small perturbations in the training data. We sho…