2019

1. On Reproducing Semi-dense Depth Map Reconstruction using Deep Convolutional Neural Networks with Perceptual Loss Makarov, Ilya, Maslov, Dmitrii, Gerasimova, Olga, Aliev, Vladimir, Korinevskaya, Alisa, Sharma, Ujjwal, and Wang, Haoliang In Proceedings of the 27th ACM International Conference on Multimedia, MM 2019, Nice, France, October 21-25, 2019 2019

2020

1. Semantic Path-Based Learning for Review Volume Prediction Sharma, Ujjwal, Rudinac, Stevan, Worring, Marcel, Demmers, Joris, and Dolen, Willemijn In Advances in Information Retrieval 2020 [Abs]

   Graphs offer a natural abstraction for modeling complex real-world systems where entities are represented as nodes and edges encode relations between them. In such networks, entities may share common or similar attributes and may be connected by paths through multiple attribute modalities. In this work, we present an approach that uses semantically meaningful, bimodal random walks on real-world heterogeneous networks to extract correlations between nodes and bring together nodes with shared or similar attributes. An attention-based mechanism is used to combine multiple attribute-specific representations in a late fusion setup. We focus on a real-world network formed by restaurants and their shared attributes and evaluate performance on predicting the number of reviews a restaurant receives, a strong proxy for popularity. Our results demonstrate the rich expressiveness of such representations in predicting review volume and the ability of an attention-based model to selectively combine individual representations for maximum predictive power on the chosen downstream task.