activity
20192022
most citedCan Graph Neural Networks Go "Online"? An Analysis of Pretraining and Inference

6 citations · 13 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CL20224 cited

Emergent Communication for Understanding Human Language Evolution: What's Missing?

Lukas Galke, Yoav Ram, Limor Raviv

Emergent communication protocols among humans and artificial neural network agents do not yet share the same properties and show some critical mismatches in results. We describe th…

cs.IR2021

Recommendations for Item Set Completion: On the Semantics of Item Co-Occurrence With Data Sparsity, Input Size, and Input Modalities

Iacopo Vagliano, Lukas Galke, Ansgar Scherp

We address the problem of recommending relevant items to a user in order to "complete" a partial set of items already known. We consider the two scenarios of citation and subject l…

cs.IR2019

Multi-Modal Adversarial Autoencoders for Recommendations of Citations and Subject Labels

Lukas Galke, Florian Mai, Iacopo Vagliano +1

We present multi-modal adversarial autoencoders for recommendation and evaluate them on two different tasks: citation recommendation and subject label recommendation. We analyze th…

cs.LG20196 cited

Can Graph Neural Networks Go "Online"? An Analysis of Pretraining and Inference

Lukas Galke, Iacopo Vagliano, Ansgar Scherp

Large-scale graph data in real-world applications is often not static but dynamic, i. e., new nodes and edges appear over time. Current graph convolution approaches are promising,…

cs.CL20193 cited

CBOW Is Not All You Need: Combining CBOW with the Compositional Matrix Space Model

Florian Mai, Lukas Galke, Ansgar Scherp

Continuous Bag of Words (CBOW) is a powerful text embedding method. Due to its strong capabilities to encode word content, CBOW embeddings perform well on a wide range of downstrea…