Using topological autoencoders as a filtering function for global and local topology. - Filip Cornell (CoRR, 2020)

NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation. - Kaustubh D. Dhole; Varun Gangal; Sebastian Gehrmann; Aadesh Gupta; Zhenhao Li; Saad Mahamood; Abinaya Mahendiran; Simon Mille; Ashish Srivastava; Samson Tan; Tongshuang Wu; Jascha Sohl-Dickstein; et al. (CoRR, 2021)

Symbolic Hyperdimensional Vectors with Sparse Graph Convolutional Neural Networks. - Filip Cornell; Jussi Karlgren; Animesh; Sarunas Girdzijauskas (IJCNN, 2022)

Challenging the Assumption of Structure-based embeddings in Few- and Zero-shot Knowledge Graph Completion. - Filip Cornell; Chenda Zhang; Jussi Karlgren; Sarunas Girdzijauskas (LREC, 2022)

Unsupervised Ontology- and Taxonomy Construction Through Hyperbolic Relational Domains and Ranges. - Filip Cornell; Yifei Jin; Jussi Karlgren; Sarunas Girdzijauskas (PKDD/ECML Workshops (3), 2023)

Are We Wasting Time? A Fast, Accurate Performance Evaluation Framework for Knowledge Graph Link Predictors. - Filip Cornell; Yifei Jin; Jussi Karlgren; Sarunas Girdzijauskas (ICDE, 2025)

Expressivity of Representation Learning on Continuous-Time Dynamic Graphs: An Information-Flow Centric Review. - Sofiane Ennadir; Gabriela Zarzar Gandler; Filip Cornell; Lele Cao; Oleg Smirnov; Tianze Wang; Levente Zólyomi; Björn Brinne; Sahar Asadi (Trans. Mach. Learn. Res., 2025)

On the Power of Heuristics in Temporal Graphs. - Filip Cornell; Oleg Smirnov; Gabriela Zarzar Gandler; Lele Cao (CoRR, 2025)

Are We Really Measuring Progress? Transferring Insights from Evaluating Recommender Systems to Temporal Link Prediction. - Filip Cornell; Oleg Smirnov; Gabriela Zarzar Gandler; Lele Cao (CoRR, 2025)