A Correlation Maximization Approach for Cross Domain Co-Embeddings. - Dan Shiebler (CoRR, 2018)

Fighting Redundancy and Model Decay with Embeddings. - Dan Shiebler; Luca Belli; Jay Baxter; Hanchen Xiong; Abhishek Tayal (CoRR, 2018)

Learning what and where to attend. - Drew Linsley; Dan Shiebler; Sven Eberhardt; Thomas Serre (ICLR (Poster), 2019)

Tuning Word2vec for Large Scale Recommendation Systems. - Benjamin Paul Chamberlain; Emanuele Rossi; Dan Shiebler; Suvash Sedhain; Michael M. Bronstein (RecSys, 2020)

Incremental Monoidal Grammars. - Dan Shiebler; Alexis Toumi; Mehrnoosh Sadrzadeh (CoRR, 2020)

Developments in AI and Machine Learning for Neuroimaging. - Shane O'Sullivan; Fleur Jeanquartier; Claire Jean-Quartier; Andreas Holzinger; Dan Shiebler; Pradip Moon; Claudio Angione (AI and ML for Digital Pathology, 2020)

Generalized Optimization: A First Step Towards Category Theoretic Learning Theory. - Dan Shiebler (ICO, 2021)

Categorical Stochastic Processes and Likelihood. - Dan Shiebler (Compositionality, 2021)

Functorial Manifold Learning and Overlapping Clustering. - Dan Shiebler (ACT, 2021)

Flattening Multiparameter Hierarchical Clustering Functors. - Dan Shiebler (CoRR, 2021)

Lessons Learned Addressing Dataset Bias in Model-Based Candidate Generation at Twitter. - Alim Virani; Jay Baxter; Dan Shiebler; Philip Gautier; Shivam Verma; Yan Xia; Apoorv Sharma; Sumit Binnani; Linlin Chen; Chenguang Yu (CoRR, 2021)

Category Theory in Machine Learning. - Dan Shiebler; Bruno Gavranovic; Paul W. Wilson 002 (CoRR, 2021)

Kan Extensions in Data Science and Machine Learning. - Dan Shiebler (CoRR, 2022)