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cs.LG2025
LLM Embeddings for Deep Learning on Tabular Data
Boshko Koloski, Andrei Margeloiu, Xiangjian Jiang +3
Tabular deep-learning methods require embedding numerical and categorical input features into high-dimensional spaces before processing them. Existing methods deal with this hetero…
cs.LG2025
HorNets: Learning from Discrete and Continuous Signals with Routing Neural Networks
Boshko Koloski, Nada Lavrač, Blaž Škrlj
Construction of neural network architectures suitable for learning from both continuous and discrete tabular data is a challenging research endeavor. Contemporary high-dimensional…