2 papers
cs.IR2025
BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation
Christos Tsirigotis, Vaibhav Adlakha, Joao Monteiro +2
Neural sentence embedding models for dense retrieval typically rely on binary relevance labels, treating query-document pairs as either relevant or irrelevant. However, real-world…
cs.CV2025
Compositional Discrete Latent Code for High Fidelity, Productive Diffusion Models
Samuel Lavoie, Michael Noukhovitch, Aaron Courville
We argue that diffusion models' success in modeling complex distributions is, for the most part, coming from their input conditioning. This paper investigates the representation us…