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cs.LG2025
Energy Matching: Unifying Flow Matching and Energy-Based Models for Generative Modeling
Michal Balcerak, Tamaz Amiranashvili, Antonio Terpin +5
Current state-of-the-art generative models map noise to data distributions by matching flows or scores. A key limitation of these models is their inability to readily integrate ava…
cs.LG2024
Probabilistic Contrastive Learning with Explicit Concentration on the Hypersphere
Hongwei Bran Li, Cheng Ouyang, Tamaz Amiranashvili +3
Self-supervised contrastive learning has predominantly adopted deterministic methods, which are not suited for environments characterized by uncertainty and noise. This paper intro…