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cs.LG2026
KL Divergence Between Gaussians: A Step-by-Step Derivation for the Variational Autoencoder Objective
Andrés Muñoz, Rodrigo Ramele
Kullback-Leibler (KL) divergence is a fundamental concept in information theory that quantifies the discrepancy between two probability distributions. In the context of Variational…
cs.LG2024
Black Box Meta-Learning Intrinsic Rewards
Octavio Pappalardo, Rodrigo Ramele, Juan Miguel Santos
The broader application of reinforcement learning (RL) is limited by challenges including data efficiency, generalization capability, and ability to learn in sparse-reward environm…