
ICLR 2020: Yann LeCun and Energy-Based Models
Machine Learning Street Talk (MLST)
Exploring Energy-Based Models in Machine Learning
This chapter examines the complexities and roles of energy-based models in machine learning, highlighting the separation between inference and learning phases. It discusses the application of energy functions in logical statements and the relationship between various methodologies, such as K-means and GANs. Through a critical lens, the chapter emphasizes the significance of self-supervised learning and the need for deeper theoretical insights into these models.
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