ICLR 2020: Yann LeCun and Energy-Based Models
Machine Learning Street Talk (MLST)
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Exploring Manifolds and Machine Learning
This chapter examines latent variables and energy functions in machine learning, focusing on Siamese networks and SIM CLR algorithms. It discusses the challenges of generalization and the complexities of language representation in BERT models, emphasizing the importance of training samples and task interactions for effective learning. The chapter highlights the significance of smooth energy functions and the limitations posed by probabilistic methods in model training.
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