ICLR 2020: Yann LeCun and Energy-Based Models
Machine Learning Street Talk (MLST)
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Exploring Energy-Based Models in NLP
This chapter examines energy-based models and their applications in natural language processing, contrasting them with traditional probabilistic methods. It highlights the importance of optimizing energy functions, particularly through self-supervised learning techniques like BERT. Additionally, the discussion includes the concept of manifolds in machine learning, addressing how language representations evolve during neural network training.
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