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[32] Andre Martins - The Geometry of Constrained Structured Prediction

The Thesis Review

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Evolution of Sparse Learning in Neural Networks

This chapter explores the evolution of inference methods in neural networks, emphasizing the shift from strict output structures to leveraging advanced encoders that learn from data. It delves into the notions of structured sparsity and the impact of techniques like SparseMax on model interpretability and dynamic feature selection. The discussion also covers innovative applications and loss functions related to SparseMax, underscoring their advantages in natural language processing and generating sparse outputs.

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