[32] Andre Martins - The Geometry of Constrained Structured Prediction
The Thesis Review
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SparseMax and Advanced Inference Techniques
This chapter explores the SparseMax concept and its role in generating sparse probability vectors for neural networks, while also discussing the significance of hybrid discrete-continuous symbols. It highlights foundational theories in measure theory, entropy, and KL divergence, as well as introduces advancements like the AD3 inference algorithm. The chapter delves into the challenges of approximate marginal inference and the optimization algorithms that enhance performance in neural network tasks.
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