Paper Review: Set Transformers. A Framework for Attention-based… | by Himanshu Gupta | Toward Humanoids | Medium

Ongoing pre-publication research on humanoids and related technologies including paper reviews and tutorials. Follow publication Top highlight 119 Listen Share When working with unordered data, standard neural networks often falter, as they are designed to process structured, sequential, or grid-like inputs such as images or text. But what about problems where the input is a set, an inherently unordered collection of elements? This is where Set Transformers come into play. In this blog, we analyze the paper “Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks” by Lee et al., diving into its motivation, background, technical approach, experiments, and results. It was published in the Proceedings of the 36th International Conference on Machine Learning (ICML) in 2019. proceedings.mlr.press Let’s start with a simple clustering problem: Given a set of n points X = {x₁, x₂, x₃,…, xₙ} where these points are generated from k different Gaussian distributions,

A Framework for Attention-based Permutation-Invariant Neural Networks 10 min read Dec 15, 2024 -- When working with unordered data, standard neural networks often falter, as they are designed to process structured, sequential, or grid-like inputs such as images or text. But what about problems where the input is a set, an inherently unordered collection of elements? This is where Set Transformers come into play. In this blog, we analyze the paper “Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks” by Lee et al., diving into its motivation, background,…

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