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Non-Parametric Transformers | Paper explained
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Paper Club with Dirk - TabPFN: A Transformer That Solves Small Tabular Classification Problems in...
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TokenFormer: Rethinking Transformer Scaling with Tokenized Model Parameters (Paper Explained)
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Rethinking Attention with Performers (Paper Explained)
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Background to Non Parametric Transformers Paper Explained
A deep dive into the TokenFormer and an opinion about its impact, novelty, and relation to prior work. fnet Do we even need Attention? FNets completely drop the Attention mechanism in favor of a simple Fourier ...
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Last Updated: June 17, 2026
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Paper Club with Dirk - TabPFN: A Transformer That Solves Small Tabular Classification Problems in...
TokenFormer: Rethinking Transformer Scaling with Tokenized Model Parameters
Rethinking Attention with Performers
FNet: Mixing Tokens with Fourier Transforms
Energy-Based Transformers are Scalable Learners and Thinkers
Linear Transformers Are Secretly Fast Weight Memory Systems
Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention
Transformers without Normalization
![[Paper Analysis] The Free Transformer (and some Variational Autoencoder stuff)](https://i0.wp.com/ytimg.googleusercontent.com/vi/Nao16-6l6dQ/mqdefault.jpg?resize=320,180)
[Paper Analysis] The Free Transformer
FNet: Mixing Tokens with Fourier Transforms – Paper Explained
RWKV: Reinventing RNNs for the Transformer Era
Attention in transformers, step-by-step | Deep Learning Chapter 6
∞-former: Infinite Memory Transformer
Stanford CS25: V1 I Self Attention and Non-parametric transformers
Sparse is Enough in Scaling Transformers | ML Research Paper Explained
Part 2 : GNNVis for visualizing large scale data
PostLN, PreLN and ResiDual Transformers
