routing-transformer
conformer


routing-transformer | conformer | |
---|---|---|
1 | 1 | |
288 | 380 | |
1.4% | 3.7% | |
0.0 | 3.1 | |
over 3 years ago | over 1 year ago | |
Python | Python | |
MIT License | MIT License |
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routing-transformer
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Will Transformers Take over Artificial Intelligence?
I would recommend Routing Transformer https://github.com/lucidrains/routing-transformer but the real truth is nothing beats full attention. Luckily, someone recently figured out how to get past the memory bottleneck. https://github.com/lucidrains/memory-efficient-attention-pyt...
conformer
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[N] Conformer-1 - A state-of-the-art speech recognition model trained on 650K hours of data
Found relevant code at https://github.com/lucidrains/conformer + all code implementations here
What are some alternatives?
memory-efficient-attention-pyt
tab-transformer-pytorch - Implementation of TabTransformer, attention network for tabular data, in Pytorch
vit-pytorch - Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch
Perceiver - Implementation of Perceiver, General Perception with Iterative Attention
Compact-Transformers - Escaping the Big Data Paradigm with Compact Transformers, 2021 (Train your Vision Transformers in 30 mins on CIFAR-10 with a single GPU!)
uformer-pytorch - Implementation of Uformer, Attention-based Unet, in Pytorch
memory-efficient-attention-pytorch - Implementation of a memory efficient multi-head attention as proposed in the paper, "Self-attention Does Not Need O(n²) Memory"
Nystromformer - An implementation of the Nyströmformer, using Nystrom method to approximate standard self attention
enformer-pytorch - Implementation of Enformer, Deepmind's attention network for predicting gene expression, in Pytorch
mixture-of-experts - A Pytorch implementation of Sparsely-Gated Mixture of Experts, for massively increasing the parameter count of language models

