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RWKV docs
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<h4>✨ <a href="conv_mixer/index.html">ConvMixer</a></h4>
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<h4>✨ <a href="capsule_networks/index.html">Capsule Networks</a></h4>
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<h4>✨ <a href="unet/index.html">U-Net</a></h4>
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<h4>✨ <a href="sketch_rnn/index.html">RNNs</a></h4>
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<ul><li><a href="rwkv/index.html">RWKV</a> </li>
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<li><a href="sketch_rnn/index.html">Sketch RNN</a></li></ul>
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<h4>✨ <a href="sketch_rnn/index.html">Sketch RNN</a></h4>
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<h4>✨ Graph Neural Networks</h4>
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<ul><li><a href="graphs/gat/index.html">Graph Attention Networks (GAT)</a> </li>
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<li><a href="graphs/gatv2/index.html">Graph Attention Networks v2 (GATv2)</a></li></ul>
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<ul><li><a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2204.10628.pdf">Autoregressive Search Engines: Generating Substrings as Document Identifiers</a> </li>
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<li><a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2203.15556.pdf">Training Compute-Optimal Large Language Models</a> </li>
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<li><a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/1910.02054.pdf">ZeRO: Memory Optimizations Toward Training Trillion Parameter Models</a> </li>
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<li><a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/RWKV.pdf">RWKV: Reinventing RNNs for the Transformer Era</a> </li>
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<li><a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2204.02311.pdf">PaLM: Scaling Language Modeling with Pathways</a> </li>
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<li><a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/dall-e-2.pdf">Hierarchical Text-Conditional Image Generation with CLIP Latents</a> </li>
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<li><a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2203.14465.pdf">STaR: Self-Taught Reasoner Bootstrapping Reasoning With Reasoning</a> </li>
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