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‎docs/cfr/kuhn/index.html‎

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<div class='section-link'>
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<a href='#section-57'>#</a>
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<p>Set models for saving </p>
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<p>Start the experiment </p>
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<div class="highlight"><pre><span class="lineno">242</span> <span class="n">experiment</span><span class="o">.</span><span class="n">add_model_savers</span><span class="p">({</span><span class="s1">&#39;info_sets&#39;</span><span class="p">:</span> <span class="n">InfoSetSaver</span><span class="p">(</span><span class="n">conf</span><span class="o">.</span><span class="n">cfr</span><span class="o">.</span><span class="n">info_sets</span><span class="p">)})</span></pre></div>
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<div class="highlight"><pre><span class="lineno">242</span> <span class="k">with</span> <span class="n">experiment</span><span class="o">.</span><span class="n">start</span><span class="p">():</span></pre></div>
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<a href='#section-58'>#</a>
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<p>Start the experiment </p>
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<p>Start iterating </p>
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<div class="highlight"><pre><span class="lineno">244</span> <span class="k">with</span> <span class="n">experiment</span><span class="o">.</span><span class="n">start</span><span class="p">():</span></pre></div>
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<div class="highlight"><pre><span class="lineno">244</span> <span class="n">conf</span><span class="o">.</span><span class="n">cfr</span><span class="o">.</span><span class="n">iterate</span><span class="p">()</span></pre></div>
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<a href='#section-59'>#</a>
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<p>Start iterating </p>
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</div>
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<div class="highlight"><pre><span class="lineno">246</span> <span class="n">conf</span><span class="o">.</span><span class="n">cfr</span><span class="o">.</span><span class="n">iterate</span><span class="p">()</span></pre></div>
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<div class='section-link'>
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<a href='#section-60'>#</a>
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</div>
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<p> </p>
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<div class="highlight"><pre><span class="lineno">250</span><span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
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<span class="lineno">251</span> <span class="n">main</span><span class="p">()</span></pre></div>
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<div class="highlight"><pre><span class="lineno">248</span><span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
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<span class="lineno">249</span> <span class="n">main</span><span class="p">()</span></pre></div>
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‎docs/index.html‎

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<li><a href="sampling/nucleus.html">Nucleus Sampling</a></li></ul>
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<h4>✨ <a href="scaling/index.html">Scalable Training/Inference</a></h4>
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<ul><li><a href="scaling/zero3/index.html">Zero3 memory optimizations</a></li></ul>
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<h2>Highlighted Research Paper PDFs</h2>
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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/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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<li><a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2112.04426.pdf">Improving language models by retrieving from trillions of tokens</a> </li>
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<li><a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2003.08934.pdf">NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis</a> </li>
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<li><a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/1706.03762.pdf">Attention Is All You Need</a> </li>
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<li><a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2006.11239.pdf">Denoising Diffusion Probabilistic Models</a> </li>
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<li><a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2109.08668.pdf">Primer: Searching for Efficient Transformers for Language Modeling</a> </li>
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<li><a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/1803.02999.pdf">On First-Order Meta-Learning Algorithms</a> </li>
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<li><a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2103.00020.pdf">Learning Transferable Visual Models From Natural Language Supervision</a> </li>
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<li><a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2109.02869.pdf">The Sensory Neuron as a Transformer: Permutation-Invariant Neural Networks for Reinforcement Learning</a> </li>
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<li><a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/1805.09801.pdf">Meta-Gradient Reinforcement Learning</a> </li>
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<li><a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/google_maps_eta.pdf">ETA Prediction with Graph Neural Networks in Google Maps</a> </li>
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<li><a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/ponder_net.pdf">PonderNet: Learning to Ponder</a> </li>
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<li><a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/muzero.pdf">Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model</a> </li>
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<li><a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/gans_n_roses.pdf">GANs N’ Roses: Stable, Controllable, Diverse Image to Image Translation (works for videos too!)</a> </li>
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<li><a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/vit.pdf">An Image is Worth 16X16 Word: Transformers for Image Recognition at Scale</a> </li>
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<li><a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/resnet.pdf">Deep Residual Learning for Image Recognition</a> </li>
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<li><a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/distillation.pdf">Distilling the Knowledge in a Neural Network</a></li></ul>
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<h3>Installation</h3>
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<pre class="highlight lang-bash"><code><span></span>pip<span class="w"> </span>install<span class="w"> </span>labml-nn</code></pre>
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<h3>Citing LabML</h3>
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<p>If you use this for academic research, please cite it using the following BibTeX entry.</p>
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<pre class="highlight lang-bibtex"><code><span></span><span class="nc">@misc</span><span class="p">{</span><span class="nl">labml</span><span class="p">,</span>
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<span class="w"> </span><span class="na">author</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{Varuna Jayasiri, Nipun Wijerathne}</span><span class="p">,</span>
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<span class="w"> </span><span class="na">title</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{labml.ai Annotated Paper Implementations}</span><span class="p">,</span>
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<span class="w"> </span><span class="na">year</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{2020}</span><span class="p">,</span>
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<span class="w"> </span><span class="na">url</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{}</span><span class="p">,</span>
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<span class="p">}</span></code></pre>
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