Research notes
Blog
Research notes and explainers, organised into sections. Pick a topic below.
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<p class="lead" data-reveal style="margin-bottom:2rem;">A working notebook on my research and the machine learning behind it: summaries of my own papers and the related work I build on, short explainers for the core algorithms, and the mathematics underneath them — written to be readable without a machine-learning background.</p>
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<h3><a href="/blog/paper-summaries.html">My paper summaries</a></h3>
<p class="muted">Plain-language write-ups of my five papers — the problem, what I did, and what came out.</p>
<p style="margin-top:.8rem;"><a class="btn btn-outline" href="/blog/paper-summaries.html">Open →</a></p>
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<h3><a href="/blog/related-work.html">Other papers I build on</a></h3>
<p class="muted">Short notes on the external work my methods extend or compare against (CTGAN, GAIN, transformers, …).</p>
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<h3><a href="/blog/algorithms.html">Algorithms, explained</a></h3>
<p class="muted">One-paragraph explainers for the building blocks: trees, MLPs, LSTMs, autoencoders, GANs, attention, fusion.</p>
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<h3><a href="/blog/math.html">Mathematical foundations</a></h3>
<p class="muted">The recurring equations — softmax, cross-entropy, gradient descent, the GAN objective, KL/JS, ROC/AUC, recall@k.</p>
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<h3><a href="/blog/glossary.html">Glossary</a></h3>
<p class="muted">Quick definitions for the terms used across the papers and notes.</p>
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<h3><a href="/blog/reading-list.html">Reading list</a></h3>
<p class="muted">The books, courses and sites I point people to for the full version.</p>
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