alexeistepa.github.io/mathdl.html at main · alexeistepa/alexeistepa.github.io · GitHub

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<!DOCTYPE html>
<html>
	<head>
		<!--Template Used: http://jmcglone.com/guides/github-pages/#-->
		<title>MathDL</title>
		<!-- link to main stylesheet -->
		<!--link rel="stylesheet" type="text/css" href="/css/main.css"-->


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	<body>
			<h1>Cardiff Mathematics of Deep Learning reading group</h1>

		The Mathematics of Deep Learning reading group ran in the academic year 2022-2023 at Cardiff University School of Mathematics.
		A variety of topics related to machine learning were covered, from rigorous mathematical studies to contemporary methods and models.
		<br />
		<br />
		<div>
        <em> Foundations of Statistical Learning,</em> Alexei Stepanenko <br />
        2nd November 2022 <br />
        <strong> Reference(s): </strong>
        <ol>
				<li>  <a href = "https://arxiv.org/abs/2105.04026">Berner, Julius, et al. "The modern mathematics of deep learning." (2021)</a> </li>
          </ol>
        </div>
		<br />
		<div>
        <em> Neural Tangent Kernels,</em> Bertrand Gauthier <br />
        (Part 1) 09 November 2022 <br />
        (Part 2) 23 November 2022 <br />
        <strong> Reference(s): </strong>
        <ol>
				<li>  <a href = "https://arxiv.org/pdf/1806.07572.pdf">Jacot, Arthur, Franck Gabriel, and Clément Hongler. "Neural tangent kernel: Convergence and generalization in neural networks." Advances in neural information processing systems 31 (2018)</a> </li>
          </ol>
        </div>
		<br />
		<div>
        <em> Neural Sheaf Diffusion,</em> Álvaro Torras Casas <br />
        (Part 1) 07 December 2022 <br />
        (Part 2) 12 December 2022 <br />
        <strong> Reference(s): </strong>
        <ol>
				<li>  <a href = "https://arxiv.org/abs/2202.04579">Bodnar, Cristian, et al. "Neural sheaf diffusion: A topological perspective on heterophily and oversmoothing in gnns." Advances in Neural Information Processing Systems 35 (2022): 18527-18541</a> </li>
				<li>  <a href = "https://arxiv.org/abs/1609.02907">T. N. Kipf, M. Welling. Semi-Supervised Classification with Graph Convolutional Networks. ICLR (2017)</a> </li>
          </ol>
        </div>
		<br />
		<div>
        <em> Probabilistic neural networks and PAC-Bayes bounds,</em> Alexei Stepanenko <br />
        21st April 2023  <br />
        <strong> Reference(s): </strong>
        <ol>
				<li>  <a href = "https://arxiv.org/abs/2007.12911">Pérez-Ortiz, María, et al. "Tighter risk certificates for neural networks." The Journal of Machine Learning Research 22.1 (2021): 10326-10365</a> </li>
				<li>  <a href = "https://arxiv.org/abs/2110.11216">Alquier, Pierre. "User-friendly introduction to PAC-Bayes bounds." (2021) 10326-10365</a> </li>
          </ol>
        </div>
		<br />
		<div>
        <em> Self-Attention and Transformers,</em> Alexei Stepanenko <br />
        5th May 2023 <br />
        <strong> Reference(s): </strong>
        <ol>
				<li>  <a href = "https://arxiv.org/abs/1706.03762">Vaswani, Ashish, et al. "Attention is all you need." Advances in neural information processing systems 30 (2017)</a> </li>
          </ol>
        </div>
		<br />
		<div>
        <em> The Adam optimisation algorithm,</em> Matthew Hutchings <br />
        28th June 2023 <br />
        <strong> Reference(s): </strong>
        <ol>
				<li>  <a href = "https://arxiv.org/abs/1412.6980">Kingma, Diederik P., and Jimmy Ba. "Adam: A method for stochastic optimization." (2014)</a> </li>
          </ol>
        </div>
	</body>
</html>