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You are here		francisbach.com Rethinking SGD's noise - Machine Learning Research Blog
\|	\|	fa.bianp.net On the consistency of ordinal regression methods	12.8 parsecs away Travel
\|	\|	MathJax.Hub.Config({ extensions: ["tex2jax.js"], jax: ["input/TeX", "output/HTML-CSS"], tex2jax: { inlineMath: [ ['$','$'], ["\$","\$"] ], displayMath: [ ['$$','$$'], ["\\[","\\]"] ], processEscapes: true }, TeX: { equationNumbers: { autoNumber: "AMS" }, extensions: ["AMSmath.js", "AMSsymbols.js"] }, "HTML-CSS": { fonts: ["TeX"] } }); My latests work (with Francis Bach and Alexandre Gramfort) is on the consistency of ordinal regression methods. It has the wildly imaginative ...	12.8 parsecs away Travel
\|	\|	christopher-beckham.github.io EDM diffusion models - a Jupyter implementation, and how they are implemented in practice \| Christopher Beckham, PhD	13.5 parsecs away Travel
\|	\|	I wrote a self-contained implementation of NVIDIA's EDM diffusion model in a Jupyter notebook, as well as its associated sampling algorithms. I also discuss the rather confusing names used for real-world implementations of those algorithms.	13.5 parsecs away Travel
\|	\|	akosiorek.github.io Normalizing Flows	12.0 parsecs away Travel
\|	\|	Machine learning is all about probability.To train a model, we typically tune its parameters to maximise the probability of the training dataset under the mo...	12.0 parsecs away Travel
\|	\|	www.graphcore.ai Improving image generation and making LLMs think: October Papers	77.7 parsecs away Travel
\|		This month the Graphcore research team examines a new approaches to image generation and advances in LLMs.	77.7 parsecs away Travel