InTDS ArchivebyCody Marie Wild·Sep 27, 2020A Search for Efficient Meta-Learning: MAMLs, Reptiles, and Related SpeciesIt’s an oft-lamented fact that the capabilities of modern machine learning tend to be narrow and brittle: while a given technique can be…A response icon1A response icon1
InTDS ArchivebyCody Marie Wild·Sep 8, 2019Conditional Love: The Rise of Renormalization Techniques for Neural Network ConditioningConditional renormalization is an oft-unsung technique powering many recent ML successes; how does it work and where did the idea come…A response icon2A response icon2
InTDS ArchivebyCody Marie Wild·Mar 9, 2019It’s Only Natural: An Excessively Deep Dive Into Natural Gradient OptimizationThis post gives an intuitive explanation of an approach called Natural Gradient, an elegant way to dynamically adjust gradient step size.A response icon9A response icon9
InTDS ArchivebyCody Marie Wild·Jan 21, 2019Generating, With Style: The Mechanics Behind NVIDIA’s Highly Realistic GAN ImagesA new architecture, designed to leverage noise and allow for precise configuration of global features, is generating impressive resultsA response icon1A response icon1
InTDS ArchivebyCody Marie Wild·Dec 27, 2018A Tale of Two Convolutions: Differing Design Paradigms for Graph Neural Networks“Graph” is a one of those terms that’s fallen prey to natural language’s tendency to be less precise than its mathematical counterpart.A response icon4A response icon4
InTDS ArchivebyCody Marie Wild·Oct 1, 2018Convolution: An Exploration of a Familiar Operator’s Deeper RootsIn the world of modern machine learning, the convolution operator occupies the strange position: it’s both trivially familiar to anyone…A response icon5A response icon5
InTDS ArchivebyCody Marie Wild·Jul 9, 2018The Pursuit of (Robotic) Happiness: How TRPO and PPO Stabilize Policy Gradient MethodsReinforcement Learning strikes me as the wild west of machine learning right now: a place full of drama and progress, with dreams of grand…A response icon5A response icon5
InTDS ArchivebyCody Marie Wild·May 7, 2018With Great Power Comes Poor Latent Codes: Representation Learning in VAEs (Pt. 2)(If you haven’t done so yet, I recommend going back and reading Part 1 of this series on VAE failure modes; I spent more time there…A response icon7A response icon7
InTDS ArchivebyCody Marie Wild·Apr 15, 2018What a Disentangled Net We Weave: Representation Learning in VAEs (Pt. 1)It’s a truth universally acknowledged: that data not in possession of labels must be in want of unsupervised learning.A response icon7A response icon7
InTDS ArchivebyCody Marie Wild·Mar 24, 2018Learning About Algorithms That Learn to LearnThe premise of meta learning was an intoxicating one to me, when I first of heard it: the project of building machines that are not only…A response icon2A response icon2
