https://www.quantamagazine.org/researchers-build-ai-that-builds-ai-20220125/ Researchers Build AI That Builds AIAnil AnanthaswamyJanuary 25, 2022“By using hypernetworks, researchers can now preemptively fine-tune artificial neural networks, saving some of the time and expense of training.”
What I Read: Engineering Trade-Offs in Automatic Differentiation
https://www.stochasticlifestyle.com/engineering-trade-offs-in-automatic-differentiation-from-tensorflow-and-pytorch-to-jax-and-julia/ Engineering Trade-Offs in Automatic Differentiation: from TensorFlow and PyTorch to Jax and JuliaDecember 25 2021Author: Christopher Rackauckas “To understand the differences between automatic differentiation libraries, let’s talk about the
What I Read: Graph Neural Networks, Differential Geometry, Algebraic Topology
https://towardsdatascience.com/graph-neural-networks-through-the-lens-of-differential-geometry-and-algebraic-topology-3a7c3c22d5f Graph Neural Networks through the lens of Differential Geometry and Algebraic TopologyMichael Bronstein “Differential geometry and algebraic topology are not encountered very frequently in mainstream machine learning… tools from
What I Read: Understanding Convolutions on Graphs
https://distill.pub/2021/understanding-gnns/ Understanding Convolutions on GraphsUnderstanding the building blocks and design choices of graph neural networks.Ameya DaigavaneBalaraman RavindranGaurav AggarwalGoogle ResearchSept. 2, 202110.23915/distill.00032 “Graph neural networks (GNNs) are a family of neural