https://medium.com/@adi.fu7/ai-accelerators-part-ii-transistors-and-pizza-or-why-do-we-need-accelerators-75738642fdaa AI Accelerators — Part II: Transistors and Pizza (or: Why Do We Need Accelerators)?Adi FuchsDec 5 “There can not be a “one size fits all” approach when it comes
What I Read: Interpretable Time Series
https://ai.googleblog.com/2021/12/interpretable-deep-learning-for-time.html Interpretable Deep Learning for Time Series ForecastingMonday, December 13, 2021Posted by Sercan O. Arik, Research Scientist and Tomas Pfister, Engineering Manager, Google Cloud “Multi-horizon forecasting, i.e. predicting variables-of-interest at
What I Read: Dataset Distillation
https://ai.googleblog.com/2021/12/training-machine-learning-models-more.html Training Machine Learning Models More Efficiently with Dataset DistillationWednesday, December 15, 2021Posted by Timothy Nguyen1, Research Engineer and Jaehoon Lee, Senior Research Scientist, Google Research“For a machine learning (ML)
What I Read: Neural-Control Family
https://www.gshi.me/blog/NeuralControl/ Neural-Control Family: What Deep Learning + Control Enables in the Real WorldGuanya Shi “…is machine learning (especially deep learning) really ready to be deployed in safety-critical systems?”
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: Brains Predict Their Perceptions
https://www.quantamagazine.org/to-be-energy-efficient-brains-predict-their-perceptions-20211115/ To Be Energy-Efficient, Brains Predict Their PerceptionsAnil AnanthaswamyNovember 15, 2021 “Results from neural networks support the idea that brains are “prediction machines” — and that they work that way