Google AI Chief Dean Sees Evolution In MLPerf Benchmark For Machine Learning

Our work on SpineNet describes a meta-learned architecture that can retain spatial information more effectively, allowing detection to be done at finer resolution. We also focused on learning effective architectures for a variety of video classification problems. AssembleNet: Searching for Multi-Stream Neural Connectivity in Video Architectures, AssembleNet++: Assembling Modality Representations via Attention Connections, and AttentionNAS: Spatiotemporal Attention Cell Search for Video Classification demonstrate how to use evolutionary algorithms to create novel state-of-the-art video processing machine learning architectures.

Google AI chief Dean sees evolution in MLPerf benchmark for machine learning

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