Learning Semantic Image Representations at a Large Scale

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Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) (2015, background): “The difference is small because activation functions generally have only minor inﬂuence on the overall training time (Jia, 2014).”
MeshCNN: a network with an edge (2019, methodology): “Following their success in the image domain, extensions to the 3D domain were quickly made, with shapes represented and processed predominately in voxel [Li et al. 2017; Wu et al. 2015] or point cloud form [Qi et al. 2017b].”
Occlumency: Privacy-preserving Remote Deep-learning Inference Using SGX (2019, methodology): “In particular, im2col-based convolution lowering [23, 47] is widely used in various DL frameworks including Caffe and TensorFlow [14].”
SphereNet: Learning Spherical Representations for Detection and Classification in Omnidirectional Images (2018, methodology): “These look-up tables are used in a customized convolution operation which is based on highly optimized general matrix multiply (GEMM) functions [13].”