337 citations · 408 across the 4 of their papers we have counts for
5 papers
3D Semantic Segmentation with Submanifold Sparse Convolutional Networks
Benjamin Graham, Martin Engelcke, Laurens van der Maaten
Convolutional networks are the de-facto standard for analyzing spatio-temporal data such as images, videos, and 3D shapes. Whilst some of this data is naturally dense (e.g., photos…
Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55
Li Yi, Lin Shao, Manolis Savva +47
We introduce a large-scale 3D shape understanding benchmark using data and annotation from ShapeNet 3D object database. The benchmark consists of two tasks: part-level segmentation…
Submanifold Sparse Convolutional Networks
Benjamin Graham, Laurens van der Maaten
Convolutional network are the de-facto standard for analysing spatio-temporal data such as images, videos, 3D shapes, etc. Whilst some of this data is naturally dense (for instance…
Low-Precision Batch-Normalized Activations
Benjamin Graham
Artificial neural networks can be trained with relatively low-precision floating-point and fixed-point arithmetic, using between one and 16 bits. Previous works have focused on rel…
Efficient batchwise dropout training using submatrices
Ben Graham, Jeremy Reizenstein, Leigh Robinson
Dropout is a popular technique for regularizing artificial neural networks. Dropout networks are generally trained by minibatch gradient descent with a dropout mask turning off som…