Europe
Learning long-range spatial dependencies with horizontal gated recurrent units
Drew Linsley, Junkyung Kim, Vijay Veerabadran, Charles Windolf, Thomas Serre
We introduce a visual challenge, Pathfinder, and describe a novel recurrent neural network architecture called the horizontal gated recurrent unit (hGRU) to learn intrinsic horizontal connections - both within and across feature columns.
Efficient Stochastic Gradient Hard Thresholding
Pan Zhou, Xiaotong Yuan, Jiashi Feng
Neural Information Processing Systems http://nips.cc/
Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer
Wenzheng Chen, Huan Ling, Jun Gao, Edward Smith, Jaakko Lehtinen, Alec Jacobson, Sanja Fidler
In this paper, we presentDIB-R, a differentiable rendering framework which allows gradients to be analytically computed for allpixels inanimage.
Minimax statistical learning with Wasserstein distances
Recently, however, an alternative viewpoint has emerged, inspired by ideas from robust statistics and robust stochastic optimization.
