Neural Information Processing Systems
Found 4 free book(s)arXiv:1706.02216v4 [cs.SI] 10 Sep 2018
arxiv.org31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA. arXiv:1706.02216v4 [cs.SI] 10 Sep 2018. Figure 1: Visual illustration of the GraphSAGE sample and aggregate approach. recognize structural properties of a node’s neighborhood that reveal both the node’s local role in the
Non-Local Neural Networks
openaccess.thecvf.comimage processing (e.g., [12, 4]), has been largely overlooked in recent neural networks for computer vision. Video classification architectures. A natural solution to video classification is to combine the success of CNNs for images and RNNs for sequences [55, 11]. In contrast, feed-forward models are achieved by 3D convolutions (C3D)
1 DeepLab: Semantic Image Segmentation with Deep ...
arxiv.orgrating richer information from context [45] and structured prediction techniques [22], [26], [27], [46], but the perfor-mance of these systems has always been compromised by the limited expressive power of the features. Over the past few years the breakthroughs of Deep Learning in image classification were quickly transferred to the semantic seg-
Generative Adversarial Nets - NeurIPS
proceedings.neurips.ccgenerator network with a second neural network. Unlike generative adversarial networks, the sec-ond network in a VAE is a recognition model that performs approximate inference. GANs require differentiation through the visible units, and thus cannot model discrete data, while VAEs require