Results 281 to 290 of about 13,581,527 (336)

Image Super-Resolution Using Very Deep Residual Channel Attention Networks

European Conference on Computer Vision, 2018
Convolutional neural network (CNN) depth is of crucial importance for image super-resolution (SR). However, we observe that deeper networks for image SR are more difficult to train.
Yulun Zhang, Kunpeng Li, Kai Li, Lichen Wang, Bineng Zhong, Y. Fu   +5 more
semanticscholar   +1 more source

Graph Attention Networks

International Conference on Learning Representations, 2017
We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their ...
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, Yoshua Bengio   +5 more
semanticscholar   +1 more source

Modeling Relational Data with Graph Convolutional Networks

Extended Semantic Web Conference, 2017
Knowledge graphs enable a wide variety of applications, including question answering and information retrieval. Despite the great effort invested in their creation and maintenance, even the largest (e.g., Yago, DBPedia or Wikidata) remain incomplete.
M. Schlichtkrull, Thomas Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, M. Welling   +5 more
semanticscholar   +1 more source

Networked Networks

International Studies of Management & Organization, 1997
Interfirm networks are an increasingly popular organizational form not only in manufacturing industries such as construction (Eccles, 1981), apparel (Uzzi, 1996), semiconductors (Saxenian, 1994; Voskamp and Wittke, 1994), or biotechnology (Powell, Koput, and Smith-Doerr, 1996), but also in service industries such as entertainment (Faulkner and Anderson,
Jörg Sydow, Bennet Van Well, Arnold Windeler   +2 more
openaire   +1 more source

Networking, Networking, Networking, Networking, Networking

Nature, 2004
Drinks with a few dozen friends or a visit to an interesting employer: Myrna Watanabe meets groups finding informal ways into work.
openaire   +2 more sources

Identity Mappings in Deep Residual Networks

European Conference on Computer Vision, 2016
Deep residual networks have emerged as a family of extremely deep architectures showing compelling accuracy and nice convergence behaviors. In this paper, we analyze the propagation formulations behind the residual building blocks, which suggest that the
Kaiming He, X. Zhang, Shaoqing Ren, Jian Sun   +3 more
semanticscholar   +1 more source

Networks, Network Governance, and Networked Networks

International Review of Public Administration, 2006
Theories of democratic governance have undergone significant changes over the last two decades with the spread of ideas and popular practices associated with New Public Management (NPM) and New Governance. In particular, inter-ministerial and inter-societal networking is becoming important because of the capacity to regulate complex transactional ...
openaire   +1 more source

Networking

Public Health Nursing, 1984
AbstractNetworking involves establishing and using contacts for information, support, and other assistance in order to achieve career goals. Public health nurses must understand the concept, process, and techniques of networking. There are do's and don'ts for effective networking, as well as methods of organizing nurses' networking efforts.
openaire   +3 more sources

XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks

European Conference on Computer Vision, 2016
We propose two efficient approximations to standard convolutional neural networks: Binary-Weight-Networks and XNOR-Networks. In Binary-Weight-Networks, the filters are approximated with binary values resulting in 32\(\times \) memory saving.
Mohammad Rastegari, Vicente Ordonez, J. Redmon, Ali Farhadi   +3 more
semanticscholar   +1 more source

Temporal Segment Networks: Towards Good Practices for Deep Action Recognition

European Conference on Computer Vision, 2016
Deep convolutional networks have achieved great success for visual recognition in still images. However, for action recognition in videos, the advantage over traditional methods is not so evident.
Limin Wang, Yuanjun Xiong, Zhe Wang, Y. Qiao, Dahua Lin, Xiaoou Tang, L. Gool   +6 more
semanticscholar   +1 more source