Results 31 to 40 of about 2,210,762 (272)
Multi-Task Network Representation Learning
Frontiers in Neuroscience, 2020 Networks, such as social networks, biochemical networks, and protein-protein interaction networks are ubiquitous in the real world. Network representation learning aims to embed nodes in a network as low-dimensional, dense, real-valued vectors, and ...
Yu Xie +4 more
doaj +1 more source
Learning More Universal Representations for Transfer-Learning [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020 Submitted to IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
Tamaazousti, Youssef +4 more
openaire +4 more sources
The effect of representation location on interaction in a tangible learning environment [PDF]
, 2009 Drawing on the 'representation' TUI framework [21], this paper reports a study that investigated the concept of 'representation location' and its effect on interaction and learning.
Birkbeck College +4 more
core +5 more sources
Review of Visual Representation Learning [PDF]
Jisuanji kexueRepresentation learning is an important step of artificial intelligence algorithm,where well designed representation can boost downstream tasks.With the development of deep learning in computer vision,visual representation learning has become ...
WANG Shuaiwei, LEI Jie, FENG Zunlei, LIANG Ronghua
doaj +1 more source
A survey of information network representation learning
Journal of Hebei University of Science and Technology, 2020 The network representation learning algorithm represents the information network as a low-dimensional dense real vector carrying the characteristic information of network nodes, and is applied to the input of downstream machine learning tasks.
Junhao LU, Yunfeng XU
doaj +1 more source
Representation Learning by Learning to Count
, 2017 We introduce a novel method for representation learning that uses an artificial supervision signal based on counting visual primitives. This supervision signal is obtained from an equivariance relation, which does not require any manual annotation.
Favaro, Paolo +2 more
core +1 more source
Simplicial Complex Representation Learning
, 2021 Simplicial complexes form an important class of topological spaces that are frequently used in many application areas such as computer-aided design, computer graphics, and simulation. Representation learning on graphs, which are just 1-d simplicial complexes, has witnessed a great attention in recent years.
Hajij, Mustafa +4 more
openaire +3 more sources
An Optimized Network Representation Learning Algorithm Using Multi-Relational Data
Mathematics, 2019 Representation learning aims to encode the relationships of research objects into low-dimensional, compressible, and distributed representation vectors.
Zhonglin Ye +4 more
doaj +1 more source
Neural Discrete Representation Learning
, 2014 Learning useful representations without supervision remains a key challenge in machine learning. In this paper, we propose a simple yet powerful generative model that learns such discrete representations.
Hoon Choi +3 more
core +5 more sources
Learning Disentangled Discrete Representations
, 2023 Recent successes in image generation, model-based reinforcement learning, and text-to-image generation have demonstrated the empirical advantages of discrete latent representations, although the reasons behind their benefits remain unclear. We explore the relationship between discrete latent spaces and disentangled representations by replacing the ...
Friede, David +3 more
openaire +3 more sources