Results 31 to 40 of about 2,204,167 (303)
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
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
On Invariance and Selectivity in Representation Learning [PDF]
, 2015 We discuss data representation which can be learned automatically from data, are invariant to transformations, and at the same time selective, in the sense that two points have the same representation only if they are one the transformation of the other.
Anselmi, Fabio +2 more
core +3 more sources
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
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
Review on heterogeneous network representation learning method
Journal of Hebei University of Science and Technology, 2021 Most of the real-life networks are heterogeneous networks that contain multiple types of nodes and edges, and heterogeneous networks integrate more information and contain richer semantic information than homogeneous networks.
Jianxia WANG +3 more
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
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