Search results with tag "Representation learning"
Graph Representation Learning - McGill University School ...
www.cs.mcgill.calearning. We begin with a discussion of the goals of graph representation learning, as well as key methodological foundations in graph theory and network analysis. Follow-ing this, we introduce and review methods for learning node embeddings, including random-walk based methods and applications to knowledge graphs. We then provide
InfoGAN: Interpretable Representation Learning by ...
arxiv.orgrepresentation learning [1,2], whose goal is to use unlabelled data to learn a representation that exposes important semantic features as easily decodable factors. A method that can learn such representations is likely to exist [2], and to be useful for …
metapath2vec: Scalable Representation Learning for ...
www3.nd.edurepresentation learning methods enable the automatic discovery of useful and meaningful (latent) features from the “raw networks.” However, these work has thus far focused on representation learning for homogeneous networks—representative of singular type of nodes and relationships. Yet a large number of social and
A Simple Framework for Contrastive Learning of Visual ...
arxiv.orgRepresentation learning with contrastive cross entropy loss benefits from normalized embeddings and an appro-priately adjusted temperature parameter. Contrastive learning benefits from larger batch sizes and longer training compared to its supervised counterpart. Like supervised learning, contrastive learning benefits from deeper and wider ...
InfoGAN: Interpretable Representation Learning by ...
papers.nips.ccrepresentation learning [1,2], whose goal is to use unlabelled data to learn a representation that exposes important semantic features as easily decodable factors. A method that can learn such representations is likely to exist [2], and to be useful for …
Exploring Simple Siamese Representation Learning
openaccess.thecvf.comExploring Simple Siamese Representation Learning Xinlei Chen Kaiming He Facebook AI Research (FAIR) Abstract Siamese networks have become a common structure in various recent models for unsupervised visual representa-tion learning. These models maximize the similarity be-tween two augmentations of one image, subject to certain
Momentum Contrast for Unsupervised Visual Representation ...
openaccess.thecvf.comvised visual representation learning. From a perspective on contrastive learning [29] as dictionary look-up, we build a dynamic dictionary with a queue and a moving-averaged encoder. This enables building a large and consistent dic-tionary on-the-fly that facilitates contrastive unsupervised learning. MoCo provides competitive results under the
Graph Transformer Networks - NeurIPS
proceedings.neurips.ccremarkable success in representation learning, GNNs learn a powerful representation for given tasks and data. To improve performance or scalability, generalized convolution based on spectral convolution [4, 26], attention mechanism on neighbors [25, 33], subsampling [6, 7] and inductive representation for a large graph [14] have been studied.
Understanding Contrastive Representation Learning through ...
proceedings.mlr.pressrepresentation learning in fact directly optimizes for these two properties in the limit of infinite negative samples. We propose theoretically-motivated metrics for alignment and uniformity, and observe strong agreement between them and downstream …
Introduction - Deep Learning
www.deeplearningbook.orghuman time and e ffort; it can take decades for an entire community of researchers. The quintessential example of a representation learning algorithm is the au-toencoder. An autoencoder is the combination of an encoder function that converts the input data into a different representation, and a decoder function
Neural Discrete Representation Learning
arxiv.org1 Introduction Recent advances in generative modelling of images [38, 12, 13, 22, 10], audio [37, 26] and videos [20, 11] have yielded impressive samples and applications [24, 18]. At the same time, challenging tasks such as few-shot learning [34], domain adaptation [17], or reinforcement learning [35] heavily
Representation Learning on Graphs: Methods and Applications
www-cs.stanford.edunumber of common friends. Or in the case of node classification, one might want to include information about the global position of a node in the graph or the structure of the node’s local graph neighborhood (Figure 1), and there is no straightforward way to encode this information into a feature vector.