• AlexNet

  • Referenced in 474 articles [sw38522]
  • result, the network has learned rich feature representations for a wide range of images...
  • word2vec

  • Referenced in 205 articles [sw14978]
  • computing vector representations of words. These representations can be subsequently used in many natural language ... training text data and then learns vector representation of words. The resulting word vector file ... many natural language processing and machine learning applications...
  • node2vec

  • Referenced in 79 articles [sw27202]
  • research in the broader field of representation learning has led to significant progress in automating ... node2vec, an algorithmic framework for learning continuous feature representations for nodes in networks. In node2vec ... exploring neighborhoods is the key to learning richer representations. We demonstrate the efficacy of node2vec ... efficiently learning state-of-the-art task-independent representations in complex networks...
  • DeepWalk

  • Referenced in 63 articles [sw39604]
  • DeepWalk: Online Learning of Social Representations ... present DeepWalk, a novel approach for learning latent representations of vertices in a network. These ... latent representations encode social relations in a continuous vector space, which is easily exploited ... obtained from truncated random walks to learn latent representations by treating walks as the equivalent...
  • GloVe

  • Referenced in 94 articles [sw26211]
  • Global Vectors for Word Representation. GloVe is an unsupervised learning algorithm for obtaining vector representations...
  • InfoGAN

  • Referenced in 22 articles [sw40936]
  • InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets. This paper describes InfoGAN ... Adversarial Network that is able to learn disentangled representations in a completely unsupervised manner. InfoGAN ... CelebA face dataset. Experiments show that InfoGAN learns ... interpretable representations that are competitive with representations learned by existing fully supervised methods...
  • GraRep

  • Referenced in 22 articles [sw32342]
  • GraRep: Learning Graph Representations with Global Structural Information. In this paper, we present GraRep ... novel model for learning vertex representations of weighted graphs. This model learns low dimensional vectors ... citation network and show that our learned global representations can be effectively used as features...
  • DeepFace

  • Referenced in 25 articles [sw21625]
  • piecewise affine transformation, and derive a face representation from a nine-layer deep neural network ... more than 4, 000 identities. The learned representations coupling the accurate model-based alignment with...
  • struc2vec

  • Referenced in 13 articles [sw36495]
  • struc2vec: Learning Node Representations from Structural Identity. Structural identity is a concept of symmetry ... recently has it been addressed with representational learning techniques. This work presents struc2vec, a novel ... flexible framework for learning latent representations for the structural identity of nodes. struc2vec uses ... state-of-the-art techniques for learning node representations fail in capturing stronger notions...
  • foba

  • Referenced in 30 articles [sw35840]
  • Adaptive Forward-Backward Greedy Algorithm for Learning Sparse Representations...
  • graph2vec

  • Referenced in 10 articles [sw32340]
  • Representations of Graphs. Recent works on representation learning for graph structured data predominantly focus ... learning distributed representations of graph substructures such as nodes and subgraphs. However, many graph analytics ... classification, clustering and even seeding supervised representation learning approaches. Our experiments on several benchmark ... classification and clustering accuracies over substructure representation learning approaches and are competitive with state...
  • metapath2vec

  • Referenced in 10 articles [sw37749]
  • metapath2vec: Scalable Representation Learning for Heterogeneous Networks. We study the problem of representation learning ... embedding techniques. We develop two scalable representation learning models, namely metapath2vec and metapath2vec++. The metapath2vec...
  • PRISM

  • Referenced in 39 articles [sw23359]
  • help of the EM learning algorithm. As a knowledge representation language appropriate for probabilistic reasoning ... framework. We show by examples, together with learning results, that most popular probabilistic modeling formalisms...
  • PTE

  • Referenced in 7 articles [sw37756]
  • that these text embedding methods learn the representation of text in a fully unsupervised ... task. Although the low dimensional representations learned are applicable to many different tasks, they ... proposing a semi-supervised representation learning method for text data, which we call the extit...
  • SVMlight

  • Referenced in 264 articles [sw04076]
  • functions [Joachims, 2002c]. The goal is to learn a function from preference examples, so that ... leads to a very compact and efficient representation...
  • HIN2Vec

  • Referenced in 6 articles [sw37750]
  • paths in Heterogeneous Information Networks for Representation Learning. In this paper, we propose a novel ... representation learning framework, namely HIN2Vec, for heterogeneous information networks (HINs). The core of the proposed ... examined. To validate our ideas, we learn latent vectors of nodes using four large-scale ... outperforms the state-of-the-art representation learning models for network data, including DeepWalk, LINE...
  • SchNet

  • Referenced in 9 articles [sw40890]
  • ideally suited to learn representations for structured data and speed up the exploration of chemical ... those layers in SchNet: a novel deep learning architecture modeling quantum interactions in molecules...
  • i-RevNet

  • Referenced in 6 articles [sw29632]
  • difficulty of recovering images from their hidden representations, in most commonly used network architectures ... necessary condition to learn representations that generalize well on complicated problems, such as ImageNet ... inverse. An analysis of i-RevNets learned representations suggests an alternative explanation for the success ... model learned by the i-RevNet we reconstruct linear interpolations between natural image representations...
  • OctNet

  • Referenced in 10 articles [sw36665]
  • OctNet: Learning Deep 3D Representations at High Resolutions. We present OctNet, a representation for deep ... learning with sparse 3D data. In contrast to existing models, our representation enables 3D convolutional...
  • Geometer's Sketchpad

  • Referenced in 228 articles [sw04858]
  • through college—a tangible, visual way to learn mathematics that increases their engagement, understanding ... functional relationships through numeric, tabular, and graphical representations. And high school students can use Sketchpad...