2,238 research outputs found
GPSP: Graph Partition and Space Projection based Approach for Heterogeneous Network Embedding
In this paper, we propose GPSP, a novel Graph Partition and Space Projection
based approach, to learn the representation of a heterogeneous network that
consists of multiple types of nodes and links. Concretely, we first partition
the heterogeneous network into homogeneous and bipartite subnetworks. Then, the
projective relations hidden in bipartite subnetworks are extracted by learning
the projective embedding vectors. Finally, we concatenate the projective
vectors from bipartite subnetworks with the ones learned from homogeneous
subnetworks to form the final representation of the heterogeneous network.
Extensive experiments are conducted on a real-life dataset. The results
demonstrate that GPSP outperforms the state-of-the-art baselines in two key
network mining tasks: node classification and clustering.Comment: WWW 2018 Poste
KINEMATICS ANALYSIS ON THE FOREHAND STROKE OF ATP TENNIS PLAYER KAREN KHACHANOV
By using the three-dimensional video analysis method to kinematical analyze the forehand stroke technique of the ATP professional tennis player, Karen Khachanov. Exploring the action mode and characteristics from him to provide a reference for all tennis learners to improve their forehand stroke. The results show that: (1) At the end of the preparation, Karen Khachanov’s center of gravity was lower, and his body control was steady; (2) After the follow through, each joint of Karen Khachanov’s racquet arm were stretched, so the swing radius is larger. His right leg was like a support point for the next stage---drive leg and rotate hip; (3) At the swing stage, the move of his driving leg and rotating hip was full, so the whole hitting action match the principle whipping technique movement. (4) The “wiper ” style follow through was natural, coordinated, and smooth. After hitting the ball, his body of control was steady, the shoulder movement was obvious, and the hitting was fast and powerful. Conclusion: The forehand stroke of Karen Khachanov was a high quality hitting action, and it’s worthy of tennis players to learn from
Conditional Random Fields as Recurrent Neural Networks
Pixel-level labelling tasks, such as semantic segmentation, play a central
role in image understanding. Recent approaches have attempted to harness the
capabilities of deep learning techniques for image recognition to tackle
pixel-level labelling tasks. One central issue in this methodology is the
limited capacity of deep learning techniques to delineate visual objects. To
solve this problem, we introduce a new form of convolutional neural network
that combines the strengths of Convolutional Neural Networks (CNNs) and
Conditional Random Fields (CRFs)-based probabilistic graphical modelling. To
this end, we formulate mean-field approximate inference for the Conditional
Random Fields with Gaussian pairwise potentials as Recurrent Neural Networks.
This network, called CRF-RNN, is then plugged in as a part of a CNN to obtain a
deep network that has desirable properties of both CNNs and CRFs. Importantly,
our system fully integrates CRF modelling with CNNs, making it possible to
train the whole deep network end-to-end with the usual back-propagation
algorithm, avoiding offline post-processing methods for object delineation. We
apply the proposed method to the problem of semantic image segmentation,
obtaining top results on the challenging Pascal VOC 2012 segmentation
benchmark.Comment: This paper is published in IEEE ICCV 201
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