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Found 6 papers, 1 papers with code
MUC: Mixture of Uncalibrated Cameras for Robust 3D Human Body Reconstruction
Next, instead of simply averaging models across views, we train a network to determine the weights of individual views for their fusion, based on the parameters estimated for joints and hands of human body as well as camera positions.
Provable Tensor Completion with Graph Information
For the model, we establish a rigorous mathematical representation of the dynamic graph, based on which we derive a new tensor-oriented graph smoothness regularization.
Efficient Fraud Detection Using Deep Boosting Decision Trees
In this paper, we propose deep boosting decision trees (DBDT), a novel approach for fraud detection based on gradient boosting and neural networks.
Effective Streaming Low-tubal-rank Tensor Approximation via Frequent Directions
Low-tubal-rank tensor approximation has been proposed to analyze large-scale and multi-dimensional data.
Universal Consistency of Deep Convolutional Neural Networks
Compared with avid research activities of deep convolutional neural networks (DCNNs) in practice, the study of theoretical behaviors of DCNNs lags heavily behind.
SPLBoost: An Improved Robust Boosting Algorithm Based on Self-paced Learning
Specifically, the underlying loss being minimized by the traditional AdaBoost is the exponential loss, which is proved to be very sensitive to random noise/outliers.
