Sparse Depth Estimation via Sparse Gaussian Mixture Modeling and Constraint Optimization – We present a deep learning-based clustering method based on the Sparse Bayes algorithm. By leveraging the low-rank structure of the image, the proposed method achieves better clustering performance and better statistical performance than the known ones. The proposed method is designed to be able to perform dense-rank clustering as an efficient optimization algorithm, and our main contribution is in the construction of a sparsifying Bayesian network to capture the sparsity of the image in the sparse sparse structure of the data.
Kernel methods have proven to be well applied to many tasks. In this paper, we present the first implementation of kernel methods for the task of learning to learn.
T-distributed multi-objective regression with stochastic support vector machines
Learning to predict footballs using deep learning
Sparse Depth Estimation via Sparse Gaussian Mixture Modeling and Constraint Optimization
Automatic Dental Bioavailability test using hybrid method
Learning to Learn by Transfer Learning: An Application to Learning Natural Language to InteractionsKernel methods have proven to be well applied to many tasks. In this paper, we present the first implementation of kernel methods for the task of learning to learn.