Deep Learning for Precise Spatio-temporal Game Analysis – Recent Convolutional Neural Networks (CNNs) have achieved quite good performance in many natural language processing tasks. However, they will not be the only one to suffer from this phenomenon. Many state-of-the-art models rely on large amounts of labeled data to compute and the output will be heavily dependent on the source domain. As it pertains to many tasks, it is important to develop a robust model with real-world datasets. This work aims to tackle these challenges by learning deep convolutional networks for image segmentation (an important task for both humans and computers). To train our model, we first develop an extensive set of fine-grained models, using a large number of labeled datasets, to automatically infer which model is the best. The experiments on CIFARS show that our model outperforms several state-of-the-art models in terms of accuracy, speed and the amount of data used.
We present a nonlinear model to model the temporal evolution of human knowledge about the world. Our approach is to first embed temporally related knowledge into the form of a multidimensional variable. We then embed the inter- and intra-variable covariate into a multidimensional structure in order to model the temporal motion in the multi-dimensional space. The multidimensional structure serves as a feature representation of multidimensional variables and represents temporally related variables in such a way that temporal evolution is also modeled as a multidimensional process of continuous evolution. The multidimensional structure is computed through a novel approach of learning from multidimensional features in a set of labeled items by using a multi-layer recurrent neural network. Experiments on large-scale public datasets show that we achieve state-of-the-art performance on real-world datasets.
Learning for Multi-Label Speech Recognition using Gaussian Processes
Tensorizing the Loss Weight for Accurate Multi-label Speech Recognition
Deep Learning for Precise Spatio-temporal Game Analysis
A new scoring approach based on Bayesian network of vowel sounds
Towards a better understanding of the intrinsic value of training topic modelsWe present a nonlinear model to model the temporal evolution of human knowledge about the world. Our approach is to first embed temporally related knowledge into the form of a multidimensional variable. We then embed the inter- and intra-variable covariate into a multidimensional structure in order to model the temporal motion in the multi-dimensional space. The multidimensional structure serves as a feature representation of multidimensional variables and represents temporally related variables in such a way that temporal evolution is also modeled as a multidimensional process of continuous evolution. The multidimensional structure is computed through a novel approach of learning from multidimensional features in a set of labeled items by using a multi-layer recurrent neural network. Experiments on large-scale public datasets show that we achieve state-of-the-art performance on real-world datasets.