Neural Networks for Activity Recognition in Mobile Social Media – In this paper, we study the problem of finding the most probable state of a set of spatio-temporally coherent entities in the given temporal scene. This task is typically seen as a quadratic process that requires a very large number of distinct features and can be performed in many cases from multiple approaches. However, there are a number of plausible models that are able to cope with this problem. In this paper, we propose a novel nonlinear nonconvex algorithm (n-CNN) based on the structure of entity and entity information and nonconvexity of the output space. The model has the ability to deal with uncertainty and ambiguity in the source data and can be used for generating new entities in the future. The model is able to perform the task efficiently, achieving a higher accuracy rate than the state-of-the-art approaches despite using only a very small collection of entity and entity information. We also present and analyze three nonlinear CNNs (one representing entity information and one representing entity output) and illustrate the performance of our model.
We investigate the use of latent variable models to train a machine-learned model to predict the location of objects. It is generally defined as a nonlinear network structure, and the network structure often consists of a fixed number of variables. In this paper, we model the network structure of a latent variable model and show that the network structure, in the latent space, is important to the learning task. We model the network structure of the model, which consists of one feature, multiple variables, and a fixed dimensionality measure (e.g., k-fold weight). The dimensionality measure is used to infer which variable is most relevant for the model. Extensive evaluation on both synthetic and real data shows that the proposed algorithm obtains superior performance in the real world. Experiments on ImageNet and BIDS demonstrate that the proposed algorithm consistently produces superior results compared to the state of the art.
Recurrent Reinforcement Learning with Spatially-Varying Recurrent Neural Networks
On the Relation Between the Matrix Symmetry Transform and Image Restoration
Neural Networks for Activity Recognition in Mobile Social Media
Quantum Combinatorial Subspace: Part II: Completing the Stack
Robust Learning of Spatial Context-Dependent KernelsWe investigate the use of latent variable models to train a machine-learned model to predict the location of objects. It is generally defined as a nonlinear network structure, and the network structure often consists of a fixed number of variables. In this paper, we model the network structure of a latent variable model and show that the network structure, in the latent space, is important to the learning task. We model the network structure of the model, which consists of one feature, multiple variables, and a fixed dimensionality measure (e.g., k-fold weight). The dimensionality measure is used to infer which variable is most relevant for the model. Extensive evaluation on both synthetic and real data shows that the proposed algorithm obtains superior performance in the real world. Experiments on ImageNet and BIDS demonstrate that the proposed algorithm consistently produces superior results compared to the state of the art.