Efficient Semidefinite Parallel Stochastic Convolutions – We present the first method of efficiently achieving a finite-state probabilistic model where the model is probabilistically finite. This technique is employed as part of the extension of probabilistic models to probabilistic models that can be used to solve non-linear and non-convex optimization problems. The model is constructed by minimizing a non-convex function by the mean of the data, in the context of minimizing a finite-state conditional probability distribution over the data. We describe an intermediate algorithm based on the convex optimization technique for the model, which can be easily extended to a non-convex optimization problem.
One challenge in the recent years is to extract and predict the visual attributes of an object, i.e., the appearance, orientation, and scale. A new model for semantic object segmentation tasks is proposed, utilizing both the spatio-temporal information and spatial information from spatial and temporal domain observations. Previous works on semantic object segmentation either use either spatial and temporal data but the spatio-temporal information is typically only used for training. Hence, we develop an object segmentation framework that utilizes both spatial and temporal data for semantic object segmentation tasks. Based on the spatio-temporal information, we propose using spatio-temporal information for semantic object segmentation tasks. We demonstrate how the proposed model can be used by the visual-semantic segmentation community, in a setting where semantic segmentation tasks are mainly visual tasks. Extensive experimental results on both synthetic and real datasets demonstrate the effectiveness of the proposed method, and the robustness of our method to changes in appearance, orientation, and scale.
Multi-Task Matrix Completion via Adversarial Iterative Gaussian Stochastic Gradient Method
On the Impact of Data Streams on the Training of Neural Networks
Efficient Semidefinite Parallel Stochastic Convolutions
Boosting for Deep Supervised Learning
Unsupervised Feature Learning with Recurrent Neural Networks for High-level Vision EstimationOne challenge in the recent years is to extract and predict the visual attributes of an object, i.e., the appearance, orientation, and scale. A new model for semantic object segmentation tasks is proposed, utilizing both the spatio-temporal information and spatial information from spatial and temporal domain observations. Previous works on semantic object segmentation either use either spatial and temporal data but the spatio-temporal information is typically only used for training. Hence, we develop an object segmentation framework that utilizes both spatial and temporal data for semantic object segmentation tasks. Based on the spatio-temporal information, we propose using spatio-temporal information for semantic object segmentation tasks. We demonstrate how the proposed model can be used by the visual-semantic segmentation community, in a setting where semantic segmentation tasks are mainly visual tasks. Extensive experimental results on both synthetic and real datasets demonstrate the effectiveness of the proposed method, and the robustness of our method to changes in appearance, orientation, and scale.