Dictionary Learning, Super-Resolution and Texture Matching with Hashing Algorithm – With the proliferation of digital art, there have been numerous applications of unsupervised sparse learning to automatically estimate an object from a sparse representation using a deep convolutional network. We propose an unsupervised sparse estimation framework based on an iterative process of minimizing and discretizing the input data. Our algorithm achieves a fast reconstruction using linear convergence rates, a lower memory footprint, and higher accuracy than many state-of-the-art unsupervised sparse detection algorithms. We also show that the residuals of the object can be extracted by the encoder as a regularity function which is very useful for unsupervised learning. We then extend that sparse reconstruction procedure to an unsupervised setting where the reconstruction can take place offline. Further, we show that sparse reconstruction can lead to better performance in image classification, i.e. object detection and classification.
The ability to predict an event using simple model parameters in real time is an important task for AI systems. One approach is to train and optimise the model to anticipate a particular event. However, previous methods tend to perform worst case predictions with extremely high confidence, which is very rare for any practical purpose. In this paper, we propose a new technique that can be applied to predict future events. On a real world network, we train a prediction model to predict future actions, and then use that prediction to predict future actions. To improve accuracy, we also present a novel method that learns an event model by learning from inputs of different types, such as time, environment and environment changes. Our approach is based on several assumptions and a new constraint on how events can be predicted. To demonstrate the ability of the prediction model to predict future actions, we use this dataset of time series for future action updates.
The Power of Linear-Graphs: Learning Conditionally in Stochastic Graphical Models
A Neural Network Model for Spatio-Temporal Perception and Awareness from Unstructured Data
Dictionary Learning, Super-Resolution and Texture Matching with Hashing Algorithm
A Simple End-to-end Deep Reinforcement Learning Method for Situation Calculus
Modelling Economic Conditions: An Event CalculusThe ability to predict an event using simple model parameters in real time is an important task for AI systems. One approach is to train and optimise the model to anticipate a particular event. However, previous methods tend to perform worst case predictions with extremely high confidence, which is very rare for any practical purpose. In this paper, we propose a new technique that can be applied to predict future events. On a real world network, we train a prediction model to predict future actions, and then use that prediction to predict future actions. To improve accuracy, we also present a novel method that learns an event model by learning from inputs of different types, such as time, environment and environment changes. Our approach is based on several assumptions and a new constraint on how events can be predicted. To demonstrate the ability of the prediction model to predict future actions, we use this dataset of time series for future action updates.