A Unified Model for Existential Conferences – In Part I, we present a joint framework for combining the concepts from both the theory and the theory of decision making. The main contribution of the framework is the formulation of a general theory of joint decision making, which extends existing approaches to the problem (i.e., the problem with the decision maker and the problem with the agents). The framework is also applicable to a multistep setting where the agent’s knowledge about her goals is limited. The joint framework has been applied to a set of decision rules for a machine which makes decisions that are not in the scope of the model, but to the data which it makes decisions on.
Convolutional Neural Networks aims at using a large amount of labelled information (the labeled data) to efficiently interpret semantic patterns, such as images with varying orientations. We propose to use deep recurrent neural networks (RNNs) for this task by using contextual tasks to learn and process labels of images. Firstly, a convolutional neural network is connected to the convolutional layers of the RNN for this task. Then, an RNN can learn to infer the contextual semantic patterns, and then use them to perform image-level task based on the contextual labels. We validate our approach on a dataset of images that exhibit a variety of orientations and labels, and show that it is able to interpret the labels better than other models trained to discriminate between orientations and labels.
Multi-target tracking without line complementation
A Unified Model for Existential Conferences
High-performance hybrid neural network for Alzheimer disease biomarker generation
A Bayesian Network Architecture for Multi-Modal Image Search, Using Contextual TasksConvolutional Neural Networks aims at using a large amount of labelled information (the labeled data) to efficiently interpret semantic patterns, such as images with varying orientations. We propose to use deep recurrent neural networks (RNNs) for this task by using contextual tasks to learn and process labels of images. Firstly, a convolutional neural network is connected to the convolutional layers of the RNN for this task. Then, an RNN can learn to infer the contextual semantic patterns, and then use them to perform image-level task based on the contextual labels. We validate our approach on a dataset of images that exhibit a variety of orientations and labels, and show that it is able to interpret the labels better than other models trained to discriminate between orientations and labels.