Machine learning and networked sensing – In this work we consider the interaction between artificial intelligence and the environment, which is a fundamental step towards a new field of human-computer symbiosis. We formulate the problem of intelligent decision making as an environment-based decision problem, and discuss a framework for designing the answer to intelligent and environment-based decision making and applications. The answer is a question: do the actions that we execute when doing well (learning new strategies, evaluating the utility of existing strategies, or evaluating the outcome of existing strategies) affect the way in which that policy will be deployed? This provides us with an example where, as a consequence of a decision that we made, an agent chooses what to do in response to a task. Our theoretical framework allows us to explain the relationship between intelligent decision making and the environment, and the way that the agent learns to execute knowledge about the decision making process over the environment.
This paper presents a new approach to deep learning for emotion recognition in the context of emotion classification, where we use deep neural networks to learn how people react. These networks learn to process natural language, not human language. This leads to the use of deep neural networks to detect emotion as a continuous feature representation of a human’s internal state. This paper presents a supervised learning system which produces an emotion graph to classify people based on their emotional state. We trained an emotion graph to classify people and then presented this graph through a set of reinforcement learning tasks for a task-dependent evaluation. Our experiments show that the supervised learning method performs better than the previous methods. We show that on the one hand, supervised learning can achieve good performance on emotion recognition tasks. On the other hand, classification in the presence of external stimuli cannot be used as an additional feature representation. Therefore, our approach also can be a complementary tool for emotion recognition tasks. Our approaches are evaluated against several challenging benchmark datasets: COCO, CelebA and the W3C human emotion classification dataset.
Efficient Learning-Invariant Signals and Sparse Approximation Algorithms
Machine learning and networked sensing
Recurrent Topic Models for Sequential Segmentation
Show, challenge and adapt – the importance of context in natural language processingThis paper presents a new approach to deep learning for emotion recognition in the context of emotion classification, where we use deep neural networks to learn how people react. These networks learn to process natural language, not human language. This leads to the use of deep neural networks to detect emotion as a continuous feature representation of a human’s internal state. This paper presents a supervised learning system which produces an emotion graph to classify people based on their emotional state. We trained an emotion graph to classify people and then presented this graph through a set of reinforcement learning tasks for a task-dependent evaluation. Our experiments show that the supervised learning method performs better than the previous methods. We show that on the one hand, supervised learning can achieve good performance on emotion recognition tasks. On the other hand, classification in the presence of external stimuli cannot be used as an additional feature representation. Therefore, our approach also can be a complementary tool for emotion recognition tasks. Our approaches are evaluated against several challenging benchmark datasets: COCO, CelebA and the W3C human emotion classification dataset.