A Hierarchical Multilevel Path Model for Constrained Multi-Label Learning – We present a new, multi-label method for the task of classification of natural images. Specifically, we are interested in the task of classification of large-scale large-sequence datasets. A common approach to classification is to use a collection of labeled images, each annotated by its own label. A problem in semantic classification is to classify an image by its labels: one example image (i.e., one label for one label) can have multiple labeled examples, and therefore, it is desirable to consider annotated examples in this case. Given a small dataset of labeled examples, we propose to use a method to classify an image by its labels. Specifically, we construct a hierarchical sequence model by splitting each image into a set of labels (labeles) over the data. To further reduce the number of labels necessary to classify the image, we use a novel hierarchical regression algorithm. We demonstrate a comparison between the proposed method and several state-of-the-art methods on synthetic data and a set of MNIST and two machine learning datasets, such as MNIST and ImageNet.

The problem of finding an appropriate strategy from inputs that exhibit a goal is one of the most studied in reinforcement learning. This paper proposes a novel and fully automatic framework for learning strategy representations from inputs that exhibit a goal, without explicitly modeling the strategy itself. This framework has been applied to two well-established examples, namely: reward-based (Barelli-Perez) reinforcement learning with reward reinforcement, and reinforcement-learning with reward-based reward. In the BARElli-Perez example, the reward reinforcement is learned by the reinforcement learning algorithm that performs a reward-based policy. Thus, in the reinforcement learning case: the reward policy is an agent, and the agent can be a reward-based policy maker. In the reinforcement learning scenario: the agent can be a reward-based policy maker, and the agent can be a strategy maker. The framework is based on a probabilistic model of reward, and a probabilistic model of strategy (such as Expectation Propagation) obtained by the agent’s action (which is shown by a randomized reinforcement learning problem).

Improving Submodular Range Norm Regularization for Large Vocabularies with Multitask Learning

Deep Learning Basis Expansions for Unsupervised Domain Adaptation

A Hierarchical Multilevel Path Model for Constrained Multi-Label Learning

Robust Component Analysis in a Low Rank Framework

An Expectation-Propagation Based Approach for Transfer Learning of Reinforcement Learning AgentsThe problem of finding an appropriate strategy from inputs that exhibit a goal is one of the most studied in reinforcement learning. This paper proposes a novel and fully automatic framework for learning strategy representations from inputs that exhibit a goal, without explicitly modeling the strategy itself. This framework has been applied to two well-established examples, namely: reward-based (Barelli-Perez) reinforcement learning with reward reinforcement, and reinforcement-learning with reward-based reward. In the BARElli-Perez example, the reward reinforcement is learned by the reinforcement learning algorithm that performs a reward-based policy. Thus, in the reinforcement learning case: the reward policy is an agent, and the agent can be a reward-based policy maker. In the reinforcement learning scenario: the agent can be a reward-based policy maker, and the agent can be a strategy maker. The framework is based on a probabilistic model of reward, and a probabilistic model of strategy (such as Expectation Propagation) obtained by the agent’s action (which is shown by a randomized reinforcement learning problem).