TASK DEFINITION: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
PROBLEM: Reactive (memoryless) policies are sufficient in completely observable Markov decision pro cesses (MDPs), but some kind of memory is usually necessary for optimal control of a par tially observable MDP. Policies with finite mem ory can be represented as finite-state automata. In this paper, we extend Baird and Moore's YAPS algorithm to the problem of learning gen eral finite-state automata. Because it performs stochastic gradient descent, this algorithm can be shown to converge to a locally optimal finite state controller. We provide the details of the algorithm and then consider the question of un der what conditions stochastic gradient descent will outperform exact gradient descent. We con clude with empirical results comparing the per formance of stochastic and exact gradient de scent, and showing the ability of our algorithm to extract the useful information contained in the sequence of past observations to compensate for the lack of observability at each time-step.

SOLUTION: Learning Finite-State Controllers for Partially Observable Environments

PROBLEM: Object recognition and localization are important tasks in computer vision. The focus of this work is the incorporation of contextual information in order to improve object recognition and localization. For instance, it is natural to expect not to see an elephant to appear in the middle of an ocean. We consider a simple approach to encapsulate such common sense knowledge using co-occurrence statistics from web documents. By merely counting the number of times nouns (such as elephants, sharks, oceans, etc.) co-occur in web documents, we obtain a good estimate of expected co-occurrences in visual data. We then cast the problem of combining textual co-occurrence statistics with the predictions of image-based classifiers as an optimization problem. The resulting optimization problem serves as a surrogate for our inference procedure. Albeit the simplicity of the resulting optimization problem, it is effective in improving both recognition and localization accuracy. Concretely, we observe significant improvements in recognition and localization rates for both ImageNet Detection 2012 and Sun 2012 datasets.

SOLUTION: Using Web Co-occurrence Statistics for Improving Image Categorization

PROBLEM: Truth discovery is to resolve conflicts and find the truth from multiple-source statements. Conventional methods mostly research based on the mutual effect between the reliability of sources and the credibility of statements, however, pay no attention to the mutual effect among the credibility of statements about the same object. We propose memory network based models to incorporate these two ideas to do the truth discovery. We use feedforward memory network and feedback memory network to learn the representation of the credibility of statements which are about the same object. Specially, we adopt memory mechanism to learn source reliability and use it through truth prediction. During learning models, we use multiple types of data (categorical data and continuous data) by assigning different weights automatically in the loss function based on their own effect on truth discovery prediction. The experiment results show that the memory network based models much outperform the state-of-the-art method and other baseline methods.

SOLUTION:
Truth Discovery with Memory Network