Recurrent Neural Networks (RNNs) and their variants (suchas LSTMs and GRUs) have been remarkably successful atmachine-learning tasks on diverse kinds of sequential data(e.g. text, time-series, etc.). However, training of RNNs con-tinue to be a challenge due to difficulties stemming from regu-larization and the highly non-convex optimizations involved.In this paper, we propose to regularize training of RNNs byencouraging higher decorrelation in the hidden representa-tions. The cost function is devised to minimize non-diagonalelements of the correlation matrix computed over the hid-den representations of RNNs, along with the usual trainingaccuracy term; thereby penalizing redundancy in the learnedmodel. Furthermore, we propose to utilize the idea of decor-relating representations in learning an ensemble of RNNs,in order to maximize diversity in the resulting models; thusenforcing every individual network of the ensemble to gainabilities that are complementary to the ensemble. Extensiveexperiments are presented on various datasets with differentarchitectures of RNNs. Results are offered for multiple tasksand show that the proposed methods yield a significant im-provement; when compared with the state-of-the-art methods.

Collaborative filtering (CF) with implicit feedback is a successful method for recommending items to users, which does not require a knowledge of the items or users. CF methods can be mainly classified into two categories. One is point-wise regression based and the other is pair-wise ranking based, where the latter one only tries to find out the items that users prefer while ignores the items that users dislike, and usually gives out a better recommended item list. The performance of CF-based methods degrades significantly when the feedback information is sparse. To address the problem, many kinds of auxiliary information have been utilized such as users’ reviews on items, items’ content and description information, price, brands. In this paper we utilize a weighted recursive autoencoder (RAE) to extract useful features from several heterogeneous auxiliary information and tightly couple the weighted RAE with a pair-wise ranking based CF method. Analysis of the hyperparameters illustrates that auxiliary information from different sources is indeed able to benefit our model. Empirical experiments on six real world datasets show that our method outperforms other state-of-the-art methods.

Hand pose estimation plays an important role in human-computer interaction and virtual reality. In this paper, we present a regression framework to estimate 3D hand pose using depth image. Different from the previous methods, we propose a new method that has three key aspects: first, performance of system can be improved by setting up the better initial images using feature extraction via Convolution Neural Network (CNN); secondly, the error of joint position is estimated by dividing the dataset into groups of gesture type; thirdly, accuracy can be improved by learning the residual intensity of depth image by updating the residual of 3D joint coordinates constantly. It is noticed that importance of categorizing hand poses by gesture in computing the joint positions has been underestimated. Experimental evaluation with a public dataset A*STAR shows that our method produces low error of hand pose estimation and has more potential for the future work of the hand pose estimation.

Successful natural language processing (NLP) annotation tasks utilize crowdsourced worker pools that are optimized for the quality, cost, and speed of task completion. Recent work has shown that it is difficult to compare worker pools across platforms because there are insufficient numbers of workers in the geographical locations speaking the languages that are required. This problem is only getting worse with limits on worker location on the dominant crowdsourced worker platform, Amazon’s Mechanical Turk. Preliminary work suggests a general method for measuring the cost in time, money, and worker quality across platforms. This paper suggests a strategy for comparing a series of annotation tasks that seek a more definite, empirical approach. This work in progress proposes running a series of parallel experiments across popular crowdsourcing platforms and using strategies from image processing tasks to compare task difficulty.

For a multitude of tasks that come naturally to humans, performance of AI systems is inferior to human level performance. We show how human intellect made available via crowdsourcing can be used to complement an existing system during execution. We introduce a hybrid workflow that queries people to verify and correct the output of the system and present a simulation-based workflow optimization method to balance the cost of human input with the expected improvement in performance. Through empirical evaluations on an image captioning system, we show that the hybrid system, which combines the AI system with human input, significantly outperforms the automated system by properly trading off the cost of human input with expected benefit. Finally, we show that human input collected at execution time can be used to teach the system about its errors and limitations.

Many prediction problems can be phrased as inferences over local neighborhoods of graphs. The graph represents the interaction between entities, and the neighborhood of each entity contains information that allows the inferences or predictions. We present an approach for applying machine learning directly to such graph neighborhoods, yielding predictions for graph nodes on the basis of the structure of their local neighborhood and the features of the nodes in it. Our approach allows predictions to be learned directly from examples, bypassing the step of creating and tuning an inference model or summarizing the neighborhoods via a fixed set of hand-crafted features. The approach is based on a multi-level architecture built from Long Short-Term Memory neural nets (LSTMs); the LSTMs learn how to summarize the neighborhood from data. We demonstrate the effectiveness of the proposed technique on a synthetic example and on real-world data related to crowdsourced grading, Bitcoin transactions, and Wikipedia edit reversions.

The Strong Stackelberg Equilibrium (SSE) has drawn extensive attention recently in several security domains, which optimizes the defender's random allocation of limited security resources. However, the SSE concept neglects the advantage of defender's strategic revelation of her private information, and overestimates the observation ability of the adversaries. In this paper, we overcome these restrictions and analyze the tradeoff between strategic secrecy and commitment in security games. We propose a Disguised-resource Security Game (DSG) where the defender strategically disguises some of her resources. We compare strategic information revelation with public commitment and formally show that they have different advantages depending the payoff structure. To compute the Perfect Bayesian Equilibrium (PBE), several novel approaches are provided, including basic MILP formulations with mixed defender strategy and compact representation, a novel algorithm based on support set enumeration, and an approximation algorithm for epsilon-PBE. Extensive experimental evaluation shows that both strategic secrecy and Stackelberg commitment are critical measures in security domain, and our approaches can solve PBE for realistic-sized problems with good enough and robust solution quality.

Unlike perfect-information games, imperfect-information games cannot be decomposed into subgames that are solved independently. Thus more computationally intensive equilibrium-finding techniques are used, and abstraction---in which a smaller version of the game is generated and solved---is essential. Endgame solving is the process of computing a (presumably) better strategy for just an endgame than what can be computationally afforded for the full game. Endgame solving has many benefits, such as being able to 1) solve the endgame in a finer information abstraction than what is computationally feasible for the full game, and 2) incorporate into the endgame actions that an opponent took that were not included in the action abstraction used to solve the full game. We introduce an endgame solving technique that outperforms prior methods both in theory and practice. We also show how to adapt it, and past endgame-solving techniques, to respond to opponent actions that are outside the original action abstraction; this significantly outperforms the state-of-the-art approach, action translation. Finally, we show that endgame solving can be repeated as the game progresses down the tree, leading to significantly lower exploitability. All of the techniques are evaluated in terms of exploitability; to our knowledge, this is the first time that exploitability of endgame-solving techniques has been measured in large imperfect-information games.

Uncertainty analysis in the form of probabilistic forecasting can provide significant improvements in decision making processes in the smart power gird for better integrating renewable energies such as wind. Whereas point forecasting provides a single expected value, probabilistic forecasts provide more information in the form of quantiles, prediction intervals, or full predictive densities. This paper analyzes the effectiveness of an approach for nonparametric probabilistic forecasting of wind power that combines support vector machines and nonlinear quantile regression with non-crossing constraints. A numerical case study is conducted using publicly available wind data from the Global Energy Forecasting Competition 2014. Multiple quantiles are estimated to form 20%, 40%, 60% and 80% prediction intervals which are evaluated using the pinball loss function and reliability measures. Three benchmark models are used for comparison where results demonstrate the proposed approach leads to significantly better performance while preventing the problem of overlapping quantile estimates.

Analysis of an organization's computer network activity is a key component of early detection and mitigation of insider threat, a growing concern for many organizations. Raw system logs are a prototypical example of streaming data that can quickly scale beyond the cognitive power of a human analyst. As a prospective filter for the human analyst, we present an online unsupervised deep learning approach to detect anomalous network activity from system logs in real time. Our models decompose anomaly scores into the contributions of individual user behavior features for increased interpretability to aid analysts reviewing potential cases of insider threat. Using the CERT Insider Threat Dataset v6.2 and threat detection recall as our performance metric, our novel deep and recurrent neural network models outperform Principal Component Analysis, Support Vector Machine and Isolation Forest based anomaly detection baselines. For our best model, the events labeled as insider threat activity in our dataset had an average anomaly score in the 95.53 percentile, demonstrating our approach's potential to greatly reduce analyst workloads.