Analysis, pattern discovery, and decision support all can benefit greatly from informative and interpretable visualizations, especially of high-dimensional data. Informative Projection Ensemble (IPE) methodology has proven effective in finding ...

There exist solutions for tracking of objects in 3D space involving hi-tech cameras and powerful computer systems capable of tracking many objects in large dynamic space simultaneously in real time. On the other hand, there are situations where such ...

General-purpose speech engines are trained on large corpus. However, studies and experiments have shown that when such engines are used to recognize spoken sentences in specific domains they may not produce accurate ASR output. Further, the accent and ...

We establish connections from optimizing Bellman Residual and Temporal Difference Loss to worst-case long-term predictive error. In the online learning framework, learning takes place over a sequence of trials with the goal of predicting a future ...

In this paper, we describe proximal gradient temporal difference learning, which provides a principled way for designing and analyzing true stochastic gradient temporal difference learning algorithms. We show how gradient TD (GTD) reinforcement learning ...

We present a novel approach to enrich classification trees with the representation learning ability of deep (neural) networks within an end-to-end trainable architecture. We combine these two worlds via a stochastic and differentiable decision tree ...

We analyze the observability of 3-D position and orientation from the fusion of visual and inertial sensors. The model contains unknown parameters, such as sensor biases, and so the problem is usually cast as a mixed filtering/identification problem, ...

We consider the problem of robots following natural language commands through previously unknown outdoor environments. A robot receives commands in natural language, such as "Navigate around the building to the car left of the fire hydrant and near the ...

We study online boosting, the task of converting any weak online learner into a strong online learner. Based on a novel and natural definition of weak online learnability, we develop two online boosting algorithms. The first algorithm is an online ...

Gelfond and Zhang recently proposed a new stable model semantics based on Vicious Circle Principle in order to improve the interpretation of logic programs with aggregates. A detailed complexity analysis of coherence testing and cautious reasoning under ...

In answer set programming, knowledge involving sets of objects collectively is naturally represented by aggregates, which are rewritten into simpler forms known as monotone aggregates by current implementations. However, there is a complexity gap ...

In recent years AI research has had an increasing role in models and algorithms for security problems. Game theoretic models of security, and Stackelberg security games in particular, have received special attention, in part because these models and ...

Formalizing and automating aspects of human plausible reasoning is an important challenge for the field of artificial intelligence. Practical advances, however, are hampered by the fact that most forms of plausible reasoning rely on background knowledge ...

Open Information Extraction (Open IE) extracts textual tuples comprising relation phrases and argument phrases from within a sentence, without requiring a pre-specified relation vocabulary. In this paper we first describe a decade of our progress on ...

Because of playing one of the most important roles in the artificial intelligent systems like robots, visual understanding has gained vast interests in the past few decades. Most of the existing approaches need human labelled training data to train the ...

I am developing a framework for anomaly reasoning for agents that plan and learn in complex, sometimes unfamiliar domains. Anomaly reasoning encompasses recognizing, interpreting, and reacting to unfamiliar objects or familiar objects appearing in ...

Our research focuses on the problem of automatically acquiring structured narrative information from natural language. We have focused on character extraction and narrative role identification from a corpus of Slavic folktales. To address natural ...

Short-texts accentuate the challenges posed by the high feature space dimensionality of text learning tasks. The linked nature of social data causes new dimensions to be added to the feature space, which, also becomes sparser. Thus, efficient and ...