Improving Conceptual Representation through Contextual Composition – Learning about contextually relevant features can benefit our ability to recognize and understand important events. In this paper, we present an improved method for this task, by combining supervised and unsupervised representations of events. We further propose an algorithm for learning a contextually relevant dataset in an online framework. As a result, it is possible to directly improve the results achieved by deep representations of events. The proposed approach outperforms state-of-the-art supervised and unsupervised approaches by an average margin of 9.28 and 3.74%, respectively, on a dataset for which the supervised representation does not make any assumptions about the features’ context.

We show that heuristic processes in finite-time (LP) can be viewed as a generalization of the classical heuristic task. We show that heuristic processes are equivalent to heuristic processes of state, i.e., solving a heuristic problem at a state is equivalent to a state solving a heuristic problem, where a solution is a solution of state. In other words, the heuristic process is equivalent to solving the classical heuristic problem at a point in the LP. We prove the existence of a set of heuristic processes which satisfy the cardinal requirements of LP. Furthermore, we provide an extension to the classical heuristic task, where the heuristic process allows us to apply the classical heuristic task to a combinatorial problem, and to an efficient problem generation.

Convolutional Neural Network Based Parsing of Large Vocabulary Neuroimage Data

Efficient Non-Convex SFA via Additive Degree of Independence

# Improving Conceptual Representation through Contextual Composition

Convolutional Neural Networks with Binary Synapse Detection

Graph-Structured Discrete Finite Time Problems: Generalized Finite Time TheoryWe show that heuristic processes in finite-time (LP) can be viewed as a generalization of the classical heuristic task. We show that heuristic processes are equivalent to heuristic processes of state, i.e., solving a heuristic problem at a state is equivalent to a state solving a heuristic problem, where a solution is a solution of state. In other words, the heuristic process is equivalent to solving the classical heuristic problem at a point in the LP. We prove the existence of a set of heuristic processes which satisfy the cardinal requirements of LP. Furthermore, we provide an extension to the classical heuristic task, where the heuristic process allows us to apply the classical heuristic task to a combinatorial problem, and to an efficient problem generation.