The Role of Intensive Regression in Learning to Play StarCraft – In this paper we present a novel framework for predicting the importance of an actor’s performance in StarCraft games using a sequence of simple examples. This framework applies probabilistically, learning to a player’s state in a game, and to a character’s actions in the game via the model of the actor’s performance on a sequence of simple examples. We show that this framework outperforms the state-of-the-art predictions and we explore the idea to use probabilistic models through different learning methods. We show that learning to perform at the level of a human actor results in significant improvements over classical probabilistic models that do not learn to play at this level of a human actor.

This paper presents an algorithm for object classification based upon the joint representation learning process. The joint representation learning process is used in the decision making for object classification using an agent’s behavior, which is made possible via the input of a graph node. By applying a neural network based classification strategy, the network is better able to learn the classifier parameters in the task at hand.

High quality structured output learning using single-step gradient discriminant analysis

A study to determine the maximum number of participants in the screening process for the Multi-Person Registration Platform

The Role of Intensive Regression in Learning to Play StarCraft

Robust Learning of Spatial Context-Dependent Kernels

Graph Classification: A Deep Neural Network ApproachThis paper presents an algorithm for object classification based upon the joint representation learning process. The joint representation learning process is used in the decision making for object classification using an agent’s behavior, which is made possible via the input of a graph node. By applying a neural network based classification strategy, the network is better able to learn the classifier parameters in the task at hand.