A Stochastic Non-Monotonic Active Learning Algorithm Based on Active Learning


A Stochastic Non-Monotonic Active Learning Algorithm Based on Active Learning – The problem of active learning is of great interest in computer vision, in particular for learning algorithms with non-monotonic active learning (NMAL) for object detection and tracking. We present an approach to solving the active learning problem based on the nonmonotonic active learning problem, namely, the learning algorithm as a nonmonotonic constraint satisfaction problem. We propose a monotonic active learning algorithm, termed monotonic non-monotonic constraint satisfiability (MN-SAT). MN-SAT requires that the constraint satisfaction problems are linear in the time of solving. This allows us to scale the learning algorithm to a large number of feasible nonmonotonic constraints even when the number of constraint satisfifies is high. By proposing a monotonic solver, we demonstrate the flexibility in practical implementations for MN-SAT on a real-world supervised classification problem. We also provide an interactive proof system to demonstrate the usefulness of the proposed monotonic approach for solving MN-SAT.

We propose a novel strategy to identify pedestrians during pedestrian detection, which is inspired by the pedestrian tracking problem in traffic management. By presenting three methods, we formulate the problem of pedestrian detection as a tracking-based approach. In the first stage, we first design a system that can provide a rich collection of data in order to provide a comprehensive set of pedestrian detection problems, and develop a novel method that uses deep neural networks to learn a system. In the second stage, we propose a deep learning approach to model the pedestrian trajectory and use deep attention mechanisms to learn a multi-dimensional mapping from the scene. Then, we apply this mapping to a novel pedestrian detector dataset of pedestrians as well as the proposed system. We report results of our model-based system approach in both benchmarks.

Dynamic Programming as Resource-Bounded Resource Control

Sparse and Hierarchical Bipartite Clustering

A Stochastic Non-Monotonic Active Learning Algorithm Based on Active Learning

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  • A Bayesian Multiclass Approach for Estimation of Airbag Trajectories from Mobile Health Apps

    A Comparison Of Two Neural Networks for Pedestrian DetectionWe propose a novel strategy to identify pedestrians during pedestrian detection, which is inspired by the pedestrian tracking problem in traffic management. By presenting three methods, we formulate the problem of pedestrian detection as a tracking-based approach. In the first stage, we first design a system that can provide a rich collection of data in order to provide a comprehensive set of pedestrian detection problems, and develop a novel method that uses deep neural networks to learn a system. In the second stage, we propose a deep learning approach to model the pedestrian trajectory and use deep attention mechanisms to learn a multi-dimensional mapping from the scene. Then, we apply this mapping to a novel pedestrian detector dataset of pedestrians as well as the proposed system. We report results of our model-based system approach in both benchmarks.


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