Sparse Nonparametric MAP Inference


Sparse Nonparametric MAP Inference – In this work, we present a sparse nonparametric MAP inference algorithm to improve the precision of model predictions. In our method, the objective is to estimate the optimal distribution given the model parameters in terms of a non-convex function with an appropriate dimension. For each parameter, we propose an algorithm that performs the sparse mapping and then approximates the likelihood to a vector given the model parameters according to the likelihood. We show that the algorithm converges to the optimal distribution when the model parameters correspond to the most likely distribution and vice versa. We also provide an additional step of inference which may be used to compute the correct distributions. The algorithm is compared to other MAP inference algorithms on a synthetic data set.

We present a new method for unsupervised interactive learning in 3D scenes. We first build an interactive visualization tool to explore and evaluate the performance of different supervised learning methods and their applications (e.g., object tracking, pose estimation). We then use this tool to generate a dataset of 3D scenes from annotated, publicly available 3D datasets for 3D video. We evaluate our method on two benchmark datasets (the KOMED 2014 dataset and the UAV dataset). Our methods outperform state-of-the-art methods, while being comparable to the state-of-the-art methods in terms of both the computational time and computational complexity.

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Sparse Nonparametric MAP Inference

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  • Learning Compact Feature Spaces with Convolutional Autoregressive Priors

    An Interactive Graph Segmentation Framework for 3D ScenesWe present a new method for unsupervised interactive learning in 3D scenes. We first build an interactive visualization tool to explore and evaluate the performance of different supervised learning methods and their applications (e.g., object tracking, pose estimation). We then use this tool to generate a dataset of 3D scenes from annotated, publicly available 3D datasets for 3D video. We evaluate our method on two benchmark datasets (the KOMED 2014 dataset and the UAV dataset). Our methods outperform state-of-the-art methods, while being comparable to the state-of-the-art methods in terms of both the computational time and computational complexity.


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