FractalGradient: Learning the Gradient of Least Regularized Proximal Solutions


FractalGradient: Learning the Gradient of Least Regularized Proximal Solutions – In this paper, we propose a novel algorithm for stochastic matrix update (SPA) by optimizing a variational inference. The proposed method is based on the use of latent variable models (LVs), where LVs are fixed-valued latent variables that encode the regularity of the function over latent values. We define an optimization problem that updates LVs with a priori inference that is optimal in terms of a latent space model in which LVs represent the regularity of the function. We investigate a number of variants of this problem, including a multi-shot update-based update, a single-shot update based on variational inference and a sequential-based update, and show that all variants are applicable. Experiments show that the proposed method outperforms the standard SPA algorithm.

We present a Bayesian convolutional neural network (CNN) for video surveillance, where CNNs learn to match scene-specific features, but not human-generated ones. Specifically, we study the task of automatically predicting the spatial locations of human hands using a pairwise linear dictionary of hand textures with human-generated attributes. Using a human motion model trained on a hand-labeled dataset from a human-generated video dataset, a novel approach for hand-labeled action recognition is proposed. We compare the performance of hand-labeled action and human-generated visual features by comparing them across the different hand-labeled action categories. We show that the performance is better when the hand-labeled action is human-generated (i.e., when the human-generated textures are not hand-luminous in scale). For this, the proposed method outperforms a supervised learning-based method by a significant margin, while also finding consistent improvements in a human-generated motion model trained on human-labeled action data. We also present some preliminary experimental results on the task of manually annotated hand-labeled images.

Multi-objective Sparse Principal Component Analysis with Regression Variables

Convolutional Spatial Transformer Networks (CST)

FractalGradient: Learning the Gradient of Least Regularized Proximal Solutions

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  • MorphMan: A System for Morph Recognition

    On-the-fly LSTM for Action Recognition on Video via a Spatio-Temporal AlgorithmWe present a Bayesian convolutional neural network (CNN) for video surveillance, where CNNs learn to match scene-specific features, but not human-generated ones. Specifically, we study the task of automatically predicting the spatial locations of human hands using a pairwise linear dictionary of hand textures with human-generated attributes. Using a human motion model trained on a hand-labeled dataset from a human-generated video dataset, a novel approach for hand-labeled action recognition is proposed. We compare the performance of hand-labeled action and human-generated visual features by comparing them across the different hand-labeled action categories. We show that the performance is better when the hand-labeled action is human-generated (i.e., when the human-generated textures are not hand-luminous in scale). For this, the proposed method outperforms a supervised learning-based method by a significant margin, while also finding consistent improvements in a human-generated motion model trained on human-labeled action data. We also present some preliminary experimental results on the task of manually annotated hand-labeled images.


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