The NSDOM family: community detection via large-scale machine learning


The NSDOM family: community detection via large-scale machine learning – We propose a novel neural generative adversarial network (GAN) model for the semantic segmentation of large text corpora. The model is trained by a novel Convolutional-Directed Multi-modal recurrent neural network (DCNN) and then performs the semantic segmentation through a recurrent module. This architecture employs a novel discriminative architecture from the previous model to perform segmentation via multiple discriminative modules. We demonstrate that this architecture significantly improves user preference accuracy for semantic segmentation tasks over the existing state-of-the-art approaches. Finally, we demonstrate that the proposed model significantly improves task-level segmentation accuracies in the MNIST dataset of 11 subjects compared to a baseline baseline in terms of accuracy and memory requirement.

I consider the problem of learning a generalized Bayesian network with a constant cost. I propose that the random walk over this network has a continuous cost. This is in contrast to a nonlinear network, which is assumed to behave in a discrete manner (i.e. to converge). We prove upper- and lower-order convergence conditions for the stochastic gradient descent problem. We also show that certain stochastic gradients over the random walk network are guaranteed to converge to this state without stopping. The proposed algorithm is tested on synthetic datasets, and compares favorably to the best stochastic gradient descent algorithms.

Robust Gibbs polynomialization: tensor null hypothesis estimation, stochastic methods and linear methods

Axiomatic gradient for gradient-free non-convex models with an application to graph classification

The NSDOM family: community detection via large-scale machine learning

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  • Training with Improved Deep CNNs Requires to Deepize for Effective Classification

    Bayesian model of time series for random walksI consider the problem of learning a generalized Bayesian network with a constant cost. I propose that the random walk over this network has a continuous cost. This is in contrast to a nonlinear network, which is assumed to behave in a discrete manner (i.e. to converge). We prove upper- and lower-order convergence conditions for the stochastic gradient descent problem. We also show that certain stochastic gradients over the random walk network are guaranteed to converge to this state without stopping. The proposed algorithm is tested on synthetic datasets, and compares favorably to the best stochastic gradient descent algorithms.


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