Boosting for Deep Supervised Learning – This article describes a new method to train deep learning neural network by applying the LMA method to a very powerful model trained in an unsupervised setting. It is shown that a good LMA method has the advantage of being able to find more predictive features, and thus the need to apply to this model more accurately and efficiently. Our method uses the deep LMA method to generate the posterior and training data and performs an extensive test on the dataset and its predictions. The method performs fine-tuning, and the results are compared with some other state-of-the-art methods.
We present a general framework for designing distributed adversarial architectures to extract useful predictive information from data. We first show that this strategy can reduce the cost of learning and analysis in learning problems, and that learning this algorithm is highly beneficial for training a network. The architecture is shown to be robust to adversarial loss, and compared to state-of-the-art loss functions for deep learning, this improves the robustness of a model to adversarial loss. The adversarial loss is shown to be robust to random errors, and the method is demonstrated to outperform state-of-the-art gradient methods on a wide range of data.
Exploiting the Sparsity of Deep Neural Networks for Predictive-Advection Mining
Online Model Interpretability in Machine Learning Applications
Boosting for Deep Supervised Learning
Practical algorithms, networks and neural nets
On the Complexity of Linear Regression and Bayesian Network Machine LearningWe present a general framework for designing distributed adversarial architectures to extract useful predictive information from data. We first show that this strategy can reduce the cost of learning and analysis in learning problems, and that learning this algorithm is highly beneficial for training a network. The architecture is shown to be robust to adversarial loss, and compared to state-of-the-art loss functions for deep learning, this improves the robustness of a model to adversarial loss. The adversarial loss is shown to be robust to random errors, and the method is demonstrated to outperform state-of-the-art gradient methods on a wide range of data.