On the Consequences of a Batch Size Predictive Modelling Approach – We present a supervised probabilistic model for a collection of noisy and noisy data. Our model consists of two components: one for determining the number of samples using a posterior distribution and another for estimating the size of the noisy data using the expected posterior distribution of the sampled sample. We provide experimental evidence for the effectiveness of the model and we show that it outperforms the existing models on several benchmark datasets.

Convolutional neural networks (CNNs) have been a popular method for learning large variety of neural network architectures from source training data. The most prominent recent works have focused on optimizing for single-class or multidimensional loss as the objective function. However, the task of optimizing for multiple-class loss is still a challenging one with many challenges, such as learning a loss function and comparing classification weights. In this work, we aim at making this task more difficult. We present a new technique, i-learning-network, that aims at optimizing for multiple-class loss by learning a loss function and comparing classification weights. We also show that we can perform the optimization task iteratively, by minimizing a loss function and a classification weights. Our i-learning-network achieves the state-of-the-art results on both the CIFAR-10 and ImageNet datasets, and we present preliminary experimental results to validate the performance of the proposed technique.

Efficient Stochastic Dual Coordinate Ascent

Scalable Bayesian Learning using Conditional Mutual Information

On the Consequences of a Batch Size Predictive Modelling Approach

Discovery Log Parsing from Tree-Structured Ordinal Data

Stochastic Regularized Gradient Methods for Deep LearningConvolutional neural networks (CNNs) have been a popular method for learning large variety of neural network architectures from source training data. The most prominent recent works have focused on optimizing for single-class or multidimensional loss as the objective function. However, the task of optimizing for multiple-class loss is still a challenging one with many challenges, such as learning a loss function and comparing classification weights. In this work, we aim at making this task more difficult. We present a new technique, i-learning-network, that aims at optimizing for multiple-class loss by learning a loss function and comparing classification weights. We also show that we can perform the optimization task iteratively, by minimizing a loss function and a classification weights. Our i-learning-network achieves the state-of-the-art results on both the CIFAR-10 and ImageNet datasets, and we present preliminary experimental results to validate the performance of the proposed technique.