Learning time, recurrence, and retention in recurrent neural networks – In many applications, the task of finding the next most frequent element in a sequence of atoms can be viewed as a natural optimization problem. We show that the task can be expressed in terms of a learning scheme that considers three types of atoms over time, i.e. with time and with atoms. Given one or even all atoms, the learning objective is to learn to learn to find the next atoms from the previous ones. Although the goal of the learning is to minimize the computational cost to compute the next state, the goal of the learning scheme is to estimate the probability of finding the next atoms in the entire set of atoms. We show that this optimization problem under generalization to time-dependent graphs and atom-specific constraints, where the graph is a continuous polytope and the atom is the atom, is computationally tractable in stochastic and scalable models. The algorithm is shown to be efficient in solving the optimization problem for real-world data.
This paper proposes a new method to classify a set of images into two groups, called pairwise multi-label. The proposed learning model, named Label-Label Multi-Label Learning (LML), encodes the visual features of each image into a set of labels and the labels, respectively. The main objective is to learn which labels are similar to the data. To this end, the LML model can be designed by taking the labels as inputs, and is trained by computing the joint ranking. Since labels have importance for the classification, we design a pairwise multi-label learning method. We develop a set of two LMLs, i.e., two multi-label datasets for ImageNet, VGGNet, and ImageNet, with a combination of deep CNN and deep latent space models. The learned networks are connected in the two networks by a dual manifold, and are jointly optimized by a neural network. Through simulation experiments, we demonstrate that the network’s performance can be considerably improved compared to the prior state-of-the-art approaches and outperforms that of those using supervised learning.
Efficient Representation Learning for Classification
Stochastic Variational Inference for Gaussian Process Models with Sparse Labelings
Learning time, recurrence, and retention in recurrent neural networks
Sequential Adversarial Learning for Language Modeling
Structured Multi-Label Learning for Text ClassificationThis paper proposes a new method to classify a set of images into two groups, called pairwise multi-label. The proposed learning model, named Label-Label Multi-Label Learning (LML), encodes the visual features of each image into a set of labels and the labels, respectively. The main objective is to learn which labels are similar to the data. To this end, the LML model can be designed by taking the labels as inputs, and is trained by computing the joint ranking. Since labels have importance for the classification, we design a pairwise multi-label learning method. We develop a set of two LMLs, i.e., two multi-label datasets for ImageNet, VGGNet, and ImageNet, with a combination of deep CNN and deep latent space models. The learned networks are connected in the two networks by a dual manifold, and are jointly optimized by a neural network. Through simulation experiments, we demonstrate that the network’s performance can be considerably improved compared to the prior state-of-the-art approaches and outperforms that of those using supervised learning.
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