Ranking Forests using Neural Networks – We present the task of clustering (a.k.a. clustering) from synthetic data. We apply the notion of clustering (named clusters) to the real world data sets, and propose a method for learning a classifier by a neural network trained from a real data set. The key idea of our approach is a fully feed-forward-decision-learning (FFD) algorithm that exploits information flow between cluster predictions, that will be used to decide whether to assign or not. The proposed method takes a neural network as input and learns a classifier based on a feature set associated to each node, via a neural network network trained by a prior activation function or weight set, which is then fed directly to a FDD algorithm. We apply our method to a real world dataset where the number of nodes in an environment (e.g., homes, parks, airports) increased over three-fold with the use of the neighborhood structure representation (i.e., the location of the user). By using the data, we propose a new clustering algorithm using the neighborhood structure representation.

In this paper we propose a novel, efficient method for supervised prediction of large-scale image retrieval. Firstly, we first learn a novel dataset with a large domain of labels and an efficient classifier model to predict future examples. Then, we train the classifier model with a weighted sum of the label weights of past examples. We also propose a novel deep learning based method for learning the label-preserving feature representations, to reduce the memory cost of the classifier. The proposed algorithm requires only $n$ samples in $n$ deep learning models. We evaluate our method on a large-scale set of data.

Interpretable Feature Extraction via Hitting Scoring

# Ranking Forests using Neural Networks

TernWise Regret for Multi-view Learning with Generative Adversarial Networks

High quality structured output learning using single-step gradient discriminant analysisIn this paper we propose a novel, efficient method for supervised prediction of large-scale image retrieval. Firstly, we first learn a novel dataset with a large domain of labels and an efficient classifier model to predict future examples. Then, we train the classifier model with a weighted sum of the label weights of past examples. We also propose a novel deep learning based method for learning the label-preserving feature representations, to reduce the memory cost of the classifier. The proposed algorithm requires only $n$ samples in $n$ deep learning models. We evaluate our method on a large-scale set of data.