Learning to Detect Small Signs from Large Images – Automated localization systems are among the most important tools for recognizing image objects in video. Recent work has demonstrated that machine-generated images can be used to train a classifier of object detection methods. In this work, we are interested in learning to associate the features of a object to its position, which we also refer to as the camera position. We exploit a deep recurrent network for image training that learns this joint representation using the input features of the network for this purpose. Experiments on the MNIST dataset show that the proposed method outperforms the state of the art methods in several image detection tasks.

The problem of causal chain discovery (CCD) is an application of the deterministic duality of causality. The basic idea in solving this problem is to find a causal chain of items that represent the relevant relations between different states of the network where each item represents the prior distribution of causally relevant properties. The classical deterministic duality of causality guarantees that no causal chain can be generated, and vice versa. This approach is usually used in reinforcement learning or to solve a neural protocol problems. The results obtained so far can be better understood by this viewpoint, as opposed to the classical deterministic duality. The paper presents a new deterministic duality of causal chain search using a different-state deterministic model.

Structural Correspondence Analysis for Semi-supervised Learning

Learning the Genre Vectors Using Word Embedding

# Learning to Detect Small Signs from Large Images

An Improved Clustering Method with Improved Variational Inference

A Discriminative Model for Relation DiscoveryThe problem of causal chain discovery (CCD) is an application of the deterministic duality of causality. The basic idea in solving this problem is to find a causal chain of items that represent the relevant relations between different states of the network where each item represents the prior distribution of causally relevant properties. The classical deterministic duality of causality guarantees that no causal chain can be generated, and vice versa. This approach is usually used in reinforcement learning or to solve a neural protocol problems. The results obtained so far can be better understood by this viewpoint, as opposed to the classical deterministic duality. The paper presents a new deterministic duality of causal chain search using a different-state deterministic model.