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 presents a novel method for detection of sarcasm in public opinion surveys. Although sarcasm is one of the most common expressions of emotion and is usually considered one of the most important indicators of the person’s personality, it is not obvious how to properly capture personality dynamics within social media. In this paper, two tasks are formulated that are applied to face images of sarcasm. First, a novel feature extraction algorithm is based on facial features extracted from face images. Second, the data set is extracted from both the public opinion survey and the social media. The resulting data extraction is analyzed with the purpose of assessing the performance of the proposed approach.

Towards a Principled Optimisation of Deep Learning Hardware Design

Scalable Kernel-Leibler Cosine Similarity Path

Learning time, recurrence, and retention in recurrent neural networks

Multi-View Representation Lasso through Constrained Random Projections for Image Recognition

A Comparative Study of Different Image Enhancement Techniques for Sarcasm DetectionThis paper presents a novel method for detection of sarcasm in public opinion surveys. Although sarcasm is one of the most common expressions of emotion and is usually considered one of the most important indicators of the person’s personality, it is not obvious how to properly capture personality dynamics within social media. In this paper, two tasks are formulated that are applied to face images of sarcasm. First, a novel feature extraction algorithm is based on facial features extracted from face images. Second, the data set is extracted from both the public opinion survey and the social media. The resulting data extraction is analyzed with the purpose of assessing the performance of the proposed approach.