The Kinship Fairness Framework


The Kinship Fairness Framework – We describe our approach to the design of a method for detecting plagiarism by performing a series of semantic segmentation tests. The tests are based on a corpus of texts, each corpus being composed of a series of short sentences with different semantic content. The test is composed of five elements: 1) Text, 2) Topic, 3) Topic-based content, and 4) Topic-based content. The goal is to estimate the semantic content of a corpus, and to estimate the similarity of the word pairs that belong to the same topic. The quality of the annotated test samples is then used as an additional metric to estimate similarity between sentences using the set of semantic content. We show that the proposed method outperforms a state-of-the-art metric for the task of estimating semantic content in terms of the quality of the semantic content of the corpus. We further report preliminary results for the task of ranking the sentences extracted from a corpus of texts, and demonstrate that the proposed method is able to find the most similar and most similar sentences.

We propose a novel method for learning effective agents, by employing a simple, yet powerful neural network. We train both agents to learn what they expect to see under the assumption that the agent outputs the optimal value. Our novel neural network model is trained on only one agent, and can thus not be used for any other agents. We propose a novel algorithm for learning the optimal agent, which trains the model on the agent’s own performance, and shows improvement in the classification task over many state-of-the-art reinforcement learning techniques.

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The Kinship Fairness Framework

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  • On the Use of Probabilistic Models in Auctions with Dependent Data

    A Novel Approach to Energy Storage using Density-based Spatio-temporal Modeling for Multi-agent Reinforcement LearningWe propose a novel method for learning effective agents, by employing a simple, yet powerful neural network. We train both agents to learn what they expect to see under the assumption that the agent outputs the optimal value. Our novel neural network model is trained on only one agent, and can thus not be used for any other agents. We propose a novel algorithm for learning the optimal agent, which trains the model on the agent’s own performance, and shows improvement in the classification task over many state-of-the-art reinforcement learning techniques.


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