In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Input: Consider Input: This paper presents capabilities of using genetic algorithms to find approximations of function extrema, which cannot be found using analytic ways. To enhance effectiveness of calculations, algorithm has been parallelized using OpenMP library. We gained much increase in speed on platforms using multithreaded processors with shared memory free access. During analysis we used different modifications of genetic operator, using them we obtained varied evolution process of potential solutions. Results allow to choose best methods among many applied in genetic algorithms and observation of acceleration on Yorkfield, Bloomfield, Westmere-EX and most recent Sandy Bridge cores.

Output: GENERATING EXTREMA APPROXIMATION OF ANALYTICALLY INCOMPUTABLE FUNCTIONS THROUGH USAGE OF PARALLEL COMPUTER AIDED GENETIC ALGORITHMS


Input: Consider Input: The ubiquity of metaphor in our everyday communication makes it an important problem for natural language understanding. Yet, the majority of metaphor processing systems to date rely on handengineered features and there is still no consensus in the field as to which features are optimal for this task. In this paper, we present the first deep learning architecture designed to capture metaphorical composition. Our results demonstrate that it outperforms the existing approaches in the metaphor identification task.

Output: Grasping the Finer Point: A Supervised Similarity Network for Metaphor Detection


Input: Consider Input: We consider the problem of efficient exploration in finite horizon MDPs. We show that an optimistic modification to modelbased value iteration, can achieve a regret bound Õ( √ HSAT+HSA+H √ T )whereH is the time horizon, S the number of states, A the number of actions and T the time elapsed. This result improves over the best previous known bound Õ(HS √ AT ) achieved by the UCRL2 algorithm Jaksch et al. (2010a). The key significance of our new results is that when T ≥ HSA and SA ≥ H , it leads to a regret of Õ( √ HSAT ) that matches the established lower bounds of Ω( √ HSAT ) up to a logarithmic factor. Our analysis contain two key insights. We use careful application of concentration inequalities to the optimal value function as a whole, rather than to the transitions probabilities (to improve scaling in S), and we use "exploration bonuses" based on Bernstein’s inequality, together with using a recursive -Bellman-typeLaw of Total Variance (to improve scaling in H).


Output: Minimax Regret Bounds for Reinforcement Learning