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: Policies for complex visual tasks have been successfully learned with deep reinforcement learning, using an approach called deep Q-networks (DQN), but relatively large (task-specific) networks and extensive training are needed to achieve good performance. In this work, we present a novel method called policy distillation that can be used to extract the policy of a reinforcement learning agent and train a new network that performs at the expert level while being dramatically smaller and more efficient. Furthermore, the same method can be used to consolidate multiple task-specific policies into a single policy. We demonstrate these claims using the Atari domain and show that the multi-task distilled agent outperforms the single-task teachers as well as a jointly-trained DQN agent.

Output: POLICY DISTILLATION


Input: Consider Input: Knowledge graph embedding aims to embed entities and relations of knowledge graphs into low-dimensional vector spaces. Translating embedding methods regard relations as the translation from head entities to tail entities, which achieve the state-of-the-art results among knowledge graph embedding methods. However, a major limitation of these methods is the time consuming training process, which may take several days or even weeks for large knowledge graphs, and result in great diculty in practical applications. In this paper, we propose an ecient parallel framework for translating embedding methods, called ParTrans-X, which enables the methods to be paralleled without locks by utilizing the distinguished structures of knowledge graphs. Experiments on two datasets with three typical translating embedding methods, i.e., TransE [3], TransH [19], and a more ecient variant TransEAdaGrad [11] validate that ParTrans-X can speed up the training process by more than an order of magnitude.

Output: Eicient Parallel Translating Embedding For Knowledge Graphs


Input: Consider Input: In previous work [BGHK92, BGHK93], we have studied the random-worlds approach�a particular (and quite powerful) method for generating degrees of belief (i.e., subjective probabilities) from a knowl­ edge base consisting of objective (first-order, slalisti­ cal, and defaull) infonnation. But allowing a know l ­ edge base to contain only objective information is sometimes limiting. We occa<;ionally wish to include infonnation about degrees of belief in the knowledge base as well, because there are contexts in which old be1iefs represent important information that should influence new beliefs. In this paper, we describe three quite general techniques for extending a method that generates degrees of belief from objective informa­ tion to one that can make use of degrees of belief as well. All of our techniques are ha<;ed on well-known approaches, such a5 cross-emropy. We discuss gen­ eral connections between the techniques and in partic­ ular show that, although conceptually and technically quite different, all of the techniques give the same answer when applied to the random-worlds method.


Output: Generating New Beliefs From Old*