14,563 research outputs found
Path Ranking with Attention to Type Hierarchies
The objective of the knowledge base completion problem is to infer missing
information from existing facts in a knowledge base. Prior work has
demonstrated the effectiveness of path-ranking based methods, which solve the
problem by discovering observable patterns in knowledge graphs, consisting of
nodes representing entities and edges representing relations. However, these
patterns either lack accuracy because they rely solely on relations or cannot
easily generalize due to the direct use of specific entity information. We
introduce Attentive Path Ranking, a novel path pattern representation that
leverages type hierarchies of entities to both avoid ambiguity and maintain
generalization. Then, we present an end-to-end trained attention-based RNN
model to discover the new path patterns from data. Experiments conducted on
benchmark knowledge base completion datasets WN18RR and FB15k-237 demonstrate
that the proposed model outperforms existing methods on the fact prediction
task by statistically significant margins of 26% and 10%, respectively.
Furthermore, quantitative and qualitative analyses show that the path patterns
balance between generalization and discrimination.Comment: Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20
Investigating Simple Object Representations in Model-Free Deep Reinforcement Learning
We explore the benefits of augmenting state-of-the-art model-free deep
reinforcement algorithms with simple object representations. Following the
Frostbite challenge posited by Lake et al. (2017), we identify object
representations as a critical cognitive capacity lacking from current
reinforcement learning agents. We discover that providing the Rainbow model
(Hessel et al.,2018) with simple, feature-engineered object representations
substantially boosts its performance on the Frostbite game from Atari 2600. We
then analyze the relative contributions of the representations of different
types of objects, identify environment states where these representations are
most impactful, and examine how these representations aid in generalizing to
novel situations
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