17,102 research outputs found
Cross-relation Cross-bag Attention for Distantly-supervised Relation Extraction
Distant supervision leverages knowledge bases to automatically label
instances, thus allowing us to train relation extractor without human
annotations. However, the generated training data typically contain massive
noise, and may result in poor performances with the vanilla supervised
learning. In this paper, we propose to conduct multi-instance learning with a
novel Cross-relation Cross-bag Selective Attention (CSA), which leads to
noise-robust training for distant supervised relation extractor. Specifically,
we employ the sentence-level selective attention to reduce the effect of noisy
or mismatched sentences, while the correlation among relations were captured to
improve the quality of attention weights. Moreover, instead of treating all
entity-pairs equally, we try to pay more attention to entity-pairs with a
higher quality. Similarly, we adopt the selective attention mechanism to
achieve this goal. Experiments with two types of relation extractor demonstrate
the superiority of the proposed approach over the state-of-the-art, while
further ablation studies verify our intuitions and demonstrate the
effectiveness of our proposed two techniques.Comment: AAAI 201
Approximate Dynamic Programming with Gaussian Processes
In general, it is difficult to determine an optimal closed-loop policy in nonlinear control problems with continuous-valued state and control domains. Hence, approximations are often inevitable. The standard method of discretizing states and controls suffers from the curse of dimensionality and strongly depends on the chosen temporal sampling rate. In this paper, we introduce Gaussian process dynamic programming (GPDP) and determine an approximate globally optimal closed-loop policy. In GPDP, value functions in the Bellman recursion of the dynamic programming algorithm are modeled using Gaussian processes. GPDP returns an optimal statefeedback for a finite set of states. Based on these outcomes, we learn a possibly discontinuous closed-loop policy on the entire state space by switching between two independently trained Gaussian processes. A binary classifier selects one Gaussian process to predict the optimal control signal. We show that GPDP is able to yield an almost optimal solution to an LQ problem using few sample points. Moreover, we successfully apply GPDP to the underpowered pendulum swing up, a complex nonlinear control problem
- …