117,205 research outputs found
Stochastic Inverse Reinforcement Learning
The goal of the inverse reinforcement learning (IRL) problem is to recover
the reward functions from expert demonstrations. However, the IRL problem like
any ill-posed inverse problem suffers the congenital defect that the policy may
be optimal for many reward functions, and expert demonstrations may be optimal
for many policies. In this work, we generalize the IRL problem to a well-posed
expectation optimization problem stochastic inverse reinforcement learning
(SIRL) to recover the probability distribution over reward functions. We adopt
the Monte Carlo expectation-maximization (MCEM) method to estimate the
parameter of the probability distribution as the first solution to the SIRL
problem. The solution is succinct, robust, and transferable for a learning task
and can generate alternative solutions to the IRL problem. Through our
formulation, it is possible to observe the intrinsic property for the IRL
problem from a global viewpoint, and our approach achieves a considerable
performance on the objectworld.Comment: 8+2 pages, 5 figures, Under Revie
Evaluations of infinite series involving reciprocal hyperbolic functions
This paper presents a approach of summation of infinite series of hyperbolic
functions. The approach is based on simple contour integral representions and
residue computations with the help of some well known results of Eisenstein
series given by Ramanujan and Berndt et al. Several series involving quadratic
hyperbolic functions are evaluated, which can be expressed in terms of
and . When a certain parameter in these
series equal to the series are summable in terms of functions.
Moreover, some interesting new consequences and illustrative examples are
considered
Entity Recognition at First Sight: Improving NER with Eye Movement Information
Previous research shows that eye-tracking data contains information about the
lexical and syntactic properties of text, which can be used to improve natural
language processing models. In this work, we leverage eye movement features
from three corpora with recorded gaze information to augment a state-of-the-art
neural model for named entity recognition (NER) with gaze embeddings. These
corpora were manually annotated with named entity labels. Moreover, we show how
gaze features, generalized on word type level, eliminate the need for recorded
eye-tracking data at test time. The gaze-augmented models for NER using
token-level and type-level features outperform the baselines. We present the
benefits of eye-tracking features by evaluating the NER models on both
individual datasets as well as in cross-domain settings.Comment: Accepted at NAACL-HLT 201
GM-Net: Learning Features with More Efficiency
Deep Convolutional Neural Networks (CNNs) are capable of learning
unprecedentedly effective features from images. Some researchers have struggled
to enhance the parameters' efficiency using grouped convolution. However, the
relation between the optimal number of convolutional groups and the recognition
performance remains an open problem. In this paper, we propose a series of
Basic Units (BUs) and a two-level merging strategy to construct deep CNNs,
referred to as a joint Grouped Merging Net (GM-Net), which can produce joint
grouped and reused deep features while maintaining the feature discriminability
for classification tasks. Our GM-Net architectures with the proposed BU_A
(dense connection) and BU_B (straight mapping) lead to significant reduction in
the number of network parameters and obtain performance improvement in image
classification tasks. Extensive experiments are conducted to validate the
superior performance of the GM-Net than the state-of-the-arts on the benchmark
datasets, e.g., MNIST, CIFAR-10, CIFAR-100 and SVHN.Comment: 6 Pages, 5 figure
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