825,712 research outputs found
CrossNorm: Normalization for Off-Policy TD Reinforcement Learning
Off-policy temporal difference (TD) methods are a powerful class of
reinforcement learning (RL) algorithms. Intriguingly, deep off-policy TD
algorithms are not commonly used in combination with feature normalization
techniques, despite positive effects of normalization in other domains. We show
that naive application of existing normalization techniques is indeed not
effective, but that well-designed normalization improves optimization stability
and removes the necessity of target networks. In particular, we introduce a
normalization based on a mixture of on- and off-policy transitions, which we
call cross-normalization. It can be regarded as an extension of batch
normalization that re-centers data for two different distributions, as present
in off-policy learning. Applied to DDPG and TD3, cross-normalization improves
over the state of the art across a range of MuJoCo benchmark tasks
Normalization at the field level: fractional counting of citations
Van Raan et al. (2010; arXiv:1003.2113) have proposed a new indicator (MNCS)
for field normalization. Since field normalization is also used in the Leiden
Rankings of universities, we elaborate our critique of journal normalization in
Opthof & Leydesdorff (2010; arXiv:1002.2769) in this rejoinder concerning field
normalization. Fractional citation counting thoroughly solves the issue of
normalization for differences in citation behavior among fields. This indicator
can also be used to obtain a normalized impact factor
A Domain Agnostic Normalization Layer for Unsupervised Adversarial Domain Adaptation
We propose a normalization layer for unsupervised domain adaption in semantic
scene segmentation. Normalization layers are known to improve convergence and
generalization and are part of many state-of-the-art fully-convolutional neural
networks. We show that conventional normalization layers worsen the performance
of current Unsupervised Adversarial Domain Adaption (UADA), which is a method
to improve network performance on unlabeled datasets and the focus of our
research. Therefore, we propose a novel Domain Agnostic Normalization layer and
thereby unlock the benefits of normalization layers for unsupervised
adversarial domain adaptation. In our evaluation, we adapt from the synthetic
GTA5 data set to the real Cityscapes data set, a common benchmark experiment,
and surpass the state-of-the-art. As our normalization layer is domain agnostic
at test time, we furthermore demonstrate that UADA using Domain Agnostic
Normalization improves performance on unseen domains, specifically on
Apolloscape and Mapillary
The WMAP normalization of inflationary cosmologies
We use the three-year WMAP observations to determine the normalization of the
matter power spectrum in inflationary cosmologies. In this context, the
quantity of interest is not the normalization marginalized over all parameters,
but rather the normalization as a function of the inflationary parameters n and
r with marginalization over the remaining cosmological parameters. We compute
this normalization and provide an accurate fitting function. The statistical
uncertainty in the normalization is 3 percent, roughly half that achieved by
COBE. We use the k-l relation for the standard cosmological model to identify
the pivot scale for the WMAP normalization. We also quote the inflationary
energy scale corresponding to the WMAP normalization.Comment: 4 pages RevTex4 with two figure
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