17,134 research outputs found
Results of the WMT16 Tuning Shared Task
This paper presents the results of the
WMT16 Tuning Shared Task. We provided
the participants of this task with a
complete machine translation system and
asked them to tune its internal parameters
(feature weights). The tuned systems were
used to translate the test set and the outputs
were manually ranked for translation
quality. We received 4 submissions in the
Czech-English and 8 in the English-Czech
translation direction. In addition, we ran
2 baseline setups, tuning the parameters
with standard optimizers for BLEU score.
In contrast to previous years, the tuned
systems in 2016 rely on large data
Results of the WMT15 Tuning Shared Task
This paper presents the results of the WMT15 Tuning Shared Task. We provided the
participants of this task with a complete machine translation system and asked them to tune its
internal parameters (feature weights). The tuned systems were used to translate the test set and
the outputs were manually ranked for translation quality. We received 4 submissions in the
English-Czech and 6 in the Czech-English translation direction. In addition, we ran
3 baseline setups, tuning the
parameters with standard optimizers for BLEU score
MAGAN: Margin Adaptation for Generative Adversarial Networks
We propose the Margin Adaptation for Generative Adversarial Networks (MAGANs)
algorithm, a novel training procedure for GANs to improve stability and
performance by using an adaptive hinge loss function. We estimate the
appropriate hinge loss margin with the expected energy of the target
distribution, and derive principled criteria for when to update the margin. We
prove that our method converges to its global optimum under certain
assumptions. Evaluated on the task of unsupervised image generation, the
proposed training procedure is simple yet robust on a diverse set of data, and
achieves qualitative and quantitative improvements compared to the
state-of-the-art
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