Predicting the performance of highly configurable software systems is the
foundation for performance testing and quality assurance. To that end, recent
work has been relying on machine/deep learning to model software performance.
However, a crucial yet unaddressed challenge is how to cater for the sparsity
inherited from the configuration landscape: the influence of configuration
options (features) and the distribution of data samples are highly sparse.
In this paper, we propose an approach based on the concept of
'divide-and-learn', dubbed $DaL$. The basic idea is that, to handle sample
sparsity, we divide the samples from the configuration landscape into distant
divisions, for each of which we build a regularized Deep Neural Network as the
local model to deal with the feature sparsity. A newly given configuration
would then be assigned to the right model of division for the final prediction.
Experiment results from eight real-world systems and five sets of training
data reveal that, compared with the state-of-the-art approaches, $DaL$ performs
no worse than the best counterpart on 33 out of 40 cases (within which 26 cases
are significantly better) with up to $1.94\times$ improvement on accuracy;
requires fewer samples to reach the same/better accuracy; and producing
acceptable training overhead. Practically, $DaL$ also considerably improves
different global models when using them as the underlying local models, which
further strengthens its flexibility. To promote open science, all the data,
code, and supplementary figures of this work can be accessed at our repository:
this https URL

通过提出 DaL 算法，将高度可配置软件系统的样本空间划分成不同的分区并在每个区域使用规范化的神经网络来应对功能稀疏性，以提高样本预测的准确度和效率。

用分治学习预测软件性能

Predicting Software Performance with Divide-and-Learn

Generalising dialogue state tracking (DST) to new data is especially
challenging due to the strong reliance on abundant and fine-grained supervision
during training. Sample sparsity, distributional shift and the occurrence of
new concepts and topics frequently lead to severe performance degradation
during inference. In this paper we propose a training strategy to build
extractive DST models without the need for fine-grained manual span labels. Two
novel input-level dropout methods mitigate the negative impact of sample
sparsity. We propose a new model architecture with a unified encoder that
supports value as well as slot independence by leveraging the attention
mechanism. We combine the strengths of triple copy strategy DST and value
matching to benefit from complementary predictions without violating the
principle of ontology independence. Our experiments demonstrate that an
extractive DST model can be trained without manual span labels. Our
architecture and training strategies improve robustness towards sample
sparsity, new concepts and topics, leading to state-of-the-art performance on a
range of benchmarks. We further highlight our model's ability to effectively
learn from non-dialogue data.

本文介绍了一个基于 attention mechanism 的迁移学习方法，适用于在训练过程中缺乏丰富人工标注数据的情况下训练 extractive DST 模型，通过两种新颖的输入级 dropout 方法来减轻样本稀疏性的负面影响。我们的实验表明，提出的模型训练策略和架构方法在应对新概念、样本稀疏性等挑战时表现出优越性。

使用弱监督和稀疏数据的稳健对话状态跟踪

Robust Dialogue State Tracking with Weak Supervision and Sparse Data

Distinguishing subtle differences in attributes is valuable, yet learning to
make visual comparisons remains non-trivial. Not only is the number of possible
comparisons quadratic in the number of training images, but also access to
images adequately spanning the space of fine-grained visual differences is
limited. We propose to overcome the sparsity of supervision problem via
synthetically generated images. Building on a state-of-the-art image generation
engine, we sample pairs of training images exhibiting slight modifications of
individual attributes. Augmenting real training image pairs with these
examples, we then train attribute ranking models to predict the relative
strength of an attribute in novel pairs of real images. Our results on datasets
of faces and fashion images show the great promise of bootstrapping imperfect
image generators to counteract sample sparsity for learning to rank.

通过合成图像来弥补样本稀缺性问题的研究，其结果显示出使用不完美的图像生成器来学习排名具有很大的潜力。