Scientific papers are complex and understanding the usefulness of these
papers requires prior knowledge. Peer reviews are comments on a paper provided
by designated experts on that field and hold a substantial amount of
information, not only for the editors and chairs to make the final decision,
but also to judge the potential impact of the paper. In this paper, we propose
to use aspect-based sentiment analysis of scientific reviews to be able to
extract useful information, which correlates well with the accept/reject
decision.
While working on a dataset of close to 8k reviews from ICLR, one of the top
conferences in the field of machine learning, we use an active learning
framework to build a training dataset for aspect prediction, which is further
used to obtain the aspects and sentiments for the entire dataset. We show that
the distribution of aspect-based sentiments obtained from a review is
significantly different for accepted and rejected papers. We use the aspect
sentiments from these reviews to make an intriguing observation, certain
aspects present in a paper and discussed in the review strongly determine the
final recommendation. As a second objective, we quantify the extent of
disagreement among the reviewers refereeing a paper. We also investigate the
extent of disagreement between the reviewers and the chair and find that the
inter-reviewer disagreement may have a link to the disagreement with the chair.
One of the most interesting observations from this study is that reviews, where
the reviewer score and the aspect sentiments extracted from the review text
written by the reviewer are consistent, are also more likely to be concurrent
with the chair's decision.

利用基于方面的情感分析从科学评论中提取有用信息可以更好地判断接受 / 拒绝决策，并且发现审稿人之间的不一致程度可能与主席的意见不一致有关。

科技评论的基于方面的情感分析

Aspect-based Sentiment Analysis of Scientific Reviews

Adaptive attacks have (rightfully) become the de facto standard for
evaluating defenses to adversarial examples. We find, however, that typical
adaptive evaluations are incomplete. We demonstrate that thirteen defenses
recently published at ICLR, ICML and NeurIPS---and chosen for illustrative and
pedagogical purposes---can be circumvented despite attempting to perform
evaluations using adaptive attacks. While prior evaluation papers focused
mainly on the end result---showing that a defense was ineffective---this paper
focuses on laying out the methodology and the approach necessary to perform an
adaptive attack. We hope that these analyses will serve as guidance on how to
properly perform adaptive attacks against defenses to adversarial examples, and
thus will allow the community to make further progress in building more robust
models.

本文探讨了 13 种已在相关会议上发表的对抗性防御方法对抗自适应攻击方法的实验结果，发现这些方法在实际运用中并不完善，因此提出了一种可行的自适应攻击方法，希望能够为研究更为健壮的防御模型提供指导。

关于对抗性样本防御的自适应攻击

On Adaptive Attacks to Adversarial Example Defenses

Although variational autoencoders (VAEs) represent a widely influential deep
generative model, many aspects of the underlying energy function remain poorly
understood. In particular, it is commonly believed that Gaussian
encoder/decoder assumptions reduce the effectiveness of VAEs in generating
realistic samples. In this regard, we rigorously analyze the VAE objective,
differentiating situations where this belief is and is not actually true. We
then leverage the corresponding insights to develop a simple VAE enhancement
that requires no additional hyperparameters or sensitive tuning.
Quantitatively, this proposal produces crisp samples and stable FID scores that
are actually competitive with a variety of GAN models, all while retaining
desirable attributes of the original VAE architecture. A shorter version of
this work will appear in the ICLR 2019 conference proceedings (Dai and Wipf,
2019). The code for our model is available at this https URL
TwoStageVAE.

本研究针对变分自编码器（VAEs）的效率和各种假设，提出了新的 VAE 增强方案，该方案不需要任何超参数或敏感调整，并且与多种 GAN 模型竞争时，能产生清晰的样本和稳定的 FID 分数。