### VIP内容

1、Approximation Ratios of Graph Neural Networks for Combinatorial Problems

2、D-VAE: A Variational Autoencoder for Directed Acyclic Graphs

3、End to end learning and optimization on graphs

4、Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels

5、HyperGCN: A New Method of Training Graph Convolutional Networks on Hypergraphs

6、Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks

7、Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching

8、Universal Invariant and Equivariant Graph Neural Networks

### 最新内容

In this paper we revisit the 2014 NeurIPS experiment that examined inconsistency in conference peer review. We determine that 50\% of the variation in reviewer quality scores was subjective in origin. Further, with seven years passing since the experiment we find that for \emph{accepted} papers, there is no correlation between quality scores and impact of the paper as measured as a function of citation count. We trace the fate of rejected papers, recovering where these papers were eventually published. For these papers we find a correlation between quality scores and impact. We conclude that the reviewing process for the 2014 conference was good for identifying poor papers, but poor for identifying good papers. We give some suggestions for improving the reviewing process but also warn against removing the subjective element. Finally, we suggest that the real conclusion of the experiment is that the community should place less onus on the notion of top-tier conference publications' when assessing the quality of individual researchers. For NeurIPS 2021, the PCs are repeating the experiment, as well as conducting new ones.

### 最新论文

In this paper we revisit the 2014 NeurIPS experiment that examined inconsistency in conference peer review. We determine that 50\% of the variation in reviewer quality scores was subjective in origin. Further, with seven years passing since the experiment we find that for \emph{accepted} papers, there is no correlation between quality scores and impact of the paper as measured as a function of citation count. We trace the fate of rejected papers, recovering where these papers were eventually published. For these papers we find a correlation between quality scores and impact. We conclude that the reviewing process for the 2014 conference was good for identifying poor papers, but poor for identifying good papers. We give some suggestions for improving the reviewing process but also warn against removing the subjective element. Finally, we suggest that the real conclusion of the experiment is that the community should place less onus on the notion of top-tier conference publications' when assessing the quality of individual researchers. For NeurIPS 2021, the PCs are repeating the experiment, as well as conducting new ones.

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