Several works have proposed Simplicity Bias (SB)---the tendency of standard
training procedures such as Stochastic Gradient Descent (SGD) to find simple
models---to justify why neural networks generalize well [Arpit et al. 2017,
Nakkiran et al. 2019, Soudry et al. 2018]. However, the precise notion of
simplicity remains vague. Furthermore, previous settings that use SB to
theoretically justify why neural networks generalize well do not simultaneously
capture the non-robustness of neural networks---a widely observed phenomenon in
practice [Goodfellow et al. 2014, Jo and Bengio 2017]. We attempt to reconcile
SB and the superior standard generalization of neural networks with the
non-robustness observed in practice by designing datasets that (a) incorporate
a precise notion of simplicity, (b) comprise multiple predictive features with
varying levels of simplicity, and (c) capture the non-robustness of neural
networks trained on real data. Through theory and empirics on these datasets,
we make four observations: (i) SB of SGD and variants can be extreme: neural
networks can exclusively rely on the simplest feature and remain invariant to
all predictive complex features. (ii) The extreme aspect of SB could explain
why seemingly benign distribution shifts and small adversarial perturbations
significantly degrade model performance. (iii) Contrary to conventional wisdom,
SB can also hurt generalization on the same data distribution, as SB persists
even when the simplest feature has less predictive power than the more complex
features. (iv) Common approaches to improve generalization and
robustness---ensembles and adversarial training---can fail in mitigating SB and
its pitfalls. Given the role of SB in training neural networks, we hope that
the proposed datasets and methods serve as an effective testbed to evaluate
novel algorithmic approaches aimed at avoiding the pitfalls of SB.

本文旨在通过设计包含不同时简单性的多个预测特征的数据集，捕捉实际训练数据中的非鲁棒性，从理论和实证研究中发现简洁性偏见在训练神经网络中的作用及其对泛化和鲁棒性的影响，提出新算法以避免简洁性偏见的缺陷。

神经网络中简化偏差的陷阱

The Pitfalls of Simplicity Bias in Neural Networks

Recent works show that Graph Neural Networks (GNNs) are highly non-robust
with respect to adversarial attacks on both the graph structure and the node
attributes, making their outcomes unreliable. We propose the first method for
certifiable (non-)robustness of graph convolutional networks with respect to
perturbations of the node attributes. We consider the case of binary node
attributes (e.g. bag-of-words) and perturbations that are L_0-bounded. If a
node has been certified with our method, it is guaranteed to be robust under
any possible perturbation given the attack model. Likewise, we can certify
non-robustness. Finally, we propose a robust semi-supervised training procedure
that treats the labeled and unlabeled nodes jointly. As shown in our
experimental evaluation, our method significantly improves the robustness of
the GNN with only minimal effect on the predictive accuracy.

本文提出了第一种证明（非）稳健性的图卷积网络方法，该方法能够在考虑特定攻击模型的情况下，保证节点属性的 L0 - 范围内的扰动。最后，我们还提出了一种鲁棒的半监督训练过程，通过实验证明我们的方法证明了图神经网络的鲁棒性，对预测精度的影响最小。

图卷积网络的可认证鲁棒性和鲁棒训练

Certifiable Robustness and Robust Training for Graph Convolutional  Networks

Binary neural networks (BNN) have been studied extensively since they run
dramatically faster at lower memory and power consumption than floating-point
networks, thanks to the efficiency of bit operations. However, contemporary
BNNs whose weights and activations are both single bits suffer from severe
accuracy degradation. To understand why, we investigate the representation
ability, speed and bias/variance of BNNs through extensive experiments. We
conclude that the error of BNNs is predominantly caused by the intrinsic
instability (training time) and non-robustness (train & test time). Inspired by
this investigation, we propose the Binary Ensemble Neural Network (BENN) which
leverages ensemble methods to improve the performance of BNNs with limited
efficiency cost. While ensemble techniques have been broadly believed to be
only marginally helpful for strong classifiers such as deep neural networks,
our analyses and experiments show that they are naturally a perfect fit to
boost BNNs. We find that our BENN, which is faster and much more robust than
state-of-the-art binary networks, can even surpass the accuracy of the
full-precision floating number network with the same architecture.

对二元神经网络进行了广泛的实验以了解其表示能力、速度及偏差 / 方差。提出了一种二元集成神经网络，通过集成方法来提高二元神经网络的性能，从而更快、更鲁棒，甚至能够超越具有相同结构的完全精确的浮点数网络的准确性。