Yet, their performance degrades in the presence of noisy labels at train
time. Inspired by the setting of learning with expert advice, where
multiplicative weights (MW) updates were recently shown to be robust to
moderate data corruptions in expert advice, we propose to use MW for
reweighting examples during neural networks optimization. We theoretically
establish the convergence of our method when used with gradient descent and
prove its advantage for label noise in 1d cases. We then validate empirically
our findings for the general case by showing that MW improves neural networks
accuracy in the presence of label noise on CIFAR-10, CIFAR-100 and Clothing1M.
We also show the impact of our approach on adversarial robustness.

提出一种使用 MW 重新加权示例的神经网络优化方法，该方法在标签存在噪声的情况下稳健且可提高准确性，同时不会影响其对抗鲁棒性。

用乘法重新加权进行神经网络鲁棒性优化

Multiplicative Reweighting for Robust Neural Network Optimization

Balanced graph partitioning is a critical step for many large-scale
distributed computations with relational data. As graph datasets have grown in
size and density, a range of highly-scalable balanced partitioning algorithms
have appeared to meet varied demands across different domains. As the starting
point for the present work, we observe that two recently introduced families of
iterative partitioners---those based on restreaming and those based on balanced
label propagation (including Facebook's Social Hash Partitioner)---can be
viewed through a common modular framework of design decisions. With the help of
this modular perspective, we find that a key combination of design decisions
leads to a novel family of algorithms with notably better empirical performance
than any existing highly-scalable algorithm on a broad range of real-world
graphs. The resulting prioritized restreaming algorithms employ a constraint
management strategy based on multiplicative weights, borrowed from the
restreaming literature, while adopting notions of priority from balanced label
propagation to optimize the ordering of the streaming process. Our experimental
results consider a range of stream orders, where a dynamic ordering based on
what we call ambivalence is broadly the most performative in terms of the cut
quality of the resulting balanced partitions, with a static ordering based on
degree being nearly as good.

介绍了一种新型的基于 restreaming 的算法，使用带有乘性权重的约束管理策略来优化流式过程的排序，同时借鉴了平衡标签传播的优先级概念来进行分区优化，得到了与现有算法相比在真实世界图上具有更好性能的结果。

基于优先级的平衡图分区再流算法

Prioritized Restreaming Algorithms for Balanced Graph Partitioning

Recently, it was shown that if multiplicative weights are assigned to the
edges of a Tanner graph used in belief propagation decoding, it is possible to
use deep learning techniques to find values for the weights which improve the
error-correction performance of the decoder. Unfortunately, this approach
requires many multiplications, which are generally expensive operations. In
this paper, we suggest a more hardware-friendly approach in which offset
min-sum decoding is augmented with learnable offset parameters. Our method uses
no multiplications and has a parameter count less than half that of the
multiplicative algorithm. This both speeds up training and provides a feasible
path to hardware architectures. After describing our method, we compare the
performance of the two neural decoding algorithms and show that our method
achieves error-correction performance within 0.1 dB of the multiplicative
approach and as much as 1 dB better than traditional belief propagation for the
codes under consideration.

使用可学习的偏置参数，我们提出一种硬件友好的方法，以取代在 Tanner 图中使用乘法权重的深度学习技术，从而改进纠错解码器的性能，并将其与另一种神经解码算法进行比较，结果在考虑的代码中实现了比传统置信传播高 1dB 的错误纠正性能，并取得比乘法算法少 0.1dB 的性能。