Test-time domain adaptation effectively adjusts the source domain model to
accommodate unseen domain shifts in a target domain during inference. However,
the model performance can be significantly impaired by continuous distribution
changes in the target domain and non-independent and identically distributed
(non-i.i.d.) test samples often encountered in practical scenarios. While
existing memory bank methodologies use memory to store samples and mitigate
non-i.i.d. effects, they do not inherently prevent potential model degradation.
To address this issue, we propose a resilient practical test-time adaptation
(ResiTTA) method focused on parameter resilience and data quality.
Specifically, we develop a resilient batch normalization with estimation on
normalization statistics and soft alignments to mitigate overfitting and model
degradation. We use an entropy-driven memory bank that accounts for timeliness,
the persistence of over-confident samples, and sample uncertainty for
high-quality data in adaptation. Our framework periodically adapts the source
domain model using a teacher-student model through a self-training loss on the
memory samples, incorporating soft alignment losses on batch normalization. We
empirically validate ResiTTA across various benchmark datasets, demonstrating
state-of-the-art performance.

通过在测试时进行领域适应，提出了一种抗干扰的实用测试时间适应（ResiTTA）方法，用于解决模型退化和数据质量问题。利用鲁棒批量归一化方法和软对齐策略缓解过拟合和模型退化，并采用熵驱动的记忆库存储高质量数据。通过教师 - 学生模型和自学习损失对模型进行周期性的适应，最终在多个基准数据集上取得了最先进的性能。

弹性实践测试时间适应：软批归一化对齐和熵驱动的记忆库

Resilient Practical Test-Time Adaptation: Soft Batch Normalization  Alignment and Entropy-driven Memory Bank

Effective recommendation systems rely on capturing user preferences, often
requiring incorporating numerous features such as universally unique
identifiers (UUIDs) of entities. However, the exceptionally high cardinality of
UUIDs poses a significant challenge in terms of model degradation and increased
model size due to sparsity. This paper presents two innovative techniques to
address the challenge of high cardinality in recommendation systems.
Specifically, we propose a bag-of-words approach, combined with layer sharing,
to substantially decrease the model size while improving performance. Our
techniques were evaluated through offline and online experiments on Uber use
cases, resulting in promising results demonstrating our approach's
effectiveness in optimizing recommendation systems and enhancing their overall
performance.

该论文提出了两种创新技术来解决推荐系统中高基数的挑战，通过采用词袋模型和层共享相结合的方法，显著减少模型大小并提高性能，在优步的使用案例中进行了离线和在线实验，取得了有希望的结果，展示了优化推荐系统及提高其整体性能的有效方法。

构建推荐系统中的大规模基数处理

Handling Large-scale Cardinality in building recommendation systems

Federated learning allows for the training of a model using data on multiple
clients without the clients transmitting that raw data. However the standard
method is to transmit model parameters (or updates), which for modern neural
networks can be on the scale of millions of parameters, inflicting significant
computational costs on the clients. We propose a method for federated learning
where instead of transmitting a gradient update back to the server, we instead
transmit a small amount of synthetic `data'. We describe the procedure and show
some experimental results suggesting this procedure has potential, providing
more than an order of magnitude reduction in communication costs with minimal
model degradation.

在联邦学习中，通过传输合成数据而不是模型参数实现通信代价大幅降低和模型效果最小化的方法。