This work presents the design, implementation and validation of learning
techniques based on the kNN scheme for gesture detection in prosthetic control.
To cope with high computational demands in instance-based prediction, methods
of dataset reduction are evaluated considering real-time determinism to allow
for the reliable integration into battery-powered portable devices. The
influence of parameterization and varying proportionality schemes is analyzed,
utilizing an eight-channel-sEMG armband. Besides offline cross-validation
accuracy, success rates in real-time pilot experiments (online target
achievement tests) are determined. Based on the assessment of specific dataset
reduction techniques' adequacy for embedded control applications regarding
accuracy and timing behaviour, Decision Surface Mapping (DSM) proves itself
promising when applying kNN on the reduced set. A randomized, double-blind user
study was conducted to evaluate the respective methods (kNN and kNN with
DSM-reduction) against Ridge Regression (RR) and RR with Random Fourier
Features (RR-RFF). The kNN-based methods performed significantly better
(p<0.0005) than the regression techniques. Between DSM-kNN and kNN, there was
no statistically significant difference (significance level 0.05). This is
remarkable in consideration of only one sample per class in the reduced set,
thus yielding a reduction rate of over 99% while preserving success rate. The
same behaviour could be confirmed in an extended user study. With k=1, which
turned out to be an excellent choice, the runtime complexity of both kNN (in
every prediction step) as well as DSM-kNN (in the training phase) becomes
linear concerning the number of original samples, favouring dependable wearable
prosthesis applications.

基于 kNN 方案的学习技术在假肢控制中实现了姿势检测，通过评估数据集减小的方法，确保了高效的实时决策，从而使其能够可靠地集成到电池供电的便携设备中。

实例化学习与原型缩减在实时比例肌电控制中的应用：一个随机用户研究展示了为假肢嵌入式系统实现精确性保持的数据缩减

Instance-based Learning with Prototype Reduction for Real-Time  Proportional Myocontrol: A Randomized User Study Demonstrating  Accuracy-preserving Data Reduction for Prosthetic Embedded Systems

Calibrating deep learning models to yield uncertainty-aware predictions is
crucial as deep neural networks get increasingly deployed in safety-critical
applications. While existing post-hoc calibration methods achieve impressive
results on in-domain test datasets, they are limited by their inability to
yield reliable uncertainty estimates in domain-shift and out-of-domain (OOD)
scenarios. We aim to bridge this gap by proposing DAC, an accuracy-preserving
as well as Density-Aware Calibration method based on k-nearest-neighbors (KNN).
In contrast to existing post-hoc methods, we utilize hidden layers of
classifiers as a source for uncertainty-related information and study their
importance. We show that DAC is a generic method that can readily be combined
with state-of-the-art post-hoc methods. DAC boosts the robustness of
calibration performance in domain-shift and OOD, while maintaining excellent
in-domain predictive uncertainty estimates. We demonstrate that DAC leads to
consistently better calibration across a large number of model architectures,
datasets, and metrics. Additionally, we show that DAC improves calibration
substantially on recent large-scale neural networks pre-trained on vast amounts
of data.

使用基于最近邻的密度感知校准方法，利用分类器的隐藏层作为不确定性相关信息的来源对深度学习模型进行校准，提高了对域漂移和离域数据的可靠性，并在保持良好域内预测不确定性估计的同时，提高了校准的鲁棒性。

超越领域场景：稳健的密度感知校准

Beyond In-Domain Scenarios: Robust Density-Aware Calibration

Data poisoning attacks and backdoor attacks aim to corrupt a machine learning
classifier via modifying, adding, and/or removing some carefully selected
training examples, such that the corrupted classifier makes incorrect
predictions as the attacker desires. The key idea of state-of-the-art certified
defenses against data poisoning attacks and backdoor attacks is to create a
majority vote mechanism to predict the label of a testing example. Moreover,
each voter is a base classifier trained on a subset of the training dataset.
Classical simple learning algorithms such as k nearest neighbors (kNN) and
radius nearest neighbors (rNN) have intrinsic majority vote mechanisms. In this
work, we show that the intrinsic majority vote mechanisms in kNN and rNN
already provide certified robustness guarantees against data poisoning attacks
and backdoor attacks. Moreover, our evaluation results on MNIST and CIFAR10
show that the intrinsic certified robustness guarantees of kNN and rNN
outperform those provided by state-of-the-art certified defenses. Our results
serve as standard baselines for future certified defenses against data
poisoning attacks and backdoor attacks.

利用 kNN 和 rNN 内在的多数投票机制可以提供针对数据污染攻击和后门攻击的保护，并且我们的评估结果表明 kNN 和 rNN 的内在保护机制胜过目前最先进的合格防御。

最近邻居对数据恶意植入和后门攻击的认证鲁棒性

Certified Robustness of Nearest Neighbors against Data Poisoning and  Backdoor Attacks

Interpretability techniques in NLP have mainly focused on understanding
individual predictions using attention visualization or gradient-based saliency
maps over tokens. We propose using k nearest neighbor (kNN) representations to
identify training examples responsible for a model's predictions and obtain a
corpus-level understanding of the model's behavior. Apart from
interpretability, we show that kNN representations are effective at uncovering
learned spurious associations, identifying mislabeled examples, and improving
the fine-tuned model's performance. We focus on Natural Language Inference
(NLI) as a case study and experiment with multiple datasets. Our method deploys
backoff to kNN for BERT and RoBERTa on examples with low model confidence
without any update to the model parameters. Our results indicate that the kNN
approach makes the finetuned model more robust to adversarial inputs.

使用 kNN 表示法来解释 NLP 模型的预测结果，发现其除了具有解释性外，还能揭示学习到的虚假关联，发现存在错误标记的例子，并提高模型的性能和抵御对抗攻击的能力。