Ensuring reliable confidence scores from deep neural networks is of paramount
significance in critical decision-making systems, particularly in real-world
domains such as healthcare. Recent literature on calibrating deep segmentation
networks has resulted in substantial progress. Nevertheless, these approaches
are strongly inspired by the advancements in classification tasks, and thus
their uncertainty is usually modeled by leveraging the information of
individual pixels, disregarding the local structure of the object of interest.
Indeed, only the recent Spatially Varying Label Smoothing (SVLS) approach
considers pixel spatial relationships across classes, by softening the pixel
label assignments with a discrete spatial Gaussian kernel. In this work, we
first present a constrained optimization perspective of SVLS and demonstrate
that it enforces an implicit constraint on soft class proportions of
surrounding pixels. Furthermore, our analysis shows that SVLS lacks a mechanism
to balance the contribution of the constraint with the primary objective,
potentially hindering the optimization process. Based on these observations, we
propose NACL (Neighbor Aware CaLibration), a principled and simple solution
based on equality constraints on the logit values, which enables to control
explicitly both the enforced constraint and the weight of the penalty, offering
more flexibility. Comprehensive experiments on a wide variety of well-known
segmentation benchmarks demonstrate the superior calibration performance of the
proposed approach, without affecting its discriminative power. Furthermore,
ablation studies empirically show the model agnostic nature of our approach,
which can be used to train a wide span of deep segmentation networks.

通过等式约束方式，NACL（Neighbor Aware CaLibration）能够在深度分割网络中实现优越的校准性能，而不影响其判别能力。

基于惩罚约束的分割网络的邻近感知校准

Neighbor-Aware Calibration of Segmentation Networks with Penalty-Based  Constraints

Inertial sensing is used in many applications and platforms, ranging from
day-to-day devices such as smartphones to very complex ones such as autonomous
vehicles. In recent years, the development of machine learning and deep
learning techniques has increased significantly in the field of inertial
sensing. This is due to the development of efficient computing hardware and the
accessibility of publicly available sensor data. These data-driven approaches
are used to empower model-based navigation and sensor fusion algorithms. This
paper provides an in-depth review of those deep learning methods. We examine
separately, each vehicle operation domain including land, air, and sea. Each
domain is divided into pure inertial advances and improvements based on filter
parameters learning. In addition, we review deep learning approaches for
calibrating and denoising inertial sensors. Throughout the paper, we discuss
these trends and future directions. We also provide statistics on the commonly
used approaches to illustrate their efficiency and stimulate further research
in deep learning embedded in inertial navigation and fusion.

本文介绍了惯性导航及传感器融合中深度学习的方法及其在车载、航空、海洋等领域中的应用，同时提供了相应的统计数据，以促进更多相关深度学习方面的研究。

惯性导航遇上深度学习：当前趋势与未来发展的综述

Inertial Navigation Meets Deep Learning: A Survey of Current Trends and  Future Directions

Many challenging real-world problems require the deployment of ensembles
multiple complementary learning models to reach acceptable performance levels.
While effective, applying the entire ensemble to every sample is costly and
often unnecessary. Deep Reinforcement Learning (DRL) offers a cost-effective
alternative, where detectors are dynamically chosen based on the output of
their predecessors, with their usefulness weighted against their computational
cost. Despite their potential, DRL-based solutions are not widely used in this
capacity, partly due to the difficulties in configuring the reward function for
each new task, the unpredictable reactions of the DRL agent to changes in the
data, and the inability to use common performance metrics (e.g., TPR/FPR) to
guide the algorithm's performance. In this study we propose methods for
fine-tuning and calibrating DRL-based policies so that they can meet multiple
performance goals. Moreover, we present a method for transferring effective
security policies from one dataset to another. Finally, we demonstrate that our
approach is highly robust against adversarial attacks.

本文研究了使用增强学习来动态选择并加权选择多个学习模型的效果，在此基础上提出了优化和校准方法，并将其应用于安全策略的迁移，结果显示该方法具有高度的鲁棒性。

一种可传递、自动调整的深度强化学习方法，用于成本效益高的网络钓鱼检测

A Transferable and Automatic Tuning of Deep Reinforcement Learning for Cost Effective Phishing Detection

Solid results from Transformers have made them prevailing architectures in
various natural language and vision tasks. As a default component in
Transformers, Layer Normalization (LN) normalizes activations within each token
to boost the robustness. However, LN requires on-the-fly statistics calculation
in inference as well as division and square root operations, leading to
inefficiency on hardware. What is more, replacing LN with other
hardware-efficient normalization schemes (e.g., Batch Normalization) results in
inferior performance, even collapse in training. We find that this dilemma is
caused by abnormal behaviors of activation statistics, including large
fluctuations over iterations and extreme outliers across layers. To tackle
these issues, we propose Unified Normalization (UN), which can speed up the
inference by being fused with other linear operations and achieve comparable
performance on par with LN. UN strives to boost performance by calibrating the
activation and gradient statistics with a tailored fluctuation smoothing
strategy. Meanwhile, an adaptive outlier filtration strategy is applied to
avoid collapse in training whose effectiveness is theoretically proved and
experimentally verified in this paper. We demonstrate that UN can be an
efficient drop-in alternative to LN by conducting extensive experiments on
language and vision tasks. Besides, we evaluate the efficiency of our method on
GPU. Transformers equipped with UN enjoy about 31% inference speedup and nearly
18% memory reduction. Code will be released at
this https URL

提出一种名为统一归一化（UN）的新型网络层归一化方法，通过采用适合的波动整理策略校准激活和梯度统计数据，以避免过多迭代波动和极端异常值的问题，并应用自适应离群值筛选策略以避免训练崩溃。通过在语言和视觉任务上的广泛实验，验证了 UN 作为 LN 的高效替代方法，可以加快推理速度约 31％，减少内存约 18％。