In contrast to the control-theoretic methods, the lack of stability guarantee remains a significant problem for model-free reinforcement learning (RL) methods. Jointly learning a policy and a Lyapunov function has recently become a promising approach to ensuring the whole system with a stability guarantee. However, the classical Lyapunov constraints researchers introduced cannot stabilize the system during the sampling-based optimization. Therefore, we propose the Adaptive Stability Certification (ASC), making the system reach sampling-based stability. Because the ASC condition can search for the optimal policy heuristically, we design the Adaptive Lyapunov-based Actor-Critic (ALAC) algorithm based on the ASC condition. Meanwhile, our algorithm avoids the optimization problem that a variety of constraints are coupled into the objective in current approaches. When evaluated on ten robotic tasks, our method achieves lower accumulated cost and fewer stability constraint violations than previous studies.

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

Image super-resolution is a common task on mobile and IoT devices, where one often needs to upscale and enhance low-resolution images and video frames. While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints. In this Mobile AI challenge, we address this problem and propose the participants to design an efficient quantized image super-resolution solution that can demonstrate a real-time performance on mobile NPUs. The participants were provided with the DIV2K dataset and trained INT8 models to do a high-quality 3X image upscaling. The runtime of all models was evaluated on the Synaptics VS680 Smart Home board with a dedicated edge NPU capable of accelerating quantized neural networks. All proposed solutions are fully compatible with the above NPU, demonstrating an up to 60 FPS rate when reconstructing Full HD resolution images. A detailed description of all models developed in the challenge is provided in this paper.

Code pre-trained models (CodePTMs) have recently demonstrated significant success in code intelligence. To interpret these models, some probing methods have been applied. However, these methods fail to consider the inherent characteristics of codes. In this paper, to address the problem, we propose a novel probing method CAT-probing to quantitatively interpret how CodePTMs attend code structure. We first denoise the input code sequences based on the token types pre-defined by the compilers to filter those tokens whose attention scores are too small. After that, we define a new metric CAT-score to measure the commonality between the token-level attention scores generated in CodePTMs and the pair-wise distances between corresponding AST nodes. The higher the CAT-score, the stronger the ability of CodePTMs to capture code structure. We conduct extensive experiments to integrate CAT-probing with representative CodePTMs for different programming languages. Experimental results show the effectiveness of CAT-probing in CodePTM interpretation. Our codes and data are publicly available at https://github.com/nchen909/CodeAttention.

尽管在许多应用中取得了巨大的成功，但深度神经网络在实践中并不总是强大的。例如，用于分类任务的卷积神经元网络（CNN）模型通常在对某些特定类别的对象分类时表现不佳。在这项工作中，我们关注的是修补CNN模型的弱部分，而不是通过整个模型的昂贵重新培训来改进它。受到软件工程中模块化和组成的基本概念的启发，我们提出了一种压缩模块化方法CNNSplitter，该方法将$ N $ class分类的强CNN模型分解为$ n $ n $ n $ n $ smill CNN模块。每个模块都是一个子模型，其中包含强模型的卷积内核的一部分。为了修补对目标类（TC）进行不满意的弱CNN模型，我们将弱的CNN模型与从强CNN模型获得的相应模块组成。因此，弱CNN模型识别TC的能力可以通过修补来提高。此外，识别非TCS的能力也得到了提高，因为将样品错误分类为TC可以正确分类为非TCS。在三个广泛使用的数据集上使用两个代表性CNN的实验结果表明，在精度和召回方面，TC的平均改进分别为12.54％和2.14％。此外，修补程序将非TCS的准确性提高了1.18％。结果表明，CNNSplitter可以通过模块化和组成来修补弱的CNN模型，从而为开发可靠的CNN模型提供了新的解决方案。

机器学习（ML）是一种在车辆互联网（IOV）上培训预测模型的分布式方法，以实现智能公共交通。由于交通状况会随着时间而变化，因此必须连续有效地更新流量流动和乘客等待时间的ML模型。联合学习（FL）是一种分布式机器学习方案，允许车辆接收连续的模型更新，而无需将原始数据上传到云中并等待培训模型。但是，由于车辆在公共场所旅行以来，智能公共交通中FL容易受到中毒或DDOS攻击的影响。此外，由于设备异质性和不平衡数据分布，同步聚合策略在聚集之前从特定车辆中收集本地模型的同步聚合策略效率低下。尽管有异步联合学习（AFL）方案是通过收到本地模型来提高效率的，但陈旧的本地模型仍然不合理地加权，导致学习绩效不佳。为了实现更明智的公共交通，本文提供了一个基于动态缩放系数（DBAFL）的基于区块链的异步联合学习方案。具体而言，基于委员会的新型共识算法用于区块链，以最低的时间成本提高了可靠性。同时，设计的动态缩放系数允许AFL为陈旧的本地模型分配合理的重量。在异质设备上进行的广泛实验验证了DBAFL的学习效果，效率和可靠性优于外观的实验。

从无监督的图像到图像翻译的角度来看，图像漫画化最近由生成对抗网络（gan）主导，其中固有的挑战是精确捕获和充分传递的特征动画片样式（例如，透明边缘，光滑的色彩，抽象，抽象，抽象，抽象，抽象精细的结构等）。现有的高级模型试图通过学习以对抗性来促进边缘，引入样式转移损失或学习从多个表示空间保持一致的样式来增强卡通化效果。本文表明，只有基本的对抗性损失，可以轻松实现更独特和生动的漫画化效果。观察卡通风格在卡通纹理效果的本地图像区域中更为明显，我们与正常图像级平行建立了一个区域级别的对抗学习分支，该分支在卡通质量级别上限制了对抗性学习，以更好感知和转移卡通纹理功能。为此，提出了一种新型的卡通纹理 - 效果 - 采访器（CTSS）模块，以从训练数据中动态采样卡通纹理质量贴片。通过广泛的实验，我们证明了对抗性学习中的纹理显着性适应性注意力，作为图像漫画化中相关方法的缺失成分，在促进和增强图像卡通风格方面至关重要，尤其是对于高分辨率输入图片。

视频识别的标准方法通常在完整的输入视频上运行，由于视频中的时空冗余率广泛，因此效率低下。蒙版视频建模（即视频）的最新进展表明，香草视觉变压器（VIT）仅具有有限的视觉内容来补充时空上下文的能力。受到这一点的启发，我们提出了建议的蒙版动作识别（MAR），该识别（MAR）通过丢弃一定比例的补丁并仅在视频的一部分上操作来减少冗余计算。 MAR包含以下两个必不可少的组件：单元运行掩盖和桥接分类器。具体而言，为了使VIT轻松地感知细节以外的细节，并且会呈现单元格的掩蔽，以保留视频中的时空相关性，从而确保可以在同一空间位置观察到在同一空间位置的贴片，以便轻松地重建。此外，我们注意到，尽管部分观察到的特征可以重建语义上明确的隐形贴片，但它们无法实现准确的分类。为了解决这个问题，提出了一个桥接分类器，以弥合重建的VIT编码功能与专门用于分类的功能之间的语义差距。我们提出的MAR将VIT的计算成本降低了53％，并且广泛的实验表明，MAR始终以明显的边距优于现有的VIT模型。尤其是，我们发现由MAR训练的Vit-Lage胜过由标准培训方案训练的Vit-Bugue，这是通过说服Kinetics-400和某些v2数据集中的利润率，而VIT-LARGE的计算开销仅为14.5％。维特（Vit-Huge）。

在过去的25年中，我们目睹了机器学习在编译器领域的广泛应用。选择和相位订购问题。但是，有限的作品已在最先进的编译器（即LLVM）上游，以将前者无缝集成到编译器的优化管道中，以便由用户容易部署。 MLGO是此类项目的第一个项目之一，它仅努力使用强化学习使用基于ML的INLINER来减少二进制的代码大小。本文介绍了mlgoperf；第一个端到端框架，能够使用LLVM的ML Inliner优化性能。它采用二级ML模型来生成用于训练重新定位的增强学习代理的奖励，该辅助剂以前由MLGO用作主要模型。它通过预测分析功能的函数的速度加速来做到这一点，并为主要模型提供快速训练框架，否则将是不切实际的。实验结果表明，MLGOPERF在LLVM在O3时的优化方面的优化分别为SPEC CPU2006和CBENCH基准分别获得了1.8％和2.2％。此外，提出的方法为我们的基准测试带来了自动点守则区域的26％，可以将其转化为额外的3.7％速度值。

我们提出了一个用于图像分类的端到端可训练的功能增强模块，该模块提取和利用多视图本地功能来增强模型性能。不同于使用全球平均池（GAP）仅从全局视图中提取矢量化特征，我们建议我们采样和集成多样的多视图本地特征，以提高模型鲁棒性。为了示例班级代表性的本地功能，我们合并了一个简单的辅助分类器头（仅包含1 $ \ times $ 1卷积层），通过我们建议的Adacam（适应性的Adacam）（适应性的Adacam）（适应性的ADACAM）有效地适应了特征图的类别歧视局部区域（）。广泛的实验表明，我们的多视图功能增强模块获得了一致且明显的性能提高。