近年来，深度神经网络在各种应用领域中都有广泛的成功。但是，它们需要重要的计算和内存资源，严重阻碍其部署，特别是在移动设备上或实时应用程序。神经网络通常涉及大量参数，该参数对应于网络的权重。在培训过程中获得的这种参数是用于网络性能的决定因素。但是，它们也非常冗余。修剪方法尤其试图通过识别和移除不相关的重量来减小参数集的大小。在本文中，我们研究了培训策略对修剪效率的影响。考虑和比较了两种培训方式：（1）微调和（2）从头开始。在四个数据集（CIFAR10，CiFAR100，SVHN和CALTECH101）上获得的实验结果和两个不同的CNNS（VGG16和MOBILENET）证明已经在大语料库（例如想象成）上预先培训的网络，然后进行微调特定数据集可以更有效地修剪（高达80％的参数减少），而不是从头开始培训的相同网络。

在神经网络中引入稀疏性是一种有效的方法，可以降低其复杂性，同时保持其性能几乎完好无损。在大多数情况下，使用三阶段管道引入稀疏性：1）训练模型以收敛，2）根据某些标准修剪模型，3）微调修剪模型以恢复性能。最后两个步骤通常是迭代执行的，从而导致合理的结果，但也取得了耗时且复杂的过程。在我们的工作中，我们建议摆脱管道的第一步，并在单个修剪训练周期中结合其他两个步骤，从而使模型在修剪时共同学习最佳权重。我们通过介绍一个名为One Cycle Pruning的小说修剪时间表来做到这一点，该时间表从培训开始就开始修剪，直到最后。采用这样的时间表不仅可以更好地执行修剪模型，而且还大大降低了修剪模型所需的培训预算。实验是在多种架构（VGG-16和RESNET-18）和数据集（CIFAR-10，CIFAR-100和CALTECH-101）上进行的，以及相对较高的稀疏值（80％，90％，95％的权重，删除）。我们的结果表明，按固定的培训预算，一环修剪始终优于通常使用的修剪时间表，例如单发修剪，迭代修剪和自动化逐渐修剪。

在本文中，我们提出了一种新的端到端方法，以优化能量性能以及大型建筑物的舒适性和空气质量，而无需任何装修工作。我们介绍了基于经常性神经网络的元模型，并训练了使用从模拟程序采样的数据库预测一般大类建筑物的行为。然后将该元模型部署在不同的框架中，并且使用两个真实建筑的特定数据校准其参数。通过使用CMA-ES算法比较从传感器获得的真实数据的比较来估计参数，通过使用CMA-ES算法，衍生免费优化过程。然后，使用NSGA-II多目标优化过程保持目标热舒适度和空气质量的同时优化能量消耗。数值实验说明了该元模型如何确保能效显着增益，高达近10％，同时计算比数值模型更具吸引力，并且足够灵活地适应若干类型的建筑物。

Although Reinforcement Learning (RL) has shown impressive results in games and simulation, real-world application of RL suffers from its instability under changing environment conditions and hyperparameters. We give a first impression of the extent of this instability by showing that the hyperparameters found by automatic hyperparameter optimization (HPO) methods are not only dependent on the problem at hand, but even on how well the state describes the environment dynamics. Specifically, we show that agents in contextual RL require different hyperparameters if they are shown how environmental factors change. In addition, finding adequate hyperparameter configurations is not equally easy for both settings, further highlighting the need for research into how hyperparameters influence learning and generalization in RL.

Active learning as a paradigm in deep learning is especially important in applications involving intricate perception tasks such as object detection where labels are difficult and expensive to acquire. Development of active learning methods in such fields is highly computationally expensive and time consuming which obstructs the progression of research and leads to a lack of comparability between methods. In this work, we propose and investigate a sandbox setup for rapid development and transparent evaluation of active learning in deep object detection. Our experiments with commonly used configurations of datasets and detection architectures found in the literature show that results obtained in our sandbox environment are representative of results on standard configurations. The total compute time to obtain results and assess the learning behavior can thereby be reduced by factors of up to 14 when comparing with Pascal VOC and up to 32 when comparing with BDD100k. This allows for testing and evaluating data acquisition and labeling strategies in under half a day and contributes to the transparency and development speed in the field of active learning for object detection.

Earthquakes, fire, and floods often cause structural collapses of buildings. The inspection of damaged buildings poses a high risk for emergency forces or is even impossible, though. We present three recent selected missions of the Robotics Task Force of the German Rescue Robotics Center, where both ground and aerial robots were used to explore destroyed buildings. We describe and reflect the missions as well as the lessons learned that have resulted from them. In order to make robots from research laboratories fit for real operations, realistic test environments were set up for outdoor and indoor use and tested in regular exercises by researchers and emergency forces. Based on this experience, the robots and their control software were significantly improved. Furthermore, top teams of researchers and first responders were formed, each with realistic assessments of the operational and practical suitability of robotic systems.

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.

We study inductive matrix completion (matrix completion with side information) under an i.i.d. subgaussian noise assumption at a low noise regime, with uniform sampling of the entries. We obtain for the first time generalization bounds with the following three properties: (1) they scale like the standard deviation of the noise and in particular approach zero in the exact recovery case; (2) even in the presence of noise, they converge to zero when the sample size approaches infinity; and (3) for a fixed dimension of the side information, they only have a logarithmic dependence on the size of the matrix. Differently from many works in approximate recovery, we present results both for bounded Lipschitz losses and for the absolute loss, with the latter relying on Talagrand-type inequalities. The proofs create a bridge between two approaches to the theoretical analysis of matrix completion, since they consist in a combination of techniques from both the exact recovery literature and the approximate recovery literature.

Metric learning aims to learn distances from the data, which enhances the performance of similarity-based algorithms. An author style detection task is a metric learning problem, where learning style features with small intra-class variations and larger inter-class differences is of great importance to achieve better performance. Recently, metric learning based on softmax loss has been used successfully for style detection. While softmax loss can produce separable representations, its discriminative power is relatively poor. In this work, we propose NBC-Softmax, a contrastive loss based clustering technique for softmax loss, which is more intuitive and able to achieve superior performance. Our technique meets the criterion for larger number of samples, thus achieving block contrastiveness, which is proven to outperform pair-wise losses. It uses mini-batch sampling effectively and is scalable. Experiments on 4 darkweb social forums, with NBCSAuthor that uses the proposed NBC-Softmax for author and sybil detection, shows that our negative block contrastive approach constantly outperforms state-of-the-art methods using the same network architecture. Our code is publicly available at : https://github.com/gayanku/NBC-Softmax

We pose video object segmentation as spectral graph clustering in space and time, with one graph node for each pixel and edges forming local space-time neighborhoods. We claim that the strongest cluster in this video graph represents the salient object. We start by introducing a novel and efficient method based on 3D filtering for approximating the spectral solution, as the principal eigenvector of the graph's adjacency matrix, without explicitly building the matrix. This key property allows us to have a fast parallel implementation on GPU, orders of magnitude faster than classical approaches for computing the eigenvector. Our motivation for a spectral space-time clustering approach, unique in video semantic segmentation literature, is that such clustering is dedicated to preserving object consistency over time, which we evaluate using our novel segmentation consistency measure. Further on, we show how to efficiently learn the solution over multiple input feature channels. Finally, we extend the formulation of our approach beyond the segmentation task, into the realm of object tracking. In extensive experiments we show significant improvements over top methods, as well as over powerful ensembles that combine them, achieving state-of-the-art on multiple benchmarks, both for tracking and segmentation.