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.
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Current advances in recommender systems have been remarkably successful in optimizing immediate engagement. However, long-term user engagement, a more desirable performance metric, remains difficult to improve. Meanwhile, recent reinforcement learning (RL) algorithms have shown their effectiveness in a variety of long-term goal optimization tasks. For this reason, RL is widely considered as a promising framework for optimizing long-term user engagement in recommendation. Despite being a promising approach, the application of RL heavily relies on well-designed rewards, but designing rewards related to long-term user engagement is quite difficult. To mitigate the problem, we propose a novel paradigm, Preference-based Recommender systems (PrefRec), which allows RL recommender systems to learn from preferences about users' historical behaviors rather than explicitly defined rewards. Such preferences are easily accessible through techniques such as crowdsourcing, as they do not require any expert knowledge. With PrefRec, we can fully exploit the advantages of RL in optimizing long-term goals, while avoiding complex reward engineering. PrefRec uses the preferences to automatically train a reward function in an end-to-end manner. The reward function is then used to generate learning signals to train the recommendation policy. Furthermore, we design an effective optimization method for PrefRec, which uses an additional value function, expectile regression and reward model pre-training to improve the performance. Extensive experiments are conducted on a variety of long-term user engagement optimization tasks. The results show that PrefRec significantly outperforms previous state-of-the-art methods in all the tasks.
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High-dimensional linear regression model is the most popular statistical model for high-dimensional data, but it is quite a challenging task to achieve a sparse set of regression coefficients. In this paper, we propose a simple heuristic algorithm to construct sparse high-dimensional linear regression models, which is adapted from the shortest solution-guided decimation algorithm and is referred to as ASSD. This algorithm constructs the support of regression coefficients under the guidance of the least-squares solution of the recursively decimated linear equations, and it applies an early-stopping criterion and a second-stage thresholding procedure to refine this support. Our extensive numerical results demonstrate that ASSD outperforms LASSO, vector approximate message passing, and two other representative greedy algorithms in solution accuracy and robustness. ASSD is especially suitable for linear regression problems with highly correlated measurement matrices encountered in real-world applications.
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This study investigates clustered federated learning (FL), one of the formulations of FL with non-i.i.d. data, where the devices are partitioned into clusters and each cluster optimally fits its data with a localized model. We propose a novel clustered FL framework, which applies a nonconvex penalty to pairwise differences of parameters. This framework can automatically identify clusters without a priori knowledge of the number of clusters and the set of devices in each cluster. To implement the proposed framework, we develop a novel clustered FL method called FPFC. Advancing from the standard ADMM, our method is implemented in parallel, updates only a subset of devices at each communication round, and allows each participating device to perform a variable amount of work. This greatly reduces the communication cost while simultaneously preserving privacy, making it practical for FL. We also propose a new warmup strategy for hyperparameter tuning under FL settings and consider the asynchronous variant of FPFC (asyncFPFC). Theoretically, we provide convergence guarantees of FPFC for general nonconvex losses and establish the statistical convergence rate under a linear model with squared loss. Our extensive experiments demonstrate the advantages of FPFC over existing methods.
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End-to-end formulation of automatic speech recognition (ASR) and speech translation (ST) makes it easy to use a single model for both multilingual ASR and many-to-many ST. In this paper, we propose streaming language-agnostic multilingual speech recognition and translation using neural transducers (LAMASSU). To enable multilingual text generation in LAMASSU, we conduct a systematic comparison between specified and unified prediction and joint networks. We leverage a language-agnostic multilingual encoder that substantially outperforms shared encoders. To enhance LAMASSU, we propose to feed target LID to encoders. We also apply connectionist temporal classification regularization to transducer training. Experimental results show that LAMASSU not only drastically reduces the model size but also outperforms monolingual ASR and bilingual ST models.
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In this paper, we introduce our work of building a Streaming Multilingual Speech Model (SM2), which can transcribe or translate multiple spoken languages into texts of the target language. The backbone of SM2 is Transformer Transducer, which has high streaming capability. Instead of human labeled speech translation (ST) data, SM2 models are trained using weakly supervised data generated by converting the transcriptions in speech recognition corpora with a machine translation service. With 351 thousand hours of anonymized speech training data from 25 languages, SM2 models achieve comparable or even better ST quality than some recent popular large-scale non-streaming speech models. More importantly, we show that SM2 has the truly zero-shot capability when expanding to new target languages, yielding high quality ST results for {source-speech, target-text} pairs that are not seen during training.
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近年来,随着空间航天器实体的大规模部署以及卫星在板载功能的增加,在过度网络动态的情况下,与TCP/IP相比,出现了比TCP/IP更强大的通信协议。 DTN节点缓冲区管理仍然是一个活跃的研究领域,因为DTN核心协议的当前实现仍然依赖于以下假设:在不同的网络节点中始终有足够的内存来存储和正向捆绑包。此外,经典排队理论不适用于DTN节点缓冲区的动态管理。因此,本文提出了一种集中式方法,以基于高级强化学习(RL)策略优势行动者 - 批评者(A2C)自动管理低地球(LEO)卫星星座中的认知DTN节点。该方法旨在探索培训地球同步地球轨道智能代理,以管理Leo卫星星座中的所有DTN节点。 A2C代理的目的是在考虑节点内存利用率的同时最大化交付成功率并最大程度地减少网络资源消耗成本。智能代理可以根据束优先级动态调整无线电数据速率并执行下降操作。为了衡量在LEO卫星星座场景中将A2C技术应用于DTN节点管理问题的有效性,本文将受过训练的智能代理策略与其他两种非RL政策进行了比较,包括随机和标准政策。实验表明,A2C策略平衡了交付成功率和成本,并提供了最高的奖励和最低的节点存储器利用率。
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深度神经网络(DNNS)在训练过程中容易受到后门攻击的影响。该模型以这种方式损坏正常起作用,但是当输入中的某些模式触发时,会产生预定义的目标标签。现有防御通常依赖于通用后门设置的假设,其中有毒样品共享相同的均匀扳机。但是,最近的高级后门攻击表明,这种假设在动态后门中不再有效,在动态后门中,触发者因输入而异,从而击败了现有的防御。在这项工作中,我们提出了一种新颖的技术BEATRIX(通过革兰氏矩阵检测)。 BEATRIX利用革兰氏矩阵不仅捕获特征相关性,还可以捕获表示形式的适当高阶信息。通过从正常样本的激活模式中学习类条件统计,BEATRIX可以通过捕获激活模式中的异常来识别中毒样品。为了进一步提高识别目标标签的性能,BEATRIX利用基于内核的测试,而无需对表示分布进行任何先前的假设。我们通过与最先进的防御技术进行了广泛的评估和比较来证明我们的方法的有效性。实验结果表明,我们的方法在检测动态后门时达到了91.1%的F1得分,而最新技术只能达到36.9%。
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随着机器学习技术的发展,研究的注意力已从单模式学习转变为多模式学习,因为现实世界中的数据以不同的方式存在。但是,多模式模型通常比单模式模型具有更多的信息,并且通常将其应用于敏感情况,例如医疗报告生成或疾病鉴定。与针对机器学习分类器的现有会员推断相比,我们关注的是多模式模型的输入和输出的问题,例如不同的模式,例如图像字幕。这项工作通过成员推理攻击的角度研究了多模式模型的隐私泄漏,这是确定数据记录是否涉及模型培训过程的过程。为了实现这一目标,我们提出了多种模型的成员资格推理(M^4i),分别使用两种攻击方法来推断成员身份状态,分别为基于公表示的(MB)M^4i和基于特征(FB)M^4i。更具体地说,MB M^4i在攻击时采用相似性指标来推断目标数据成员资格。 FB M^4i使用预先训练的阴影多模式提取器来通过比较提取的输入和输出功能的相似性来实现数据推理攻击的目的。广泛的实验结果表明,两种攻击方法都可以实现强大的性能。在不受限制的情况下,平均可以获得攻击成功率的72.5%和94.83%。此外,我们评估了针对我们的攻击的多种防御机制。 M^4i攻击的源代码可在https://github.com/multimodalmi/multimodal-membership-inference.git上公开获得。
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预先训练的图像文本模型(如剪辑)已经证明了从大规模的Web收集的图像文本数据中学到的视觉表示的强大力量。鉴于学习良好的视觉特征,一些现有的作品将图像表示转移到视频域并取得良好的结果。但是,如何利用图像语言预训练的模型(例如,剪辑)进行视频培训(后培训)仍在探索。在本文中,我们研究了两个问题:1)阻碍后期剪辑的因素是什么因素,以进一步提高视频语言任务的性能? 2)如何减轻这些因素的影响?通过一系列比较实验和分析,我们发现语言源之间的数据量表和域间隙具有很大的影响。由这些动机,我们提出了一种配备了视频代理机制的Omnisource跨模式学习方法,即剪辑,即剪辑VIP。广泛的结果表明,我们的方法可以提高视频检索的剪辑的性能。我们的模型还可以在包括MSR-VTT,DIDEMO,LSMDC和ActivityNet在内的各种数据集上实现SOTA结果。我们在https://github.com/microsoft/xpretrain/tree/main/main/main/clip-vip上发布了代码和预训练的剪辑模型。
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