Fine-tuning pre-trained language models has become the prevalent paradigm for building downstream NLP models. Oftentimes fine-tuned models are readily available but their training data is not, due to data privacy or intellectual property concerns. This creates a barrier to fusing knowledge across individual models to yield a better single model. In this paper, we study the problem of merging individual models built on different training data sets to obtain a single model that performs well both across all data set domains and can generalize on out-of-domain data. We propose a dataless knowledge fusion method that merges models in their parameter space, guided by weights that minimize prediction differences between the merged model and the individual models. Over a battery of evaluation settings, we show that the proposed method significantly outperforms baselines such as Fisher-weighted averaging or model ensembling. Further, we find that our method is a promising alternative to multi-task learning that can preserve or sometimes improve over the individual models without access to the training data. Finally, model merging is more efficient than training a multi-task model, thus making it applicable to a wider set of scenarios.

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.

Script is a kind of structured knowledge extracted from texts, which contains a sequence of events. Based on such knowledge, script event prediction aims to predict the subsequent event. To do so, two aspects should be considered for events, namely, event description (i.e., what the events should contain) and event encoding (i.e., how they should be encoded). Most existing methods describe an event by a verb together with only a few core arguments (i.e., subject, object, and indirect object), which are not precise. In addition, existing event encoders are limited to a fixed number of arguments, which are not flexible to deal with extra information. Thus, in this paper, we propose the Rich Event Prediction (REP) framework for script event prediction. Fundamentally, it is based on the proposed rich event description, which enriches the existing ones with three kinds of important information, namely, the senses of verbs, extra semantic roles, and types of participants. REP contains an event extractor to extract such information from texts. Based on the extracted rich information, a predictor then selects the most probable subsequent event. The core component of the predictor is a transformer-based event encoder to flexibly deal with an arbitrary number of arguments. Experimental results on the widely used Gigaword Corpus show the effectiveness of the proposed framework.

In the realm of multi-modality, text-guided image retouching techniques emerged with the advent of deep learning. Most currently available text-guided methods, however, rely on object-level supervision to constrain the region that may be modified. This not only makes it more challenging to develop these algorithms, but it also limits how widely deep learning can be used for image retouching. In this paper, we offer a text-guided mask-free image retouching approach that yields consistent results to address this concern. In order to perform image retouching without mask supervision, our technique can construct plausible and edge-sharp masks based on the text for each object in the image. Extensive experiments have shown that our method can produce high-quality, accurate images based on spoken language. The source code will be released soon.

While federated learning has shown strong results in optimizing a machine learning model without direct access to the original data, its performance may be hindered by intermittent client availability which slows down the convergence and biases the final learned model. There are significant challenges to achieve both stable and bias-free training under arbitrary client availability. To address these challenges, we propose a framework named Federated Graph-based Sampling (FedGS), to stabilize the global model update and mitigate the long-term bias given arbitrary client availability simultaneously. First, we model the data correlations of clients with a Data-Distribution-Dependency Graph (3DG) that helps keep the sampled clients data apart from each other, which is theoretically shown to improve the approximation to the optimal model update. Second, constrained by the far-distance in data distribution of the sampled clients, we further minimize the variance of the numbers of times that the clients are sampled, to mitigate long-term bias. To validate the effectiveness of FedGS, we conduct experiments on three datasets under a comprehensive set of seven client availability modes. Our experimental results confirm FedGS's advantage in both enabling a fair client-sampling scheme and improving the model performance under arbitrary client availability. Our code is available at \url{https://github.com/WwZzz/FedGS}.

我们提出了EasyRec，这是一个易于使用，可扩展和高效的推荐框架，用于构建工业推荐系统。我们的EasyRec框架在以下方面是优越的：首先，EasyRec采用模块化和可插入的设计模式来减少建立定制模型的努力；其次，EasyRec实现了超参数优化和特征选择算法，以自动提高模型性能；第三，EasyRec应用在线学习，以快速适应不断变化的数据分布。该代码发布：https：//github.com/alibaba/easyrec。

本文介绍了一种新的数据增强方法，用于神经机器翻译，该方法可以在语言内部和跨语言内部实施更强的语义一致性。我们的方法基于条件掩盖语言模型（CMLM），该模型是双向的，可以在左右上下文以及标签上有条件。我们证明CMLM是生成上下文依赖性单词分布的好技术。特别是，我们表明CMLM能够通过在替换过程中对源和目标进行调节来实现语义一致性。此外，为了增强多样性，我们将软词替换的想法纳入了数据增强，该概念用词汇上的概率分布代替了一个单词。在不同量表的四个翻译数据集上进行的实验表明，总体解决方案会导致更现实的数据增强和更好的翻译质量。与最新作品相比，我们的方法始终取得了最佳性能，并且在基线上的提高了1.90个BLEU点。

手术动作三胞胎识别提供了对手术场景的更好理解。这项任务具有很高的相关性，因为它为外科医生提供了背景感知的支持和安全性。当前改善绩效的首选策略是开发新的网络机制。但是，当前最新技术的性能大大低于其他手术任务。为什么会发生这种情况？这是我们在这项工作中解决的问题。我们提出了第一项研究，以了解现有的深度学习模型通过稳健性和解释的镜头的失败。首先，我们通过对抗优化方案研究了当前的现有模型。然后，我们通过基于功能的解释提供故障模式。我们的研究对提高性能和提高可靠性的关键是核心和虚假属性。我们的工作为外科科学中更具可信赖性和可靠性的深度学习模型打开了大门。

当系统中有某些未知术语和隐藏的物理机制时，基于第一原理的复杂物理系统的管理方程可能会非常具有挑战性。在这项工作中，我们采用深度学习体系结构来学习基于从完全动力学模型中获取的数据的等离子体系统的流体部分微分方程（PDE）。证明了学到的多臂流体PDE可以融合诸如Landau阻尼等动力学效应。基于学习的流体闭合，数据驱动的多音阶流体建模可以很好地再现从完全动力学模型中得出的所有物理量。Landau阻尼的计算阻尼率与完全动力学的模拟和线性理论一致。用于复杂物理系统的PDE的数据驱动的流体建模可以应用于改善流体闭合并降低全球系统多规模建模的计算成本。

近年来，深度学习的时间序列增加了。对于时间序列的异常检测方案，例如金融，物联网，数据中心操作等，时间序列通常会根据各种外部因素显示非常灵活的基线。异常通过躺在远离基线的情况下揭示自己。但是，由于一些挑战，包括基线转换，缺乏标签，噪声干扰，流数据中的实时检测，可解释性等。从时间序列，即深基线网络（DBLN）。通过使用此深层网络，我们可以轻松地定位基线位置，然后提供可靠且可解释的异常检测结果。对合成和公共现实世界数据集的经验评估表明，我们纯粹的无监督算法与最新方法相比，实现了卓越的性能，并且具有良好的实际应用。