Personalization in Federated Learning (FL) aims to modify a collaboratively trained global model according to each client. Current approaches to personalization in FL are at a coarse granularity, i.e. all the input instances of a client use the same personalized model. This ignores the fact that some instances are more accurately handled by the global model due to better generalizability. To address this challenge, this work proposes Flow, a fine-grained stateless personalized FL approach. Flow creates dynamic personalized models by learning a routing mechanism that determines whether an input instance prefers the local parameters or its global counterpart. Thus, Flow introduces per-instance routing in addition to leveraging per-client personalization to improve accuracies at each client. Further, Flow is stateless which makes it unnecessary for a client to retain its personalized state across FL rounds. This makes Flow practical for large-scale FL settings and friendly to newly joined clients. Evaluations on Stackoverflow, Reddit, and EMNIST datasets demonstrate the superiority in prediction accuracy of Flow over state-of-the-art non-personalized and only per-client personalized approaches to FL.

作为自然语言处理领域的后起之秀，在各行各业中，问答系统（问答系统）被广泛使用。与其他方案相比，在Q＆A系统的可追溯性和解释性方面，财务方案的应用程序有很强的要求。此外，由于对人工智能技术的需求已从最初的计算智能转变为认知智能，因此这项研究主要集中于财务数值推理数据集-FinQA。在共享任务中，目标是根据包含文本和表的给定财务报告生成推理程序和最终答案。我们使用基于Deberta预训练的语言模型的方法，并采用其他优化方法，包括在此基础上进行多模型融合，训练集组合。我们最终获得了68.99的执行精度和64.53的程序精度，在2022 FinQA挑战中排名第4。

基于图形神经网络（GNN）的子图表学习在科学进步中表现出广泛的应用，例如对分子结构 - 特质关系和集体细胞功能的预测。特别是，图表增强技术在改善基于图和基于节点的分类任务方面显示出令人鼓舞的结果。尽管如此，在现有的基于GNN的子图表示学习研究中很少探索它们。在这项研究中，我们开发了一种新型的多视图增强机制，以改善子图表示学习模型，从而改善下游预测任务的准确性。我们的增强技术创建了多种子图的变体，并将这些变体嵌入原始图中，以实现高度改善的训练效率，可伸缩性和准确性。几个现实世界和生理数据集的基准实验证明了我们提出的多视图增强技术在子图表学习中的优越性。

本文调查了从紧凑型代表和存储训练参数的角度来看深神经网络（DNN）压缩。我们探讨了用于DNN参数的跨层架构 - 不可知表示共享的先前被忽视的机会。为此，我们从DNN架构中解耦了前馈参数并利用添加量量化，用于图像描述符的极端损耗压缩方法，以紧凑地表示参数。然后，在任务目标上是Fineetune的，以提高任务准确性。我们对MobileNet-V2，VGG-11，Reset-50进行了广泛的实验，具有用于分类，检测和分割任务的修剪培训的Pruned DNN。概念上简单的方案始终如一地优于迭代非结构化修剪。在ILSVRC12分类挑战上以76.1％的高精度应用于Reset-50，它实现了7.2美元的价格，没有准确性损失和15.3美元的准确度。进一步的分析表明，在网络层中可能经常发生表示共享，并且整个DNN的学习共享表示可以以与多个单独的部分压缩模型相同的压缩比以相同的压缩比实现更好的精度。我们释放Pytorch码以促进资源受限设备上的DNN部署，并对DNN参数的有效表示和存储的未来研究。

培训广泛和深度神经网络（DNN）需要大量的存储资源，例如内存，因为在转发传播期间必须在存储器中保存中间激活数据，然后恢复以便向后传播。然而，由于硬件设计约束，诸如GPU之类的最先进的加速器（例如GPU）仅配备了非常有限的存储容量，这显着限制了在训练大规模DNN时的最大批量大小和性能加速。传统的记忆保存技术均受性能开销或受限互连带宽或特定互连技术的约束。在本文中，我们提出了一种新颖的记忆高效的CNN训练框架（称为Comet），利用错误界限的损耗压缩来显着降低训练的内存要求，以允许培训更大的模型或加速培训。不同于采用基于图像的有损压缩机（例如JPEG）的最先进的解决方案来压缩激活数据，我们的框架故意采用严格的错误控制机制来采用错误界限的损耗压缩。具体而言，我们对从改变的激活数据传播到梯度的压缩误差传播的理论分析，并经验探讨改变梯度对训练过程的影响。基于这些分析，我们优化了误报的损耗压缩，并提出了一种用于激活数据压缩的自适应误差控制方案。我们评估我们对最先进的解决方案的设计，其中包含五个广泛采用的CNN和Imagenet DataSet。实验表明，我们所提出的框架可以在基线训练中显着降低13.5倍，并分别在另一个最先进的基于压缩框架上的1.8倍，几乎没有准确性损失。

多任务学习（MTL）通过在任务之间共享参数共同学习一组任务。这是降低存储成本的一种有希望的方法，同时提高许多计算机视觉任务的任务准确性。 MTL的有效采用面临两个主要挑战。第一个挑战是确定在任务中共享哪些参数，以优化内存效率和任务准确性。第二个挑战是在不需要耗时的手动重新实现和重要的域专业知识的情况下自动将MTL算法应用于任意CNN主链。本文通过开发第一个编程框架AutoMTL来应对挑战，该框架自动化有效的MTL模型开发为视觉任务。 AUTOMTL作为输入作为任意的骨干卷积神经网络（CNN）以及一组学习的任务，并自动生成一个多任务模型，该模型同时实现了高精度和较小的记忆足迹。在三个流行的MTL基准测试（CityScapes，NYUV2，Tiny-Taskonomy）上进行的实验证明了AutoMTL对最先进方法的有效性以及在CNN跨CNN的AutoMTL的普遍性。 AutOmtl是开源的，可在https://github.com/zhanglijun95/automtl上找到。

In this paper we explore the task of modeling (semi) structured object sequences; in particular we focus our attention on the problem of developing a structure-aware input representation for such sequences. In such sequences, we assume that each structured object is represented by a set of key-value pairs which encode the attributes of the structured object. Given a universe of keys, a sequence of structured objects can then be viewed as an evolution of the values for each key, over time. We encode and construct a sequential representation using the values for a particular key (Temporal Value Modeling - TVM) and then self-attend over the set of key-conditioned value sequences to a create a representation of the structured object sequence (Key Aggregation - KA). We pre-train and fine-tune the two components independently and present an innovative training schedule that interleaves the training of both modules with shared attention heads. We find that this iterative two part-training results in better performance than a unified network with hierarchical encoding as well as over, other methods that use a {\em record-view} representation of the sequence \cite{de2021transformers4rec} or a simple {\em flattened} representation of the sequence. We conduct experiments using real-world data to demonstrate the advantage of interleaving TVM-KA on multiple tasks and detailed ablation studies motivating our modeling choices. We find that our approach performs better than flattening sequence objects and also allows us to operate on significantly larger sequences than existing methods.

Representing and synthesizing novel views in real-world dynamic scenes from casual monocular videos is a long-standing problem. Existing solutions typically approach dynamic scenes by applying geometry techniques or utilizing temporal information between several adjacent frames without considering the underlying background distribution in the entire scene or the transmittance over the ray dimension, limiting their performance on static and occlusion areas. Our approach $\textbf{D}$istribution-$\textbf{D}$riven neural radiance fields offers high-quality view synthesis and a 3D solution to $\textbf{D}$etach the background from the entire $\textbf{D}$ynamic scene, which is called $\text{D}^4$NeRF. Specifically, it employs a neural representation to capture the scene distribution in the static background and a 6D-input NeRF to represent dynamic objects, respectively. Each ray sample is given an additional occlusion weight to indicate the transmittance lying in the static and dynamic components. We evaluate $\text{D}^4$NeRF on public dynamic scenes and our urban driving scenes acquired from an autonomous-driving dataset. Extensive experiments demonstrate that our approach outperforms previous methods in rendering texture details and motion areas while also producing a clean static background. Our code will be released at https://github.com/Luciferbobo/D4NeRF.

Deploying reliable deep learning techniques in interdisciplinary applications needs learned models to output accurate and ({even more importantly}) explainable predictions. Existing approaches typically explicate network outputs in a post-hoc fashion, under an implicit assumption that faithful explanations come from accurate predictions/classifications. We have an opposite claim that explanations boost (or even determine) classification. That is, end-to-end learning of explanation factors to augment discriminative representation extraction could be a more intuitive strategy to inversely assure fine-grained explainability, e.g., in those neuroimaging and neuroscience studies with high-dimensional data containing noisy, redundant, and task-irrelevant information. In this paper, we propose such an explainable geometric deep network dubbed as NeuroExplainer, with applications to uncover altered infant cortical development patterns associated with preterm birth. Given fundamental cortical attributes as network input, our NeuroExplainer adopts a hierarchical attention-decoding framework to learn fine-grained attentions and respective discriminative representations to accurately recognize preterm infants from term-born infants at term-equivalent age. NeuroExplainer learns the hierarchical attention-decoding modules under subject-level weak supervision coupled with targeted regularizers deduced from domain knowledge regarding brain development. These prior-guided constraints implicitly maximizes the explainability metrics (i.e., fidelity, sparsity, and stability) in network training, driving the learned network to output detailed explanations and accurate classifications. Experimental results on the public dHCP benchmark suggest that NeuroExplainer led to quantitatively reliable explanation results that are qualitatively consistent with representative neuroimaging studies.

The traditional statistical inference is static, in the sense that the estimate of the quantity of interest does not affect the future evolution of the quantity. In some sequential estimation problems however, the future values of the quantity to be estimated depend on the estimate of its current value. This type of estimation problems has been formulated as the dynamic inference problem. In this work, we formulate the Bayesian learning problem for dynamic inference, where the unknown quantity-generation model is assumed to be randomly drawn according to a random model parameter. We derive the optimal Bayesian learning rules, both offline and online, to minimize the inference loss. Moreover, learning for dynamic inference can serve as a meta problem, such that all familiar machine learning problems, including supervised learning, imitation learning and reinforcement learning, can be cast as its special cases or variants. Gaining a good understanding of this unifying meta problem thus sheds light on a broad spectrum of machine learning problems as well.