本文研究了一个新的在线学习问题，其中包含双流式数据，其中数据流是通过不断发展的特征空间来描述的，新的功能出现了，旧功能逐渐消失。这个问题的挑战是两个折叠：1）随着时间的推移，数据样本不断流动，可能会随着时间的推移而随着时间的流逝而携带移动的模式，因此学习者可以随时更新。 2）很少的样本描述了新出现的特征，从而导致较弱的学习者倾向于做出错误预测。克服挑战的一个合理的想法是在前进的特征空间之间建立关系，以便在线学习者可以利用从旧功能中学到的知识来改善新功能的学习性能。不幸的是，这个想法并没有扩展到具有复杂功能相互作用的高维媒体流，这在善于跨性（偏见的浅学习者）和表现力（需要深度学习者）之间的权衡受到了折衷。在此激励的情况下，我们提出了一种新颖的旧^3S范式，其中发现了一个共享的潜在子空间来总结旧功能空间中的信息，从而构建了中间功能映射关系。旧^3S的关键特征是将模型容量视为可学习的语义，根据在线方式以输入数据流的复杂性和非线性，共同产生最佳模型深度和参数。理论分析和实证研究都证实了我们提议的生存能力和有效性。

很少有语义细分旨在识别一个看不见类别的对象区域，只有几个带注释的示例作为监督。几次分割的关键是在支持图像和查询图像之间建立牢固的语义关系，并防止过度拟合。在本文中，我们提出了一个有效的多相似性超关联网络（MSHNET），以解决几个射击语义分割问题。在MSHNET中，我们提出了一种新的生成原型相似性（GPS），与余弦相似性可以在支持图像和查询图像之间建立牢固的语义关系。基于全局特征的本地生成的原型相似性在逻辑上与基于本地特征的全局余弦相似性互补，并且可以通过同时使用两个相似性来更全面地表达查询图像和受支持图像之间的关系。此外，我们提出了MSHNET中的对称合并块（SMB），以有效合并多层，多弹射和多相似性超相关特征。 MSHNET是基于相似性而不是特定类别特征而构建的，这些特征可以实现更一般的统一性并有效地减少过度拟合。在两个基准的语义分割数据集Pascal-5i和Coco-20i上，MSHNET在1次和5次语义分段任务上实现了新的最先进的表演。

我们将受约束的线性数据特征映射模型提出作为使用卷积神经网络（CNN）的图像分类的可解释数学模型。从这个角度来看，我们建立了线性系统的传统迭代方案与Reset-and Mgnet型模型的基本块体系结构之间的详细连接。使用这些连接，我们介绍了一些修改的Reset模型，与原始模型相比具有更少的参数，但可以产生更准确的结果，从而展示该受约束的学习数据特征映射假设的有效性。基于此假设，我们进一步提出了一般的数据特征迭代方案来展示MGNet的合理性。我们还对MGNet提供系统的数值研究，以显示其在图像分类问题中的成功和优势，并展示其与已建立的网络相比的优点。

基于深度学习的模型占主导地位的生产推荐系统的当前景观。此外，近年来目睹了模型规模的指数增长 - 从谷歌的2016年模型，最新的Facebook的型号有10亿个参数，具有12万亿参数。型号容量的每次跳跃都有显着的质量增强，这使我们相信100万亿参数的时代即将来临。然而，即使在工业规模数据中心内，这些模型的培训也在挑战。这种困难是从训练计算的惊人的异质性继承 - 模型的嵌入层可以包括总模型尺寸的99.99％，这是极其内存密集的;虽然其余的神经网络越来越多地计算密集型。为支持培训此类巨大模式，迫切需要有效的分布式培训系统。在本文中，我们通过仔细共同设计优化算法和分布式系统架构来解决这一挑战。具体而言，为了确保培训效率和训练精度，我们设计一种新型混合训练算法，其中嵌入层和密集的神经网络由不同的同步机制处理;然后，我们构建一个名为Persia的系统（短暂的并行推荐培训系统，其中包含混合加速），以支持这种混合培训算法。理论上的示范和实证研究均达到100万亿参数，以证明了波斯的系统设计和实施。我们将Pensia公开使用（在https://github.com/persiamml/persia），以便任何人都能够以100万亿参数的规模轻松培训推荐模型。

Designing experiments often requires balancing between learning about the true treatment effects and earning from allocating more samples to the superior treatment. While optimal algorithms for the Multi-Armed Bandit Problem (MABP) provide allocation policies that optimally balance learning and earning, they tend to be computationally expensive. The Gittins Index (GI) is a solution to the MABP that can simultaneously attain optimality and computationally efficiency goals, and it has been recently used in experiments with Bernoulli and Gaussian rewards. For the first time, we present a modification of the GI rule that can be used in experiments with exponentially-distributed rewards. We report its performance in simulated 2- armed and 3-armed experiments. Compared to traditional non-adaptive designs, our novel GI modified design shows operating characteristics comparable in learning (e.g. statistical power) but substantially better in earning (e.g. direct benefits). This illustrates the potential that designs using a GI approach to allocate participants have to improve participant benefits, increase efficiencies, and reduce experimental costs in adaptive multi-armed experiments with exponential rewards.

Transformer has achieved impressive successes for various computer vision tasks. However, most of existing studies require to pretrain the Transformer backbone on a large-scale labeled dataset (e.g., ImageNet) for achieving satisfactory performance, which is usually unavailable for medical images. Additionally, due to the gap between medical and natural images, the improvement generated by the ImageNet pretrained weights significantly degrades while transferring the weights to medical image processing tasks. In this paper, we propose Bootstrap Own Latent of Transformer (BOLT), a self-supervised learning approach specifically for medical image classification with the Transformer backbone. Our BOLT consists of two networks, namely online and target branches, for self-supervised representation learning. Concretely, the online network is trained to predict the target network representation of the same patch embedding tokens with a different perturbation. To maximally excavate the impact of Transformer from limited medical data, we propose an auxiliary difficulty ranking task. The Transformer is enforced to identify which branch (i.e., online/target) is processing the more difficult perturbed tokens. Overall, the Transformer endeavours itself to distill the transformation-invariant features from the perturbed tokens to simultaneously achieve difficulty measurement and maintain the consistency of self-supervised representations. The proposed BOLT is evaluated on three medical image processing tasks, i.e., skin lesion classification, knee fatigue fracture grading and diabetic retinopathy grading. The experimental results validate the superiority of our BOLT for medical image classification, compared to ImageNet pretrained weights and state-of-the-art self-supervised learning approaches.

Text clustering and topic extraction are two important tasks in text mining. Usually, these two tasks are performed separately. For topic extraction to facilitate clustering, we can first project texts into a topic space and then perform a clustering algorithm to obtain clusters. To promote topic extraction by clustering, we can first obtain clusters with a clustering algorithm and then extract cluster-specific topics. However, this naive strategy ignores the fact that text clustering and topic extraction are strongly correlated and follow a chicken-and-egg relationship. Performing them separately fails to make them mutually benefit each other to achieve the best overall performance. In this paper, we propose an unsupervised text clustering and topic extraction framework (ClusTop) which integrates text clustering and topic extraction into a unified framework and can achieve high-quality clustering result and extract topics from each cluster simultaneously. Our framework includes four components: enhanced language model training, dimensionality reduction, clustering and topic extraction, where the enhanced language model can be viewed as a bridge between clustering and topic extraction. On one hand, it provides text embeddings with a strong cluster structure which facilitates effective text clustering; on the other hand, it pays high attention on the topic related words for topic extraction because of its self-attention architecture. Moreover, the training of enhanced language model is unsupervised. Experiments on two datasets demonstrate the effectiveness of our framework and provide benchmarks for different model combinations in this framework.

This paper illustrates the technologies of user next intent prediction with a concept knowledge graph. The system has been deployed on the Web at Alipay, serving more than 100 million daily active users. Specifically, we propose AlipayKG to explicitly characterize user intent, which is an offline concept knowledge graph in the Life-Service domain modeling the historical behaviors of users, the rich content interacted by users and the relations between them. We further introduce a Transformer-based model which integrates expert rules from the knowledge graph to infer the online user's next intent. Experimental results demonstrate that the proposed system can effectively enhance the performance of the downstream tasks while retaining explainability.

Capturing feature information effectively is of great importance in vision tasks. With the development of convolutional neural networks (CNNs), concepts like residual connection and multiple scales promote continual performance gains on diverse deep learning vision tasks. However, the existing methods do not organically combined advantages of these valid ideas. In this paper, we propose a novel CNN architecture called GoogLe2Net, it consists of residual feature-reutilization inceptions (ResFRI) or split residual feature-reutilization inceptions (Split-ResFRI) which create transverse passages between adjacent groups of convolutional layers to enable features flow to latter processing branches and possess residual connections to better process information. Our GoogLe2Net is able to reutilize information captured by foregoing groups of convolutional layers and express multi-scale features at a fine-grained level, which improves performances in image classification. And the inception we proposed could be embedded into inception-like networks directly without any migration costs. Moreover, in experiments based on popular vision datasets, such as CIFAR10 (97.94%), CIFAR100 (85.91%) and Tiny Imagenet (70.54%), we obtain better results on image classification task compared with other modern models.

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.