顺序推荐通过历史互动来预测用户的下一个行为。推荐更长的序列可以提高建议准确性并提高个性化程度。随着序列的延长，现有作品尚未解决以下两个主要挑战。首先，在序列长度增加时，很难对远程内部序列依赖性进行建模。其次，它需要有效的内存和计算速度。在本文中，我们提出了一个稀疏的细心内存（SAM）网络，以进行长顺序用户行为建模。 SAM支持对用户行为序列的有效培训和实时推断，其长度为数千。在SAM中，我们将目标项目建模为查询和长序列作为知识数据库，在该数据库中，前者从后者中持续传达相关信息。 SAM同时模拟了目标序列依赖性和远程内部依赖性，其复杂性和O（1）顺序更新数量，只能通过具有O（l^2）复杂性的自我注意机制来实现这一目标。广泛的经验结果表明，我们提出的解决方案不仅在长期用户行为建模中而且在短序列建模中也有效。 SAM按照长度为1000的序列实施，成功部署在最大的国际电子商务平台之一上。此推论时间在30毫秒内，在线A/B测试的点击率提高了7.30％。据我们所知，这是第一个端到端的长用户序列建模框架，它以上述效率程度对序列和目标序列依赖性进行建模，并成功地部署在大型实时工业建议上系统。

通过最大似然估计（MLE）训练的文本生成模型遭受了臭名昭著的暴露偏见问题，而生成的对抗网络（GAN）被证明具有解决方案的潜力。现有的语言gans采用估计器，例如增强或连续放松来模型单词分布。此类估计器的固有局限性导致当前模型依赖于预训练技术（MLE预训练或预训练的嵌入）。但是，由于其先前尝试的性能较差，因此很少探索没有这些局限性的代表建模方法。我们的分析表明，无效的采样方法和不健康的梯度是其不令人满意的性能的主要因素。在这项工作中，我们提出了两种解决这些问题的技术：辍学抽样和完全归一化的LSTM。基于这两种技术，我们提出了初始gan，其参数是完全初始初始初始初始初始初始初始初始化的。此外，我们引入了新的评估度量，覆盖率最少，以更好地评估生成的样品的质量。实验结果表明，Initialgan的表现都优于MLE和其他比较模型。据我们所知，这是GAN语言第一次在没有任何预训练技术的情况下胜过MLE。

为了接近不同的业务目标，在线流量塑造算法旨在改善目标项目的曝光，例如提高新商品的增长。通常，这些算法假设可以通过训练良好的转换速率预测模型访问每个用户项对的实用性。然而，对于真正的电子商务平台，有不可避免的因素阻止我们学习这种准确的模型。为了打破对实用程序的准确输入的沉重依赖，我们提出了一般的在线交通整理协议，用于在线电子商务应用程序。在我们的框架中，我们近似映射奖励得分的函数，这通常是影响排名结果的唯一方法，以对曝光和购买的数量来影响流量整形问题。具体地，我们通过在探索数据点的凸壳上构造的一类转印的线性函数近似上述功能。此外，我们将在线流量整形问题重构为线性编程，其中这些分段线性函数嵌入到目标和约束中。我们的算法可以简单地优化主要空间中的线性编程，并且其解决方案可以简单地应用于随机策略来满足所优化的目标和预期限制。最后，在线A / B测试显示我们所提出的算法稳定地优于先前的工业级流量整形算法。

Human parsing aims to partition humans in image or video into multiple pixel-level semantic parts. In the last decade, it has gained significantly increased interest in the computer vision community and has been utilized in a broad range of practical applications, from security monitoring, to social media, to visual special effects, just to name a few. Although deep learning-based human parsing solutions have made remarkable achievements, many important concepts, existing challenges, and potential research directions are still confusing. In this survey, we comprehensively review three core sub-tasks: single human parsing, multiple human parsing, and video human parsing, by introducing their respective task settings, background concepts, relevant problems and applications, representative literature, and datasets. We also present quantitative performance comparisons of the reviewed methods on benchmark datasets. Additionally, to promote sustainable development of the community, we put forward a transformer-based human parsing framework, providing a high-performance baseline for follow-up research through universal, concise, and extensible solutions. Finally, we point out a set of under-investigated open issues in this field and suggest new directions for future study. We also provide a regularly updated project page, to continuously track recent developments in this fast-advancing field: https://github.com/soeaver/awesome-human-parsing.

Supervised Deep-Learning (DL)-based reconstruction algorithms have shown state-of-the-art results for highly-undersampled dynamic Magnetic Resonance Imaging (MRI) reconstruction. However, the requirement of excessive high-quality ground-truth data hinders their applications due to the generalization problem. Recently, Implicit Neural Representation (INR) has appeared as a powerful DL-based tool for solving the inverse problem by characterizing the attributes of a signal as a continuous function of corresponding coordinates in an unsupervised manner. In this work, we proposed an INR-based method to improve dynamic MRI reconstruction from highly undersampled k-space data, which only takes spatiotemporal coordinates as inputs. Specifically, the proposed INR represents the dynamic MRI images as an implicit function and encodes them into neural networks. The weights of the network are learned from sparsely-acquired (k, t)-space data itself only, without external training datasets or prior images. Benefiting from the strong implicit continuity regularization of INR together with explicit regularization for low-rankness and sparsity, our proposed method outperforms the compared scan-specific methods at various acceleration factors. E.g., experiments on retrospective cardiac cine datasets show an improvement of 5.5 ~ 7.1 dB in PSNR for extremely high accelerations (up to 41.6-fold). The high-quality and inner continuity of the images provided by INR has great potential to further improve the spatiotemporal resolution of dynamic MRI, without the need of any training data.

The counting task, which plays a fundamental rule in numerous applications (e.g., crowd counting, traffic statistics), aims to predict the number of objects with various densities. Existing object counting tasks are designed for a single object class. However, it is inevitable to encounter newly coming data with new classes in our real world. We name this scenario as \textit{evolving object counting}. In this paper, we build the first evolving object counting dataset and propose a unified object counting network as the first attempt to address this task. The proposed model consists of two key components: a class-agnostic mask module and a class-increment module. The class-agnostic mask module learns generic object occupation prior via predicting a class-agnostic binary mask (e.g., 1 denotes there exists an object at the considering position in an image and 0 otherwise). The class-increment module is used to handle new coming classes and provides discriminative class guidance for density map prediction. The combined outputs of class-agnostic mask module and image feature extractor are used to predict the final density map. When new classes come, we first add new neural nodes into the last regression and classification layers of this module. Then, instead of retraining the model from scratch, we utilize knowledge distilling to help the model remember what have already learned about previous object classes. We also employ a support sample bank to store a small number of typical training samples of each class, which are used to prevent the model from forgetting key information of old data. With this design, our model can efficiently and effectively adapt to new coming classes while keeping good performance on already seen data without large-scale retraining. Extensive experiments on the collected dataset demonstrate the favorable performance.

In this paper, we consider an intelligent reflecting surface (IRS)-aided cell-free massive multiple-input multiple-output system, where the beamforming at access points and the phase shifts at IRSs are jointly optimized to maximize energy efficiency (EE). To solve EE maximization problem, we propose an iterative optimization algorithm by using quadratic transform and Lagrangian dual transform to find the optimum beamforming and phase shifts. However, the proposed algorithm suffers from high computational complexity, which hinders its application in some practical scenarios. Responding to this, we further propose a deep learning based approach for joint beamforming and phase shifts design. Specifically, a two-stage deep neural network is trained offline using the unsupervised learning manner, which is then deployed online for the predictions of beamforming and phase shifts. Simulation results show that compared with the iterative optimization algorithm and the genetic algorithm, the unsupervised learning based approach has higher EE performance and lower running time.

With the ever-growing model size and the limited availability of labeled training data, transfer learning has become an increasingly popular approach in many science and engineering domains. For classification problems, this work delves into the mystery of transfer learning through an intriguing phenomenon termed neural collapse (NC), where the last-layer features and classifiers of learned deep networks satisfy: (i) the within-class variability of the features collapses to zero, and (ii) the between-class feature means are maximally and equally separated. Through the lens of NC, our findings for transfer learning are the following: (i) when pre-training models, preventing intra-class variability collapse (to a certain extent) better preserves the intrinsic structures of the input data, so that it leads to better model transferability; (ii) when fine-tuning models on downstream tasks, obtaining features with more NC on downstream data results in better test accuracy on the given task. The above results not only demystify many widely used heuristics in model pre-training (e.g., data augmentation, projection head, self-supervised learning), but also leads to more efficient and principled fine-tuning method on downstream tasks that we demonstrate through extensive experimental results.

Recent work has shown that fine-tuning large pre-trained language models on a collection of tasks described via instructions, a.k.a. instruction-tuning, improves their zero and few-shot generalization to unseen tasks. However, there is a limited understanding of the performance trade-offs of different decisions made during the instruction-tuning process. These decisions include the scale and diversity of the instruction-tuning benchmark, different task sampling strategies, fine-tuning with and without demonstrations, training using specialized datasets for reasoning and dialogue, and finally, the fine-tuning objectives themselves. In this paper, we characterize the effect of instruction-tuning decisions on downstream task performance when scaling both model and benchmark sizes. To this end, we create OPT-IML Bench: a large benchmark for Instruction Meta-Learning (IML) of 2000 NLP tasks consolidated into task categories from 8 existing benchmarks, and prepare an evaluation framework to measure three types of model generalizations: to tasks from fully held-out categories, to held-out tasks from seen categories, and to held-out instances from seen tasks. Through the lens of this framework, we first present insights about instruction-tuning decisions as applied to OPT-30B and further exploit these insights to train OPT-IML 30B and 175B, which are instruction-tuned versions of OPT. OPT-IML demonstrates all three generalization abilities at both scales on four different evaluation benchmarks with diverse tasks and input formats -- PromptSource, FLAN, Super-NaturalInstructions, and UnifiedSKG. Not only does it significantly outperform OPT on all benchmarks but is also highly competitive with existing models fine-tuned on each specific benchmark. We release OPT-IML at both scales, together with the OPT-IML Bench evaluation framework.

Time series anomaly detection strives to uncover potential abnormal behaviors and patterns from temporal data, and has fundamental significance in diverse application scenarios. Constructing an effective detection model usually requires adequate training data stored in a centralized manner, however, this requirement sometimes could not be satisfied in realistic scenarios. As a prevailing approach to address the above problem, federated learning has demonstrated its power to cooperate with the distributed data available while protecting the privacy of data providers. However, it is still unclear that how existing time series anomaly detection algorithms perform with decentralized data storage and privacy protection through federated learning. To study this, we conduct a federated time series anomaly detection benchmark, named FedTADBench, which involves five representative time series anomaly detection algorithms and four popular federated learning methods. We would like to answer the following questions: (1)How is the performance of time series anomaly detection algorithms when meeting federated learning? (2) Which federated learning method is the most appropriate one for time series anomaly detection? (3) How do federated time series anomaly detection approaches perform on different partitions of data in clients? Numbers of results as well as corresponding analysis are provided from extensive experiments with various settings. The source code of our benchmark is publicly available at https://github.com/fanxingliu2020/FedTADBench.