在线巨魔增加了社会成本，并对个人造成心理损害。随着自动化帐户利用机器人进行拖钓的扩散，目标个人用户很难在定量和定性上处理这种情况。为了解决这个问题，我们专注于自动化对抗巨魔的方法，因为对战斗巨魔的反应鼓励社区用户在不损害言论自由的情况下保持持续的讨论。为此，我们为自动反响应生成提出了一个新颖的数据集。特别是，我们构建了一个配对数据集，其中包括巨魔评论和使用标记的响应策略的反响应，该策略使我们的数据集中的模型可以通过根据指定策略改变反响应来生成响应。我们执行了三个任务来评估数据集的有效性，并通过自动和人类评估评估结果。在人类评估中，我们证明了数据集中微调的模型显示出策略控制的句子生成的性能有了显着改善。

电子商务搜索的关键是如何最好地利用大型但嘈杂的日志数据。在本文中，我们在Instacart介绍了基于嵌入的杂货搜索模型。该系统通过基于两个塔式变压器的编码器体系结构学习查询和产品表示。为了解决冷门问题，我们专注于基于内容的功能。为了在嘈杂的数据上有效地培训模型，我们提出了一种自我分歧学习方法和级联培训方法。Accon是一个离线人类评估数据集，我们在召回@20方面取得了10％的相对改善，对于在线A/B测试，我们每次搜索（CAPS）获得4.1％的Cart-Addds（CAPS）和1.5％的总商品价值（GMV）改进。我们描述了如何训练和部署基于嵌入的搜索模型，并对我们方法的有效性进行详细分析。

复杂物理动态的建模和控制在真实问题中是必不可少的。我们提出了一种新颖的框架，通常适用于通过用特殊校正器引入PDE解决方案操作员的代理模型来解决PDE受约束的最佳控制问题。所提出的框架的过程分为两个阶段：解决PDE约束（阶段1）的解决方案操作员学习并搜索最佳控制（阶段2）。一旦替代模型在阶段1训练，就可以在没有密集计算的阶段2中推断出最佳控制。我们的框架可以应用于数据驱动和数据的案例。我们展示了我们对不同控制变量的各种最优控制问题的成功应用，从泊松方程到汉堡方程的不同PDE约束。

虽然在实验性神经科学的功能中的最经典研究专注于个体神经元的编码性质，但录音技术的最新发展导致了对神经群体的动态产生了越来越重视。这使得各种模型用于分析与实验变量相关的人口活动，但是对许多神经人群假设的直接测试需要基于当前神经状态的系统中干预，所以能够在线推断神经状态的模型。现有方法主要基于动态系统，需要强大的参数假设，这些假设很容易侵犯在噪声主导的方案中，并且在现代实验中的数千个数据信道中不符号。为了解决这个问题，我们提出了一种方法，该方法结合快速，稳定的维度降低，通过产生的神经歧管的软平衡，允许动态近似作为瓦片之间的概率流动。这种方法可以有效地使用在线期望最大化，缩放到数万条块，并且当动态噪声主导或具有多模模式过渡概率时，现有方法优于现有方法。由此产生的模型可以接受千赫兹数据速率培训，在分钟内产生准确的神经动力学近似，并在亚倍二十四个时间尺度产生预测。它在许多时间步骤中保留了预测性能，进入了未来，并且足以作为闭环因果实验的组成部分。

有源推断可以被定义为具有生物可粘合模型的脑的贝叶斯建模。其主要思想依赖于自由能原理和药剂的优先偏好。代理人将选择一个导致其前后偏好的行动，以便将来的观察结果。在本文中，我们声称可以使用强化学习（RL）算法来解释有源推断，并在它们之间找到理论连接。我们扩展了预期的自由能量（EFE）的概念，这是有源推理的核心量，并要求EFE可以被视为负值函数。通过前后偏好的概念和理论连接的概念，我们提出了一种简单但新的方法来学习从专家的先前偏好。这说明可以通过有源推断的新视角来接近逆R1的问题。先前偏好学习的实验结果表明，基于EFE的奖励和应用于反向RL问题的可能性。

Graph Neural Networks (GNNs) have shown satisfying performance on various graph learning tasks. To achieve better fitting capability, most GNNs are with a large number of parameters, which makes these GNNs computationally expensive. Therefore, it is difficult to deploy them onto edge devices with scarce computational resources, e.g., mobile phones and wearable smart devices. Knowledge Distillation (KD) is a common solution to compress GNNs, where a light-weighted model (i.e., the student model) is encouraged to mimic the behavior of a computationally expensive GNN (i.e., the teacher GNN model). Nevertheless, most existing GNN-based KD methods lack fairness consideration. As a consequence, the student model usually inherits and even exaggerates the bias from the teacher GNN. To handle such a problem, we take initial steps towards fair knowledge distillation for GNNs. Specifically, we first formulate a novel problem of fair knowledge distillation for GNN-based teacher-student frameworks. Then we propose a principled framework named RELIANT to mitigate the bias exhibited by the student model. Notably, the design of RELIANT is decoupled from any specific teacher and student model structures, and thus can be easily adapted to various GNN-based KD frameworks. We perform extensive experiments on multiple real-world datasets, which corroborates that RELIANT achieves less biased GNN knowledge distillation while maintaining high prediction utility.

Optical coherence tomography (OCT) captures cross-sectional data and is used for the screening, monitoring, and treatment planning of retinal diseases. Technological developments to increase the speed of acquisition often results in systems with a narrower spectral bandwidth, and hence a lower axial resolution. Traditionally, image-processing-based techniques have been utilized to reconstruct subsampled OCT data and more recently, deep-learning-based methods have been explored. In this study, we simulate reduced axial scan (A-scan) resolution by Gaussian windowing in the spectral domain and investigate the use of a learning-based approach for image feature reconstruction. In anticipation of the reduced resolution that accompanies wide-field OCT systems, we build upon super-resolution techniques to explore methods to better aid clinicians in their decision-making to improve patient outcomes, by reconstructing lost features using a pixel-to-pixel approach with an altered super-resolution generative adversarial network (SRGAN) architecture.

With the development of technology and sharing economy, Airbnb as a famous short-term rental platform, has become the first choice for many young people to select. The issue of Airbnb's pricing has always been a problem worth studying. While the previous studies achieve promising results, there are exists deficiencies to solve. Such as, (1) the feature attributes of rental are not rich enough; (2) the research on rental text information is not deep enough; (3) there are few studies on predicting the rental price combined with the point of interest(POI) around the house. To address the above challenges, we proposes a multi-source information embedding(MSIE) model to predict the rental price of Airbnb. Specifically, we first selects the statistical feature to embed the original rental data. Secondly, we generates the word feature vector and emotional score combination of three different text information to form the text feature embedding. Thirdly, we uses the points of interest(POI) around the rental house information generates a variety of spatial network graphs, and learns the embedding of the network to obtain the spatial feature embedding. Finally, this paper combines the three modules into multi source rental representations, and uses the constructed fully connected neural network to predict the price. The analysis of the experimental results shows the effectiveness of our proposed model.

We propose a new causal inference framework to learn causal effects from multiple, decentralized data sources in a federated setting. We introduce an adaptive transfer algorithm that learns the similarities among the data sources by utilizing Random Fourier Features to disentangle the loss function into multiple components, each of which is associated with a data source. The data sources may have different distributions; the causal effects are independently and systematically incorporated. The proposed method estimates the similarities among the sources through transfer coefficients, and hence requiring no prior information about the similarity measures. The heterogeneous causal effects can be estimated with no sharing of the raw training data among the sources, thus minimizing the risk of privacy leak. We also provide minimax lower bounds to assess the quality of the parameters learned from the disparate sources. The proposed method is empirically shown to outperform the baselines on decentralized data sources with dissimilar distributions.

Learning to predict masked tokens in a sequence has been shown to be a powerful pretraining objective for large-scale language models. After training, such masked language models can provide distributions of tokens conditioned on bidirectional context. In this short draft, we show that such bidirectional conditionals often demonstrate considerable inconsistencies, i.e., they can not be derived from a coherent joint distribution when considered together. We empirically quantify such inconsistencies in the simple scenario of bigrams for two common styles of masked language models: T5-style and BERT-style. For example, we show that T5 models often confuse its own preference regarding two similar bigrams. Such inconsistencies may represent a theoretical pitfall for the research work on sampling sequences based on the bidirectional conditionals learned by BERT-style MLMs. This phenomenon also means that T5-style MLMs capable of infilling will generate discrepant results depending on how much masking is given, which may represent a particular trust issue.