将深度学习与象征性逻辑推理相结合旨在利用这两个领域的成功，并引起越来越多的关注。受到深度循环的启发，这是一种端到端的模型，该模型训练了逻辑程序的推理，我们引入了Ima-Glove-GA，这是一种以自然语言表达的多步推理的迭代神经推理网络。在我们的模型中，推理是使用基于带门注意机制的RNN的迭代记忆神经网络进行的。我们在三个数据集上评估了iMa-glove-ga：副本，Conceptrules V1和Conceptrules V2。实验结果表明，与DeepLo​​gic和其他RNN基线模型相比，深沟和栅极注意可以达到更高的测试精度。当规则被淘汰时，我们的模型比罗伯塔·洛尔格（Roberta-Large）实现了更好的分布概括。此外，为了解决当前多步推理数据集中推理深度分布不平衡分布的问题，我们开发了Pararule-Plus，这是一个大型数据集，其中包含更多需要更深入推理步骤的示例。实验结果表明，添加Pararule-Plus可以在需要更深层次深度的示例中提高模型的性能。源代码和数据可在https://github.com/strong-ai-lab/multi-step-deductive-reasoning-over-natural语言中获得。

自动问题质量评级（AQQR）旨在通过计算手段来评估问题质量，从而解决在线学习者的问题存储库中的出现挑战。 AQQR的现有方法仅依赖于明确定义的标准，如可读性和字数，同时不充分利用最先进的深度学习技术。我们提出DeepQR，这是一个用于AQQR的新型神经网络模型，该模型是使用从PEERWOSE，一个广泛使用的学习者平台收集的多项选择问题（MCQ）数据集进行培训的AQQR。除了设计DeepQR之外，我们还研究了基于明确定义的功能或语义功能或两者的模型。我们还引入了一种自我关注机制来捕获MCQ组件之间的语义相关性，以及使用质量评级获取问题表示的对比学习方法。从八个大学级课程中收集的数据集进行了广泛的实验，说明DeepQR在六种比较模型方面具有卓越的性能。

Optimal transport (OT) has become a widely used tool in the machine learning field to measure the discrepancy between probability distributions. For instance, OT is a popular loss function that quantifies the discrepancy between an empirical distribution and a parametric model. Recently, an entropic penalty term and the celebrated Sinkhorn algorithm have been commonly used to approximate the original OT in a computationally efficient way. However, since the Sinkhorn algorithm runs a projection associated with the Kullback-Leibler divergence, it is often vulnerable to outliers. To overcome this problem, we propose regularizing OT with the \beta-potential term associated with the so-called $\beta$-divergence, which was developed in robust statistics. Our theoretical analysis reveals that the $\beta$-potential can prevent the mass from being transported to outliers. We experimentally demonstrate that the transport matrix computed with our algorithm helps estimate a probability distribution robustly even in the presence of outliers. In addition, our proposed method can successfully detect outliers from a contaminated dataset

In the era of Internet of Things (IoT), network-wide anomaly detection is a crucial part of monitoring IoT networks due to the inherent security vulnerabilities of most IoT devices. Principal Components Analysis (PCA) has been proposed to separate network traffics into two disjoint subspaces corresponding to normal and malicious behaviors for anomaly detection. However, the privacy concerns and limitations of devices' computing resources compromise the practical effectiveness of PCA. We propose a federated PCA-based Grassmannian optimization framework that coordinates IoT devices to aggregate a joint profile of normal network behaviors for anomaly detection. First, we introduce a privacy-preserving federated PCA framework to simultaneously capture the profile of various IoT devices' traffic. Then, we investigate the alternating direction method of multipliers gradient-based learning on the Grassmann manifold to guarantee fast training and the absence of detecting latency using limited computational resources. Empirical results on the NSL-KDD dataset demonstrate that our method outperforms baseline approaches. Finally, we show that the Grassmann manifold algorithm is highly adapted for IoT anomaly detection, which permits drastically reducing the analysis time of the system. To the best of our knowledge, this is the first federated PCA algorithm for anomaly detection meeting the requirements of IoT networks.

Transformer-based models have been widely demonstrated to be successful in computer vision tasks by modelling long-range dependencies and capturing global representations. However, they are often dominated by features of large patterns leading to the loss of local details (e.g., boundaries and small objects), which are critical in medical image segmentation. To alleviate this problem, we propose a Dual-Aggregation Transformer Network called DuAT, which is characterized by two innovative designs, namely, the Global-to-Local Spatial Aggregation (GLSA) and Selective Boundary Aggregation (SBA) modules. The GLSA has the ability to aggregate and represent both global and local spatial features, which are beneficial for locating large and small objects, respectively. The SBA module is used to aggregate the boundary characteristic from low-level features and semantic information from high-level features for better preserving boundary details and locating the re-calibration objects. Extensive experiments in six benchmark datasets demonstrate that our proposed model outperforms state-of-the-art methods in the segmentation of skin lesion images, and polyps in colonoscopy images. In addition, our approach is more robust than existing methods in various challenging situations such as small object segmentation and ambiguous object boundaries.

In this paper, we propose a novel architecture, the Enhanced Interactive Transformer (EIT), to address the issue of head degradation in self-attention mechanisms. Our approach replaces the traditional multi-head self-attention mechanism with the Enhanced Multi-Head Attention (EMHA) mechanism, which relaxes the one-to-one mapping constraint among queries and keys, allowing each query to attend to multiple keys. Furthermore, we introduce two interaction models, Inner-Subspace Interaction and Cross-Subspace Interaction, to fully utilize the many-to-many mapping capabilities of EMHA. Extensive experiments on a wide range of tasks (e.g. machine translation, abstractive summarization, grammar correction, language modelling and brain disease automatic diagnosis) show its superiority with a very modest increase in model size.

Task transfer learning is a popular technique in image processing applications that uses pre-trained models to reduce the supervision cost of related tasks. An important question is to determine task transferability, i.e. given a common input domain, estimating to what extent representations learned from a source task can help in learning a target task. Typically, transferability is either measured experimentally or inferred through task relatedness, which is often defined without a clear operational meaning. In this paper, we present a novel metric, H-score, an easily-computable evaluation function that estimates the performance of transferred representations from one task to another in classification problems using statistical and information theoretic principles. Experiments on real image data show that our metric is not only consistent with the empirical transferability measurement, but also useful to practitioners in applications such as source model selection and task transfer curriculum learning.

Summary quality assessment metrics have two categories: reference-based and reference-free. Reference-based metrics are theoretically more accurate but are limited by the availability and quality of the human-written references, which are both difficulty to ensure. This inspires the development of reference-free metrics, which are independent from human-written references, in the past few years. However, existing reference-free metrics cannot be both zero-shot and accurate. In this paper, we propose a zero-shot but accurate reference-free approach in a sneaky way: feeding documents, based upon which summaries generated, as references into reference-based metrics. Experimental results show that this zero-shot approach can give us the best-performing reference-free metrics on nearly all aspects on several recently-released datasets, even beating reference-free metrics specifically trained for this task sometimes. We further investigate what reference-based metrics can benefit from such repurposing and whether our additional tweaks help.

The quality of knowledge retrieval is crucial in knowledge-intensive conversations. Two common strategies to improve the retrieval quality are finetuning the retriever or generating a self-contained query, while they encounter heavy burdens on expensive computation and elaborate annotations. In this paper, we propose an unsupervised query enhanced approach for knowledge-intensive conversations, namely QKConv. There are three modules in QKConv: a query generator, an off-the-shelf knowledge selector, and a response generator. Without extra supervision, the end-to-end joint training of QKConv explores multiple candidate queries and utilizes corresponding selected knowledge to yield the target response. To evaluate the effectiveness of the proposed method, we conducted comprehensive experiments on conversational question-answering, task-oriented dialogue, and knowledge-grounded conversation. Experimental results demonstrate that QKConv achieves state-of-the-art performance compared to unsupervised methods and competitive performance compared to supervised methods.

In this paper, we carry out numerical analysis to prove convergence of a novel sample-wise back-propagation method for training a class of stochastic neural networks (SNNs). The structure of the SNN is formulated as discretization of a stochastic differential equation (SDE). A stochastic optimal control framework is introduced to model the training procedure, and a sample-wise approximation scheme for the adjoint backward SDE is applied to improve the efficiency of the stochastic optimal control solver, which is equivalent to the back-propagation for training the SNN. The convergence analysis is derived with and without convexity assumption for optimization of the SNN parameters. Especially, our analysis indicates that the number of SNN training steps should be proportional to the square of the number of layers in the convex optimization case. Numerical experiments are carried out to validate the analysis results, and the performance of the sample-wise back-propagation method for training SNNs is examined by benchmark machine learning examples.