已知神经网络对初始化敏感。依赖神经网络的解释方法并不强大，因为当模型被初始化并用不同的随机种子训练时，它们的解释可能会有所不同。在许多安全关键应用（例如医疗保健中的疾病诊断）中，对模型初始化的敏感性是不可取的，其中解释性可能会对有助于决策产生重大影响。在这项工作中，我们引入了一种基于参数平均的新方法，以在表格数据设置（称为XTAB）中进行可靠的解释性。我们首先初始化并训练具有不同随机种子的浅网络（称为本地面具）的多个实例，以进行下游任务。然后，我们通过“平均”本地掩码的参数来获得全局掩码模型，并表明全局模型使用多数规则根据所有本地模型中的相对重要性来对特征进行排名。我们对各种真实和合成数据集进行了广泛的实验，表明所提出的方法可用于特征选择，并获得对亚最佳模型初始化不敏感的全局特征重要性。

对比学习已成为图形结构数据的自我监督学习方法的关键组成部分。然而，尽管取得了成功，但是现有的图形对比学习方法对于节点表示或其下游任务无能为力地定量，这限制了它们在高赌场域中的应用。在本文中，我们提出了一种新颖的贝叶斯视角，曲线图对比学习方法，显示随机增强导致随机编码器。结果，我们所提出的方法通过将每个节点嵌入到确定性矢量的现有技术对比潜空间中的分布来表示每个节点。通过学习分配表示，我们在下游图分析任务中提供不确定性估计，并提高预测模型的表现力。此外，我们提出了一个贝叶斯框架，以推断对比模型的每种视图中扰动的概率，消除了对普通参数调谐的计算昂贵的搜索需要。与在多个基准数据集上的现有最先进方法相比，我们经验凭经验显示了相当大的性能。

Finding and localizing the conceptual changes in two scenes in terms of the presence or removal of objects in two images belonging to the same scene at different times in special care applications is of great significance. This is mainly due to the fact that addition or removal of important objects for some environments can be harmful. As a result, there is a need to design a program that locates these differences using machine vision. The most important challenge of this problem is the change in lighting conditions and the presence of shadows in the scene. Therefore, the proposed methods must be resistant to these challenges. In this article, a method based on deep convolutional neural networks using transfer learning is introduced, which is trained with an intelligent data synthesis process. The results of this method are tested and presented on the dataset provided for this purpose. It is shown that the presented method is more efficient than other methods and can be used in a variety of real industrial environments.

Transformers have recently gained attention in the computer vision domain due to their ability to model long-range dependencies. However, the self-attention mechanism, which is the core part of the Transformer model, usually suffers from quadratic computational complexity with respect to the number of tokens. Many architectures attempt to reduce model complexity by limiting the self-attention mechanism to local regions or by redesigning the tokenization process. In this paper, we propose DAE-Former, a novel method that seeks to provide an alternative perspective by efficiently designing the self-attention mechanism. More specifically, we reformulate the self-attention mechanism to capture both spatial and channel relations across the whole feature dimension while staying computationally efficient. Furthermore, we redesign the skip connection path by including the cross-attention module to ensure the feature reusability and enhance the localization power. Our method outperforms state-of-the-art methods on multi-organ cardiac and skin lesion segmentation datasets without requiring pre-training weights. The code is publicly available at https://github.com/mindflow-institue/DAEFormer.

The stock market prediction has been a traditional yet complex problem researched within diverse research areas and application domains due to its non-linear, highly volatile and complex nature. Existing surveys on stock market prediction often focus on traditional machine learning methods instead of deep learning methods. Deep learning has dominated many domains, gained much success and popularity in recent years in stock market prediction. This motivates us to provide a structured and comprehensive overview of the research on stock market prediction focusing on deep learning techniques. We present four elaborated subtasks of stock market prediction and propose a novel taxonomy to summarize the state-of-the-art models based on deep neural networks from 2011 to 2022. In addition, we also provide detailed statistics on the datasets and evaluation metrics commonly used in the stock market. Finally, we highlight some open issues and point out several future directions by sharing some new perspectives on stock market prediction.

Existing training criteria in automatic speech recognition(ASR) permit the model to freely explore more than one time alignments between the feature and label sequences. In this paper, we use entropy to measure a model's uncertainty, i.e. how it chooses to distribute the probability mass over the set of allowed alignments. Furthermore, we evaluate the effect of entropy regularization in encouraging the model to distribute the probability mass only on a smaller subset of allowed alignments. Experiments show that entropy regularization enables a much simpler decoding method without sacrificing word error rate, and provides better time alignment quality.

Climate change has increased the intensity, frequency, and duration of extreme weather events and natural disasters across the world. While the increased data on natural disasters improves the scope of machine learning (ML) in this field, progress is relatively slow. One bottleneck is the lack of benchmark datasets that would allow ML researchers to quantify their progress against a standard metric. The objective of this short paper is to explore the state of benchmark datasets for ML tasks related to natural disasters, categorizing them according to the disaster management cycle. We compile a list of existing benchmark datasets introduced in the past five years. We propose a web platform - NADBenchmarks - where researchers can search for benchmark datasets for natural disasters, and we develop a preliminary version of such a platform using our compiled list. This paper is intended to aid researchers in finding benchmark datasets to train their ML models on, and provide general directions for topics where they can contribute new benchmark datasets.

Recent pre-trained language models have shown promising capabilities in generating fluent and realistic natural language text. However, generating multi-sentence text with global content planning has been a long-existing research question. Current approaches for controlled text generation can hardly address this issue, as they usually condition on single known control attributes. In this study, we propose a low-cost yet effective framework which explicitly models the global content plan of the generated text. Specifically, it optimizes the joint distribution of the natural language sequence and the global content plan in a plug-and-play manner. We conduct extensive experiments on the well-established Recipe1M+ benchmark. Both automatic and human evaluations verify that our model achieves the state-of-the-art performance on the task of recipe generation

We present a new algorithm to learn a deep neural network model robust against adversarial attacks. Previous algorithms demonstrate an adversarially trained Bayesian Neural Network (BNN) provides improved robustness. We recognize the adversarial learning approach for approximating the multi-modal posterior distribution of a Bayesian model can lead to mode collapse; consequently, the model's achievements in robustness and performance are sub-optimal. Instead, we first propose preventing mode collapse to better approximate the multi-modal posterior distribution. Second, based on the intuition that a robust model should ignore perturbations and only consider the informative content of the input, we conceptualize and formulate an information gain objective to measure and force the information learned from both benign and adversarial training instances to be similar. Importantly. we prove and demonstrate that minimizing the information gain objective allows the adversarial risk to approach the conventional empirical risk. We believe our efforts provide a step toward a basis for a principled method of adversarially training BNNs. Our model demonstrate significantly improved robustness--up to 20%--compared with adversarial training and Adv-BNN under PGD attacks with 0.035 distortion on both CIFAR-10 and STL-10 datasets.

Recent work reported the label alignment property in a supervised learning setting: the vector of all labels in the dataset is mostly in the span of the top few singular vectors of the data matrix. Inspired by this observation, we derive a regularization method for unsupervised domain adaptation. Instead of regularizing representation learning as done by popular domain adaptation methods, we regularize the classifier so that the target domain predictions can to some extent ``align" with the top singular vectors of the unsupervised data matrix from the target domain. In a linear regression setting, we theoretically justify the label alignment property and characterize the optimality of the solution of our regularization by bounding its distance to the optimal solution. We conduct experiments to show that our method can work well on the label shift problems, where classic domain adaptation methods are known to fail. We also report mild improvement over domain adaptation baselines on a set of commonly seen MNIST-USPS domain adaptation tasks and on cross-lingual sentiment analysis tasks.