This paper revisits building machine learning algorithms that involve interactions between entities, such as those between financial assets in an actively managed portfolio, or interactions between users in a social network. Our goal is to forecast the future evolution of ensembles of multivariate time series in such applications (e.g., the future return of a financial asset or the future popularity of a Twitter account). Designing ML algorithms for such systems requires addressing the challenges of high-dimensional interactions and non-linearity. Existing approaches usually adopt an ad-hoc approach to integrating high-dimensional techniques into non-linear models and recent studies have shown these approaches have questionable efficacy in time-evolving interacting systems. To this end, we propose a novel framework, which we dub as the additive influence model. Under our modeling assumption, we show that it is possible to decouple the learning of high-dimensional interactions from the learning of non-linear feature interactions. To learn the high-dimensional interactions, we leverage kernel-based techniques, with provable guarantees, to embed the entities in a low-dimensional latent space. To learn the non-linear feature-response interactions, we generalize prominent machine learning techniques, including designing a new statistically sound non-parametric method and an ensemble learning algorithm optimized for vector regressions. Extensive experiments on two common applications demonstrate that our new algorithms deliver significantly stronger forecasting power compared to standard and recently proposed methods.
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速度控制预测是驾驶员行为分析中一个具有挑战性的问题,旨在预测驾驶员在控制车速(例如制动或加速度)中的未来行动。在本文中,我们尝试仅使用以自我为中心的视频数据来应对这一挑战,与使用第三人称视图数据或额外的车辆传感器数据(例如GPS或两者)的文献中的大多数作品相比。为此,我们提出了一个基于新型的图形卷积网络(GCN)网络,即Egospeed-net。我们的动机是,随着时间的推移,对象的位置变化可以为我们提供非常有用的线索,以预测未来的速度变化。我们首先使用完全连接的图形图将每个类的对象之间的空间关系建模,并在其上应用GCN进行特征提取。然后,我们利用一个长期的短期内存网络将每个类别的此类特征随着时间的流逝融合到矢量中,加入此类矢量并使用多层perceptron分类器预测速度控制动作。我们在本田研究所驾驶数据集上进行了广泛的实验,并证明了Egospeed-NET的出色性能。
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深神经网络(DNN)的记忆效果在许多最先进的标签噪声学习方法中起着枢轴作用。为了利用这一财产,通常采用早期停止训练早期优化的伎俩。目前的方法通常通过考虑整个DNN来决定早期停止点。然而,DNN可以被认为是一系列层的组成,并且发现DNN中的后一个层对标签噪声更敏感,而其前同行是非常稳健的。因此,选择整个网络的停止点可以使不同的DNN层对抗彼此影响,从而降低最终性能。在本文中,我们建议将DNN分离为不同的部位,逐步培训它们以解决这个问题。而不是早期停止,它一次列举一个整体DNN,我们最初通过用相对大量的时期优化DNN来训练前DNN层。在培训期间,我们通过使用较少数量的时期使用较少的地层来逐步培训后者DNN层,以抵消嘈杂标签的影响。我们将所提出的方法术语作为渐进式早期停止(PES)。尽管其简单性,与早期停止相比,PES可以帮助获得更有前景和稳定的结果。此外,通过将PE与现有的嘈杂标签培训相结合,我们在图像分类基准上实现了最先进的性能。
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流感病毒迅速变异,可能对公共卫生构成威胁,尤其是对弱势群体的人。在整个历史中,流感A病毒在不同物种之间引起了大流行病。重要的是要识别病毒的起源,以防止爆发的传播。最近,人们对使用机器学习算法来为病毒序列提供快速准确的预测一直引起人们的兴趣。在这项研究中,使用真实的测试数据集和各种评估指标用于评估不同分类学水平的机器学习算法。由于血凝素是免疫反应中的主要蛋白质,因此仅使用血凝素序列并由位置特异性评分基质和单词嵌入来表示。结果表明,5-grams-transformer神经网络是预测病毒序列起源的最有效算法,大约99.54%的AUCPR,98.01%的F1分数和96.60%的MCC,在较高的分类水平上,约94.74%AUCPR,87.41%,87.41%,87.41% %F1分数%和80.79%的MCC在较低的分类水平下。
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流感每个季节都会发生,偶尔会引起大流行。尽管死亡率较低,但流感却是一个主要的公共卫生问题,因为肺炎等严重疾病可能会使它复杂化。一种快速,准确和低成本的方法来预测流感病毒的原始宿主和亚型,可以帮助减少病毒的传播并使资源贫乏的地区受益。在这项工作中,我们提出了多通道神经网络,以预测具有黑凝集素和神经氨酸酶蛋白序列的流感类型和宿主的抗原类型和宿主。包含完整蛋白质序列的集成数据集用于产生预训练的模型,并使用其他两个数据集来测试模型的性能。一个测试组包含完整的蛋白质序列,另一个测试组包含不完整的蛋白质序列。结果表明,多通道神经网络适用于预测具有完整和部分蛋白质序列的流感病毒宿主和抗原亚型。
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流感病毒的快速突变威胁着公共卫生。具有不同主体的病毒中的重新排列可能导致致命的大流行。然而,随着流感病毒可以在不同物种之间循环,难以在爆发期间或之后检测原始病毒的原始宿主。因此,早期和快速检测病毒宿主将有助于减少病毒的进一步扩散。我们使用各种机器学习模型,其中具有从位置特定的评分矩阵(PSSM)和从单词嵌入和单词编码中学习的特征来推断出原点寄生病毒的功能。结果表明,基于PSSM的模型的性能达到了95%的MCC,F1约为96%。使用具有Word Embedated的模型获得的MCC约为96%,F1约为97%。
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减少全身CT扫描中患者的辐射暴露引起了医学成像界的广泛关注。鉴于低辐射剂量可能导致噪声和伪像增加,这极大地影响了临床诊断。为了获得高质量的全身低剂量CT(LDCT)图像,以前的基于深度学习的研究工作引入了各种网络架构。然而,大多数这些方法只采用正常剂量CT(NDCT)图像作为地面真理来指导去噪网络的训练。这种简单的限制导致模型效率更低,并使重建的图像遭受过平滑的效果。在本文中,我们提出了一种新的任务内知识转移方法,利用来自NDCT图像的蒸馏知识来帮助LDCT图像上的培训过程。派生架构被称为师生一致性网络(TSC-Net),由教师网络和具有相同架构的学生网络组成。通过中间功能之间的监督,鼓励学生网络模仿教师网络并获得丰富的纹理细节。此外,为了进一步利用CT扫描中包含的信息,介绍了在对比学习时建立的对比正规化机制(CRM).CRM执行将恢复的CT图像拉到NDCT样本,并将远离LDCT样本的遥控器中的遥远空间。此外,基于注意力和可变形卷积机制,我们设计了一种动态增强模块(DEM)以提高网络变换能力。
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Most multimodal multi-objective evolutionary algorithms (MMEAs) aim to find all global Pareto optimal sets (PSs) for a multimodal multi-objective optimization problem (MMOP). However, in real-world problems, decision makers (DMs) may be also interested in local PSs. Also, searching for both global and local PSs is more general in view of dealing with MMOPs, which can be seen as a generalized MMOP. In addition, the state-of-the-art MMEAs exhibit poor convergence on high-dimension MMOPs. To address the above two issues, in this study, a novel coevolutionary framework termed CoMMEA for multimodal multi-objective optimization is proposed to better obtain both global and local PSs, and simultaneously, to improve the convergence performance in dealing with high-dimension MMOPs. Specifically, the CoMMEA introduces two archives to the search process, and coevolves them simultaneously through effective knowledge transfer. The convergence archive assists the CoMMEA to quickly approaching the Pareto optimal front (PF). The knowledge of the converged solutions is then transferred to the diversity archive which utilizes the local convergence indicator and the $\epsilon$-dominance-based method to obtain global and local PSs effectively. Experimental results show that CoMMEA is competitive compared to seven state-of-the-art MMEAs on fifty-four complex MMOPs.
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Conditional variational models, using either continuous or discrete latent variables, are powerful for open-domain dialogue response generation. However, previous works show that continuous latent variables tend to reduce the coherence of generated responses. In this paper, we also found that discrete latent variables have difficulty capturing more diverse expressions. To tackle these problems, we combine the merits of both continuous and discrete latent variables and propose a Hybrid Latent Variable (HLV) method. Specifically, HLV constrains the global semantics of responses through discrete latent variables and enriches responses with continuous latent variables. Thus, we diversify the generated responses while maintaining relevance and coherence. In addition, we propose Conditional Hybrid Variational Transformer (CHVT) to construct and to utilize HLV with transformers for dialogue generation. Through fine-grained symbolic-level semantic information and additive Gaussian mixing, we construct the distribution of continuous variables, prompting the generation of diverse expressions. Meanwhile, to maintain the relevance and coherence, the discrete latent variable is optimized by self-separation training. Experimental results on two dialogue generation datasets (DailyDialog and Opensubtitles) show that CHVT is superior to traditional transformer-based variational mechanism w.r.t. diversity, relevance and coherence metrics. Moreover, we also demonstrate the benefit of applying HLV to fine-tuning two pre-trained dialogue models (PLATO and BART-base).
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Cross-modality magnetic resonance (MR) image synthesis aims to produce missing modalities from existing ones. Currently, several methods based on deep neural networks have been developed using both source- and target-modalities in a supervised learning manner. However, it remains challenging to obtain a large amount of completely paired multi-modal training data, which inhibits the effectiveness of existing methods. In this paper, we propose a novel Self-supervised Learning-based Multi-scale Transformer Network (SLMT-Net) for cross-modality MR image synthesis, consisting of two stages, \ie, a pre-training stage and a fine-tuning stage. During the pre-training stage, we propose an Edge-preserving Masked AutoEncoder (Edge-MAE), which preserves the contextual and edge information by simultaneously conducting the image reconstruction and the edge generation. Besides, a patch-wise loss is proposed to treat the input patches differently regarding their reconstruction difficulty, by measuring the difference between the reconstructed image and the ground-truth. In this case, our Edge-MAE can fully leverage a large amount of unpaired multi-modal data to learn effective feature representations. During the fine-tuning stage, we present a Multi-scale Transformer U-Net (MT-UNet) to synthesize the target-modality images, in which a Dual-scale Selective Fusion (DSF) module is proposed to fully integrate multi-scale features extracted from the encoder of the pre-trained Edge-MAE. Moreover, we use the pre-trained encoder as a feature consistency module to measure the difference between high-level features of the synthesized image and the ground truth one. Experimental results show the effectiveness of the proposed SLMT-Net, and our model can reliably synthesize high-quality images when the training set is partially unpaired. Our code will be publicly available at https://github.com/lyhkevin/SLMT-Net.
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