最近在计算机视觉和认知推理中的工作引起了越来越多地通过了违反了合成数据集中的预期（voe）范式。受婴儿心理学的启发，研究人员现在正在评估模型的标签场景的能力，只有预期的场景知识。然而，物理推理中现有的基于VOE的3D数据集主要提供似乎没有启发式或归纳偏差的视觉数据。物理推理的认知模型揭示婴儿创造了对象和互动的高级抽象表示。利用这一知识，我们建立了通过策划具有因果关系和规则的地面真理启发式标签的新型大型合成3D VoO数据集来研究体力学推理的基准。为了验证我们的数据集五个事件的物理推理，我们基准和分析人类性能。我们还提出了对象文件的物理推理网络（OFPR-NET），它利用DataSet的新型启发式机构来胜过我们的基线和消融模型。 OFPR-NET在学习替代物理现实方面也是灵活的，展示其能够在物理推理中学习普遍因果关系，以创建具有更好可解释性的系统。
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Classification algorithms using Transformer architectures can be affected by the sequence length learning problem whenever observations from different classes have a different length distribution. This problem brings models to use sequence length as a predictive feature instead of relying on important textual information. Even if most public datasets are not affected by this problem, privately corpora for fields such as medicine and insurance may carry this data bias. This poses challenges throughout the value chain given their usage in a machine learning application. In this paper, we empirically expose this problem and present approaches to minimize its impacts.
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Recurrent neural networks are deep learning topologies that can be trained to classify long documents. However, in our recent work, we found a critical problem with these cells: they can use the length differences between texts of different classes as a prominent classification feature. This has the effect of producing models that are brittle and fragile to concept drift, can provide misleading performances and are trivially explainable regardless of text content. This paper illustrates the problem using synthetic and real-world data and provides a simple solution using weight decay regularization.
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本文介绍了BEER2VEC型号，该模型允许将世界上最受欢迎的酒精饮料编码成具有美味建议的向量。我们使用专注于精酿啤酒的独特数据集介绍算法。我们彻底解释了如何编码风味，从经验的角度来看，啤酒向量将产生有意义的建议。我们还提出了三种不同的方法，可以在现实世界中使用Beer2Vec来启发精酿啤酒消费者。最后，我们通过Web应用程序使每个人都可以使用模型和功能。
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