IBM 即国际商业机器公司(International Business Machines Corporation)。总部在纽约州阿蒙克市,1911年创立于美国,是全球最大的信息技术和业务解决方案公司。 目前拥有全球雇员 30多万人,业务遍及160多个国家和地区。

VIP内容

题目:

Transfer Learning in Visual and Relational Reasoning

简介:

迁移学习已成为计算机视觉和自然语言处理中的事实上的标准,尤其是在缺少标签数据的地方。通过使用预先训练的模型和微调,可以显着提高准确性。在视觉推理任务(例如图像问答)中,传递学习更加复杂。除了迁移识别视觉特征的功能外,我们还希望迁移系统的推理能力。而且,对于视频数据,时间推理增加了另一个维度。在这项工作中,我们将迁移学习的这些独特方面形式化,并提出了一种视觉推理的理论框架,以完善的CLEVR和COGdatasets为例。此外,我们引入了一种新的,端到端的微分递归模型(SAMNet),该模型在两个数据集上的传输学习中均显示了最新的准确性和更好的性能。改进的SAMNet性能源于其将抽象的多步推理与序列的长度解耦的能力及其选择性的关注能力,使其仅能存储与问题相关的信息外部存储器中的对象。

目录:

成为VIP会员查看完整内容
0
33

最新内容

Various noise models have been developed in quantum computing study to describe the propagation and effect of the noise which is caused by imperfect implementation of hardware. Identifying parameters such as gate and readout error rates are critical to these models. We use a Bayesian inference approach to identity posterior distributions of these parameters, such that they can be characterized more elaborately. By characterizing the device errors in this way, we can further improve the accuracy of quantum error mitigation. Experiments conducted on IBM's quantum computing devices suggest that our approach provides better error mitigation performance than existing techniques used by the vendor. Also, our approach outperforms the standard Bayesian inference method in such experiments.

0
0
下载
预览

最新论文

Various noise models have been developed in quantum computing study to describe the propagation and effect of the noise which is caused by imperfect implementation of hardware. Identifying parameters such as gate and readout error rates are critical to these models. We use a Bayesian inference approach to identity posterior distributions of these parameters, such that they can be characterized more elaborately. By characterizing the device errors in this way, we can further improve the accuracy of quantum error mitigation. Experiments conducted on IBM's quantum computing devices suggest that our approach provides better error mitigation performance than existing techniques used by the vendor. Also, our approach outperforms the standard Bayesian inference method in such experiments.

0
0
下载
预览
Top