Robust perception relies on both bottom-up and top-down signals. Bottom-up signals consist of what's directly observed through sensation. Top-down signals consist of beliefs and expectations based on past experience and short-term memory, such as how the phrase `peanut butter and~...' will be completed. The optimal combination of bottom-up and top-down information remains an open question, but the manner of combination must be dynamic and both context and task dependent. To effectively utilize the wealth of potential top-down information available, and to prevent the cacophony of intermixed signals in a bidirectional architecture, mechanisms are needed to restrict information flow. We explore deep recurrent neural net architectures in which bottom-up and top-down signals are dynamically combined using attention. Modularity of the architecture further restricts the sharing and communication of information. Together, attention and modularity direct information flow, which leads to reliable performance improvements in perceptual and language tasks, and in particular improves robustness to distractions and noisy data. We demonstrate on a variety of benchmarks in language modeling, sequential image classification, video prediction and reinforcement learning that the \emph{bidirectional} information flow can improve results over strong baselines.

中文翻译：

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学习将循环神经网络中的自上而下和自下而上的信号与对模块的注意力结合起来

稳健的感知依赖于自下而上和自上而下的信号。自下而上的信号由通过感觉直接观察到的信号组成。自上而下的信号包括基于过去经验和短期记忆的信念和期望，例如短语“花生酱和~...”将如何完成。自下而上和自上而下信息的最佳组合仍然是一个悬而未决的问题，但组合方式必须是动态的，并且取决于上下文和任务。为了有效利用大量潜在的自上而下的可用信息，并防止双向架构中混合信号的杂音，需要机制来限制信息流。我们探索深度循环神经网络架构，其中使用注意力动态组合自下而上和自上而下的信号。架构的模块化进一步限制了信息的共享和交流。注意力和模块化共同引导信息流，这导致感知和语言任务的可靠性能改进，特别是提高了对干扰和嘈杂数据的鲁棒性。我们在语言建模、序列图像分类、视频预测和强化学习方面的各种基准测试中证明，\emph {双向} 信息流可以改善强基线的结果。