https://mp.weixin.qq.com/s/T_3M1TMofllphjeGmupnNA

对连续时间上的离散事件进行建模，一直是一个非常重要的研究方向：发现事件中广泛而复杂的影响关系，可以帮助我们准确地预测未来事件的类型和发生时间。在这篇NIPS文章中，作者设计了一个基于神经网络的点过程模型，并通过一个continuous-time LSTM增强了该模型在连续时间上的表达和泛化能力。实验结果充分证实了所提出的模型的良好性能。

 

《The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process》

 

https://arxiv.org/pdf/1612.09328.pdf

 

Many events occur in the world. Some event types are stochastically excited or inhibited---in the sense of having their probabilities elevated or decreased---by patterns in the sequence of previous events. Discovering such patterns can help us predict which type of event will happen next and when. We propose to model streams of discrete events in continuous time, by constructing a neurally self-modulating multivariate point process in which the intensities of multiple event types evolve according to a novel continuous-time LSTM. This generative model allows past events to influence the future in complex and realistic ways, by conditioning future event intensities on the hidden state of a recurrent neural network that has consumed the stream of past events. Our model has desirable qualitative properties. It achieves competitive likelihood and predictive accuracy on real and synthetic datasets, including under missing-data conditions.

 

 

分享主题

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The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process

 

神经霍克斯过程：一个基于神经网络的自调节多变量点过程

 

 

分享人简介

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梅洪源 ，JHU CS系二年级博士生，导师Jason Eisner教授。 他的研究兴趣在于机器学习和自然语言处理。 在此之前，他曾在芝加哥大学自然科学学院获得硕士学位，并在华中科技大学电子信息工程系获得学士学位。他曾在微软研究院和丰田技术研究所实习。

 

 

 

分享时间

 

北京时间10月12日（周四） 20:00