NIPS 2017論文作者解讀

知識 10-12

分享背景

對連續時間上的離散事件進行建模，一直是一個非常重要的研究方向：發現事件中廣泛而複雜的影響關係，可以幫助我們準確地預測未來事件的類型和發生時間。在這篇NIPS文章中，作者設計了一個基於神經網路的點過程模型，並通過一個continuous-time LSTM增強了該模型在連續時間上的表達和泛化能力。實驗結果充分證實了所提出的模型的良好性能。

《The Neural Hawkes Process: A NeurallySelf-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.

分享主題

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

神經霍克斯過程：一個基於神經網路的自調節多變數點過程

分享人簡介

梅洪源，JHU CS系二年級博士生，導師Jason Eisner教授。他的研究興趣在於機器學習和自然語言處理。在此之前，他曾在芝加哥大學自然科學學院獲得碩士學位，並在華中科技大學電子信息工程系獲得學士學位。他曾在微軟研究院和豐田技術研究所實習。

分享時間

北京時間10月12日（周四） 20:00

參與方式

掃描海報二維碼添加社長微信，備註「梅洪源」

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