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Asynchronous events on the continuous time domain, e.g., social media actions and stock transactions, occur frequently in the world.
On the hausdorff dimension of the intersection of the range of a stable process with a borel set
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Point processes, temporal
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On the usefulness of attention for object recognition
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Mixture of mutually exciting processes for viral diffusion
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Learning social infectivity in sparse low-rank networks using multi-dimensional hawkes processes
Zhou, K., Zha, H., and Song, L · 2013
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Hawkes model for price and trades high-frequency dynamics
Bacry, E. and Muzy, J.-F · 2014
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Neural machine translation by jointly learning to align and translate
Bahdanau, D., Cho, K., and Bengio, Y · 2014
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Shaping social activity by incentivizing users
Farajtabar, M., Du, N., Rodriguez, M. G., Valera, I., Zha, H., and Song, L · 2014
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Constructing disease network and temporal progression model via context-sensitive hawkes process
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Coevolve: A joint point process model for information diffusion and network co-evolution
Farajtabar, M., Wang, Y., Rodriguez, M. G., Li, S., Zha, H., and Song, L · 2015
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The bayesian echo chamber: Modeling social influence via linguistic accommodation
Guo, F., Blundell, C., Wallach, H., and Heller, K · 2015
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Hawkestopic: A joint model for network inference and topic modeling from text-based cascades
He, X., Rekatsinas, T., Foulds, J., Getoor, L., and Liu, Y · 2015
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Visualizing and understanding recurrent networks
Karpathy, A., Johnson, J., and Fei-Fei, L · 2015
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Recurrent marked temporal point processes: Embedding event history to vector
Du, N., Dai, H., Trivedi, R., Upadhyay, U., Gomez-Rodriguez, M., and Song, L · 2016
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Online learning for multivariate hawkes processes
Yang, Y., Etesami, J., He, N., and Kiyavash, N · 2017
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Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
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Initiator: Noise-contrastive estimation for marked temporal point process
Guo, R., Li, J., and Liu, H · 2018
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Learning temporal point processes via reinforcement learning
Li, S., Xiao, S., Zhu, S., Du, N., Xie, Y., and Song, L · 2018
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Lecture notes: Temporal point processes and the conditional intensity function
Rasmussen, J. G · 2018
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Learning network of multivariate hawkes processes: A time series approach
Etesami, J., Kiyavash, N., Zhang, K., and Singhal, K · 2016
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Gaussian error linear units (gelus)
Hendrycks, D. and Gimpel, K · 2016
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Increasing the interpretability of recurrent neural networks using hidden markov models
Krakovna, V. and Doshi-Velez, F · 2016
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Hawkes processes for continuous time sequence classification: an application to rumour stance classification in twitter
Lukasik, M., Srijith, P., Vu, D., Bontcheva, K., Zubiaga, A., and Cohn, T · 2016
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Isotonic hawkes processes
Wang, Y., Xie, B., Du, N., and Song, L · 2016
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Learning granger causality for hawkes processes
Xu, H., Farajtabar, M., and Zha, H · 2016
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The neural hawkes process: A neurally self-modulating multivariate point process
Mei, H. and Eisner, J. M · 2017
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Shaw, P., Uszkoreit, J., and Vaswani, A · 2018
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Deep reinforcement learning of marked temporal point processes
Upadhyay, U., De, A., and Rodriguez, M. G · 2018
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Non-local neural networks
Wang, X., Girshick, R., Gupta, A., and He, K · 2018
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Learning conditional generative models for temporal point processes
Xiao, S., Xu, H., Yan, J., Farajtabar, M., Yang, X., Song, L., and Zha, H · 2018
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Fully neural network based model for general temporal point processes
Omi, T., Ueda, N., and Aihara, K · 2019
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Intensity-free learning of temporal point processes
Shchur, O., Biloš, M., and Günnemann, S · 2019
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Self-attention with structural position representations
Wang, X., Tu, Z., Wang, L., and Shi, S · 2019
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