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Neural Temporal Point Processes (TPPs) are the prevalent paradigm for modeling continuous-time event sequences, such as user activities on the web and financial transactions.
Some results on tests for poisson processes
Lewis, P. A. W · 1965
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Spectra of some self-exciting and mutually exciting point processes
Hawkes, A. G · 1971
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Catastrophic interference in connectionist networks: The sequential learning problem
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SNAP Datasets: Stanford large network dataset collection , 2014
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Adam: A method for stochastic optimization
Kingma, D. and Ba, J · 2015
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Privacy-preserving deep learning
Shokri, R. and Shmatikov, V · 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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Tracking dynamic point processes on networks
Hall, E. C. and Willett, R. M · 2016
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Rusu, A. A., Rabinowitz, N. C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., and Hadsell, R · 2016
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Bacry, E., Bompaire, M., Gaïffas, S., and Poulsen, S · 2017
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Overcoming catastrophic forgetting in neural networks
Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A. A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al · 2017
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The neural Hawkes process: A neurally self-modulating multivariate point process
Mei, H. and Eisner, J · 2017
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Automatic differentiation in PyTorch
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A · 2017
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Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I · 2017
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Modeling the intensity function of point process via recurrent neural networks
Xiao, S., Yan, J., Yang, X., Zha, H., and Chu, S · 2017
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Online learning for multivariate hawkes processes
Yang, Y., Etesami, J., He, N., and Kiyavash, N · 2017
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Continual learning through synaptic intelligence
Zenke, F., Poole, B., and Ganguli, S · 2017
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User behavior data from taobao for recommendation , 2018
Alibaba · 2018
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Packnet: Adding multiple tasks to a single network by iterative pruning
Mallya, A. and Lazebnik, S · 2018
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Amazon review data , 2018
Ni, J · 2018
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Fully neural network based model for general temporal point processes
Omi, T., Ueda, N., and Aihara, K · 2019
Cited alongside, same era.
Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift
Snoek, J., Ovadia, Y., Fertig, E., Lakshminarayanan, B., Nowozin, S., Sculley, D., Dillon, J. V., Ren, J., and Nado, Z · 2019
Cited alongside, same era.
User-dependent neural sequence models for continuous-time event data
Boyd, A., Bamler, R., Mandt, S., and Smyth, P · 2020
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Dualnet: Continual learning, fast and slow
Pham, Q., Liu, C., and Hoi, S · 2021
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Detecting anomalous event sequences with temporal point processes
Shchur, O., Turkmen, A. C., Januschowski, T., Gasthaus, J., , and Günemann, S · 2021
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A graph regularized point process model for event propagation sequence
Xue, S., Shi, X., Hao, H., Ma, L., Zhang, J., Wang, S., and Wang, S · 2021
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Improving language models by retrieving from trillions of tokens , 2022
Borgeaud, S., Mensch, A., Hoffmann, J., Cai, T., Rutherford, E., Millican, K., van den Driessche, G., Lespiau, J.-B., Damoc, B., Clark, A., de Las Casas, D., Guy, A., Menick, J., Ring, R., Hennigan, T., Huang, S., Maggiore, L., Jones, C., Cassirer, A., Brock, A., Paganini, M., Irving, G., Vinyals, O., Osindero, S., Simonyan, K., Rae, J. W., Elsen, E., and Sifre, L · 2022
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Prompt-augmented linear probing: Scaling beyond the limit of few-shot in-context learners
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Dark experience for general continual learning: a strong, simple baseline
Buzzega, P., Boschini, M., Porrello, A., Abati, D., and Calderara, S · 2020
Cited alongside, same era.
Embracing change: Continual learning in deep neural networks
Hadsell, R., Rao, D., Rusu, A. A., and Pascanu, R · 2020
Cited alongside, same era.
Continual learning of a mixed sequence of similar and dissimilar tasks
Ke, Z., Liu, B., and Huang, X · 2020
Cited alongside, same era.
Neural Datalog through time: Informed temporal modeling via logical specification
Mei, H., Qin, G., Xu, M., and Eisner, J · 2020
Cited alongside, same era.
Intensity-free learning of temporal point processes
Shchur, O., Biloš, M., and Günnemann, S · 2020
Cited alongside, same era.
Streaming graph neural networks via continual learning
Wang, J., Song, G., Wu, Y., and Wang, L · 2020
Cited alongside, same era.
Self-attentive Hawkes process
Zhang, Q., Lipani, A., Kirnap, O., and Yilmaz, E · 2020
Cited alongside, same era.
Cho, H., Kim, H. J., Kim, J., Lee, S.-W., Lee, S.-g., Yoo, K. M., and Kim, T · 2022
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Hyperhawkes: Hypernetwork based neural temporal point process
Dubey, M., Srijith, P. K., and Desarkar, M. S · 2022
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Parameter-efficient prompt tuning makes generalized and calibrated neural text retrievers
Tam, W. L., Liu, X., Ji, K., Xue, L., Zhang, X., Dong, Y., Liu, J., Hu, M., and Tang, J · 2022
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Prompt augmented generative replay via supervised contrastive learning for lifelong intent detection
Varshney, V., Patidar, M., Kumar, R., Vig, L., and Shroff, G · 2022
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Hypro: A hybridly normalized probabilistic model for long-horizon prediction of event sequences
Xue, S., Shi, X., Zhang, Y. J., and Mei, H · 2022
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Transformer embeddings of irregularly spaced events and their participants
Yang, C., Mei, H., and Eisner, J · 2022
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Continual treatment effect estimation: Challenges and opportunities
Chu, Z. and Li, S · 2023
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Continual learning in predictive autoscaling
Hao, H., Chu, Z., Zhu, S., Jiang, G., Wang, Y., Jiang, C., Zhang, J., Jiang, W., Xue, S., and Zhou, J · 2023
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Mixpro: Simple yet effective data augmentation for prompt-based learning
Li, B., Dou, L., Hou, Y., Feng, Y., Mu, H., and Che, W · 2023
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Bellman meets hawkes: Model-based reinforcement learning via temporal point processes
Qu, C., Tan, X., Xue, S., Shi, X., Zhang, J., and Mei, H · 2023
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Language models can improve event prediction by few-shot abductive reasoning
Shi, X., Xue, S., Wang, K., Zhou, F., Zhang, J. Y., Zhou, J., Tan, C., and Mei, H · 2023
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Enhancing recommender systems with large language model reasoning graphs
Wang, Y., Chu, Z., Ouyang, X., Wang, S., Hao, H., Shen, Y., Gu, J., Xue, S., Zhang, J. Y., Cui, Q., et al · 2023
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Easytpp: Towards open benchmarking the temporal point processes
Xue, S., Shi, X., Chu, Z., Wang, Y., Zhou, F., Hao, H., Jiang, C., Pan, C., Xu, Y., Zhang, J. Y., Wen, Q., Zhou, J., and Mei, H · 2023
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