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Large language models have shown astonishing performance on a wide range of reasoning tasks.
Spectra of some self-exciting and mutually exciting point processes
Hawkes, A. G · 1971
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Simulation of nonhomogeneous Poisson processes by thinning
Lewis, P. A. and Shedler, G. S · 1979
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Refining event extraction through cross-document inference
Ji, H. and Grishman, R · 2008
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Speech and language processing : an introduction to natural language processing, computational linguistics, and speech recognition
Jurafsky, D. and Martin, J. H · 2009
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Multivariate Hawkes processes
Liniger, T. J · 2009
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Artificial Intelligence: A Modern Approach
Russell, S. and Norvig, P · 2010
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Exploiting syntactico-semantic structures for relation extraction
Chan, Y. S. and Roth, D · 2011
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Joint inference for event timeline construction
Do, Q., Lu, W., and Roth, D · 2012
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Gdelt: Global data on events, location, and tone, 1979–2012
Leetaru and Schrodt · 2013
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Joint event extraction via structured prediction with global features
Li, Q., Ji, H., and Huang, L · 2013
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ICEWS Coded Event Data , 2015
Boschee, E., Lautenschlager, J., O’Brien, S., Shellman, S., Starz, J., and Ward, M · 2015
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Constructing disease network and temporal progression model via context-sensitive Hawkes process
Choi, E., Du, N., Chen, R., Song, L., and Sun, J · 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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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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Learning network of multivariate Hawkes processes: A time series approach
Etesami, J., Kiyavash, N., Zhang, K., and Singhal, K · 2016
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A language-independent neural network for event detection
Feng, X., Huang, L., Tang, D., Ji, H., Qin, B., and Liu, T · 2016
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Hawkes processes for continuous time sequence classification: An application to rumour stance classification in Twitter
Lukasik, M., Srijith, P. K., Vu, D., Bontcheva, K., Zubiaga, A., and Cohn, T · 2016
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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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Know-Evolve: Deep temporal reasoning for dynamic knowledge graphs
Trivedi, R., Dai, H., Wang, Y., and Song, L · 2017
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Joint modeling of event sequence and time series with attentional twin recurrent neural networks
Xiao, S., Yan, J., Farajtabar, M., Song, L., Yang, X., and Zha, H · 2017
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Justifying recommendations using distantly-labeled reviews and fined-grained aspects
Jianmo Ni, Jiacheng Li, J. M · 2019
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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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Sentence-bert: Sentence embeddings using siamese bert-networks
Reimers, N. and Gurevych, I · 2019
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DyRep: Learning representations over dynamic graphs
Trivedi, R., Farajtabar, M., Biswal, P., and Zha, H · 2019
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Severing the edge between before and after: Neural architectures for temporal ordering of events
Ballesteros, M., Anubhai, R., Wang, S., Pourdamghani, N., Vyas, Y., Ma, J., Bhatia, P., McKeown, K., and Al-Onaizan, Y · 2020
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User-dependent neural sequence models for continuous-time event data
Boyd, A., Bamler, R., Mandt, S., and Smyth, P · 2020
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Language models are few-shot learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
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Dynamic knowledge graph based multi-event forecasting
Deng, S., Rangwala, H., and Ning, Y · 2020
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Neural temporal point processes [for] modelling electronic health records
Enguehard, J., Busbridge, D., Bozson, A., Woodcock, C., and Hammerla, N · 2020
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Deep fourier kernel for self-attentive point processes
Zhu, S., Zhang, M., Ding, R., and Xie, Y · 2021
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Zuo, S., Jiang, H., Li, Z., Zhao, T., and Zha, H · 2021
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Causality enhanced societal event forecasting with heterogeneous graph learning
Deng, S., Rangwala, H., and Ning, Y · 2022
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Generic temporal reasoning with differential analysis and explanation
Feng, Y., Zhou, B., Wang, H., Jin, H., and Roth, D · 2022
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Scaling laws for reward model overoptimization
Gao, L., Schulman, J., and Hilton, J · 2022
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Cross-media structured common space for multimedia event extraction
Li, M., Zareian, A., Zeng, Q., Whitehead, S., Lu, D., Ji, H., and Chang, S.-F · 2020
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Neural Datalog through time: Informed temporal modeling via logical specification
Mei, H., Qin, G., Xu, M., and Eisner, J · 2020
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Torque: A reading comprehension dataset of temporal ordering questions
Ning, Q., Wu, H., Han, R., Peng, N., Gardner, M., and Roth, D · 2020
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Intensity-free learning of temporal point processes
Shchur, O., Biloš, M., and Günnemann, S · 2020
Cited alongside, same era.
Learning to summarize with human feedback
Stiennon, N., Ouyang, L., Wu, J., Ziegler, D., Lowe, R., Voss, C., Radford, A., Amodei, D., and Christiano, P. F · 2020
Cited alongside, same era.
Joint constrained learning for event-event relation extraction
Wang, H., Chen, M., Zhang, H., and Roth, D · 2020
Cited alongside, same era.
Self-attentive hawkes processes
Zhang, Q., Lipani, A., Kirnap, O., and Yilmaz, E · 2020
Cited alongside, same era.
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Text-enhanced multi-granularity temporal graph learning for event prediction
Han, X. and Ning, Y · 2022
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Hua, W., Mei, H., Zohar, S., Giral, M., and Xu, Y · 2022
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Event schema induction with double graph autoencoders
Jin, X., Li, M., and Ji, H · 2022
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Open relation and event type discovery with type abstraction
Li, S., Ji, H., and Han, J · 2022
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Pyraformer: Low-complexity pyramidal attention for long-range time series modeling and forecasting
Liu, S., Yu, H., Liao, C., Li, J., Lin, W., Liu, A. X., and Dustdar, S · 2022
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Dynamic generation of interpretable inference rules in a neuro-symbolic expert system
Weir, N. and Van Durme, B · 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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Ye, A., Cui, C., Shi, T., and Riedl, M. O · 2022
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Zhang, Y., Cao, D., and Liu, Y · 2022
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Zero-shot on-the-fly event schema induction
Dror, R., Wang, H., and Roth, D · 2023
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Language models with rationality
Kassner, N., Tafjord, O., Sabharwal, A., Richardson, K., Schutze, H., and Clark, P · 2023
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Lambada: Backward chaining for automated reasoning in natural language
Kazemi, S. M., Kim, N., Bhatia, D., Xu, X., and Ramachandran, D · 2023
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Opendomain hierarchical event schema induction by incremental prompting and verification
Li, S., Zhao, R., Li, M., Ji, H., Callison-Burch, C., and Han, J · 2023
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OpenAI · 2023
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Llama 2: Open foundation and fine-tuned chat models
Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., 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., et al · 2023
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Least-to-most prompting enables complex reasoning in large language models
Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Bousquet, O., Le, Q., and Chi, E · 2023
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