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A Primal decomposition algorithm for distributed multistage scenario model predictive control

Dinesh Krishnamoorthy, Bjarne Foss, Sigurd Skogestad
2019 Journal of Process Control  
This paper proposes a primal decomposition algorithm for efficient computation of multistage scenario model predictive control, where the future evolution of uncertainty is represented by a scenario tree  ...  The performance of the proposed approach, and the backtracking algorithm is demonstrated using a CSTR case study.  ...  In recent years, there has been an increasing trend in the use of economic objectives in the framework of nonlinear model predictive control, known as economic MPC.  ... 
doi:10.1016/j.jprocont.2019.02.003 fatcat:6lmz35r2ubgrlj3fnnpny6mgzu

Emoji Prediction: Extensions and Benchmarking [article]

Weicheng Ma, Ruibo Liu, Lili Wang, Soroush Vosoughi
2020 arXiv   pre-print
Through emoji prediction, models can learn rich representations of the communicative intent of the written text.  ...  Our results demonstrate the efficacy of deep Transformer-based models on the emoji prediction task.  ...  With the extended emoji-set, we observe much more potential of emoji prediction models in the NLP field.  ... 
arXiv:2007.07389v1 fatcat:m4qlf2lzxveuvcwapxqzqhy6l4

Neural models of factuality [article]

Rachel Rudinger, Aaron Steven White, Benjamin Van Durme
2018 arXiv   pre-print
We also present a substantial expansion of the It Happened portion of the Universal Decompositional Semantics dataset, yielding the largest event factuality dataset to date.  ...  We present two neural models for event factuality prediction, which yield significant performance gains over previous models on three event factuality datasets: FactBank, UW, and MEANTIME.  ...  The views and conclusions contained in this publication are those of the authors and should not be interpreted as representing official policies or endorsements of DARPA or the U.S. Government.  ... 
arXiv:1804.02472v1 fatcat:hisryuisk5ar7hmzewmn7jrocy

Neural Models of Factuality

Rachel Rudinger, Aaron Steven White, Benjamin Van Durme
2018 Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)  
We also present a substantial expansion of the It Happened portion of the Universal Decompositional Semantics dataset, yielding the largest event factuality dataset to date.  ...  We present two neural models for event factuality prediction, which yield significant performance gains over previous models on three event factuality datasets: FactBank, UW, and MEANTIME.  ...  The views and conclusions contained in this publication are those of the authors and should not be interpreted as representing official policies or endorsements of DARPA or the U.S. Government.  ... 
doi:10.18653/v1/n18-1067 dblp:conf/naacl/RudingerWD18 fatcat:nwxl2q6vmrcmvpt2vr5j2sj5ze

Beyond LDA: Exploring Supervised Topic Modeling for Depression-Related Language in Twitter

Philip Resnik, William Armstrong, Leonardo Claudino, Thang Nguyen, Viet-An Nguyen, Jordan Boyd-Graber
2015 Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality  
In this paper, we explore the use of supervised topic models in the analysis of linguistic signal for detecting depression, providing promising results using several models.  ...  Topic models can yield insight into how depressed and non-depressed individuals use language differently.  ...  Any opinions, findings, conclusions, or recommendations expressed here are those of the authors and do not necessarily reflect the view of the sponsor.  ... 
doi:10.3115/v1/w15-1212 dblp:conf/naacl/ResnikACNNB15 fatcat:ipiivoywlzainfxmcou7fwvwwi

Prediction of black box warning by mining patterns of Convergent Focus Shift in clinical trial study populations using linked public data

Handong Ma, Chunhua Weng
2016 Journal of Biomedical Informatics  
We also demonstrated the feasibility of the predictor for identifying long-term BBW acquisition events without compromising prediction accuracy.  ...  A random forest predictive model was developed to predict BBW acquisition incidents based on CFS patterns among these drugs.  ...  This result shows that given enough data for model training, the model could predict the future BBW acquisition events quite well, with little accuracy loss even for long-term predictions.  ... 
doi:10.1016/j.jbi.2016.01.015 pmid:26851401 pmcid:PMC4837034 fatcat:bw5isufaxzff3lkjn4pxxrun5a

Confidence Modeling for Neural Semantic Parsing [article]

Li Dong, Chris Quirk, Mirella Lapata
2018 arXiv   pre-print
Beyond confidence estimation, we identify which parts of the input contribute to uncertain predictions allowing users to interpret their model, and verify or refine its input.  ...  Experimental results show that our confidence model significantly outperforms a widely used method that relies on posterior probability, and improves the quality of interpretation compared to simply relying  ...  Acknowledgments We would like to thank Pengcheng Yin for sharing with us the preprocessed version of the DJANGO dataset.  ... 
arXiv:1805.04604v1 fatcat:tmhxcmf6avcabm3rrd4beyewzq

Confidence Modeling for Neural Semantic Parsing

Li Dong, Chris Quirk, Mirella Lapata
2018 Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)  
Beyond confidence estimation, we identify which parts of the input contribute to uncertain predictions allowing users to interpret their model, and verify or refine its input.  ...  Experimental results show that our confidence model significantly outperforms a widely used method that relies on posterior probability, and improves the quality of interpretation compared to simply relying  ...  Acknowledgments We would like to thank Pengcheng Yin for sharing with us the preprocessed version of the DJANGO dataset.  ... 
doi:10.18653/v1/p18-1069 dblp:conf/acl/QuirkLD18 fatcat:c764d4i36ba57gdqqwzgtcwiuu

Neural Models for Detecting Binary Semantic Textual Similarity for Algerian and MSA

Wafia Adouane, Jean-Philippe Bernardy, Simon Dobnik
2019 Proceedings of the Fourth Arabic Natural Language Processing Workshop  
We compare the performance of various models on both datasets and report the best performing configurations.  ...  The results show that relatively simple models composed of 2 LSTM layers outperform by far other more sophisticated attention-based architectures, for both ALG and MSA datasets.  ...  in Probability (CLASP) at the University of Gothenburg.  ... 
doi:10.18653/v1/w19-4609 dblp:conf/wanlp/AdouaneBD19 fatcat:cribkn53bnfs3j4wo575qar5nq

RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models [article]

Samuel Gehman, Suchin Gururangan, Maarten Sap, Yejin Choi, Noah A. Smith
2020 arXiv   pre-print
We investigate the extent to which pretrained LMs can be prompted to generate toxic language, and the effectiveness of controllable text generation algorithms at preventing such toxic degeneration.  ...  Pretrained neural language models (LMs) are prone to generating racist, sexist, or otherwise toxic language which hinders their safe deployment.  ...  neural language models, and therefore use the term "neural toxic degeneration."  ... 
arXiv:2009.11462v2 fatcat:sdzqn6oumjgwvheetr2jrgggqq

Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models [article]

Iulian V. Serban, Alessandro Sordoni, Yoshua Bengio, Aaron Courville, Joelle Pineau
2016 arXiv   pre-print
In support of this goal, we extend the recently proposed hierarchical recurrent encoder-decoder neural network to the dialogue domain, and demonstrate that this model is competitive with state-of-the-art  ...  We investigate the task of building open domain, conversational dialogue systems based on large dialogue corpora using generative models.  ...  Banchs for providing the Movie-DiC dataset, and Luisa Coheur for providing the SubTle dataset. The authors also thank the anonymous AAAI reviewers for their helpful feedback.  ... 
arXiv:1507.04808v3 fatcat:sw2dgffakvchphom64e2ebg43y

A Survey of Machine Narrative Reading Comprehension Assessments [article]

Yisi Sang, Xiangyang Mou, Jing Li, Jeffrey Stanton, Mo Yu
2022 arXiv   pre-print
As the body of research on machine narrative comprehension grows, there is a critical need for consideration of performance assessment strategies as well as the depth and scope of different benchmark tasks  ...  differences among assessment tasks; and discuss the implications of our typology for new task design and the challenges of narrative reading comprehension.  ...  Acknowledgements This research was supported, in part, by the NSF (USA) under Grant Numbers CNS-1948457.  ... 
arXiv:2205.00299v1 fatcat:ueifos3ymrhhfpd7hkbzdagxwy

On the Opportunities and Risks of Foundation Models [article]

Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S. Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, Erik Brynjolfsson, Shyamal Buch (+102 others)
2021 arXiv   pre-print
Homogenization provides powerful leverage but demands caution, as the defects of the foundation model are inherited by all the adapted models downstream.  ...  AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.  ...  Acknowledgments References ACKNOWLEDGMENTS We would like to thank the following people for their valuable feedback: Mohit Bansal, Boaz Barak, Yoshua Bengio, Sam Bowman, Collin Burns, Nicholas Carlini  ... 
arXiv:2108.07258v2 fatcat:yktkv4diyrgzzfzqlpvaiabc2m

Contextualization of Morphological Inflection

Ekaterina Vylomova, Ryan Cotterell, Trevor Cohn, Timothy Baldwin, Jason Eisner
2019 Proceedings of the 2019 Conference of the North  
We experiment on several typologically diverse languages from the Universal Dependencies treebanks, showing the utility of incorporating linguisticallymotivated latent variables into NLP models.  ...  In this paper, we isolate the task of predicting a fully inflected sentence from its partially lemmatized version.  ...  The first author would like to acknowledge the Google PhD fellowship. The second author would like to acknowledge a Facebook Fellowship.  ... 
doi:10.18653/v1/n19-1203 dblp:conf/naacl/VylomovaCCBE19 fatcat:psy3hsja3ne4xbbow67gyewgou

Contextualization of Morphological Inflection [article]

Ekaterina Vylomova, Ryan Cotterell, Timothy Baldwin, Trevor Cohn, Jason Eisner
2019 arXiv   pre-print
We experiment on several typologically diverse languages from the Universal Dependencies treebanks, showing the utility of incorporating linguistically-motivated latent variables into NLP models.  ...  In this paper, we isolate the task of predicting a fully inflected sentence from its partially lemmatized version.  ...  The first author would like to acknowledge the Google PhD fellowship. The second author would like to acknowledge a Facebook Fellowship.  ... 
arXiv:1905.01420v1 fatcat:chzwtudvw5fzbdrarli2xktade
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