McCallum, Andrew

Energy and Policy Considerations for Deep Learning in NLP

Strubell, E., Ganesh, A., & McCallum, A. (2019) · arXiv

Green IT and SustainabilityEnergy Efficiency in Computing

Cited by 3,735

Energy and Policy Considerations for Deep Learning in NLP

Strubell, E., Ganesh, A., & McCallum, A. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Green IT and SustainabilityEnergy Efficiency in Computing

Cited by 3,684

Energy and Policy Considerations for Modern Deep Learning Research

Strubell, E., Ganesh, A., & McCallum, A. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence

Green IT and SustainabilityEnergy Efficiency in Computing

Cited by 774

Materials Synthesis Insights from Scientific Literature via Text Extraction and Machine Learning

Kim, E., Huang, K., Saunders, A., McCallum, A., Ceder, G., & Olivetti, E. (2017) · Chemistry of Materials

Machine Learning in Materials ScienceComputational and Text Analysis Methods

Cited by 473

Probabilistic Embedding of Knowledge Graphs with Box Lattice Measures

Vilnis, L., Li, X., Murty, S., & McCallum, A. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Advanced Graph Neural NetworksTopic Modeling

Cited by 111

Paper Matching with Local Fairness Constraints

Kobren, A., Saha, B., & McCallum, A. (2019) · Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Game Theory and Voting SystemsRecommender Systems and Techniques

Cited by 52

Probabilistic Box Embeddings for Uncertain Knowledge Graph Reasoning

Chen, X., Boratko, M., Chen, M., Dasgupta, S. S., Li, X. L., & McCallum, A. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Advanced Graph Neural NetworksTopic Modeling

Cited by 47

Improving Local Identifiability in Probabilistic Box Embeddings

Dasgupta, S. S., Boratko, M., Zhang, D., Vilnis, L., Li, X. L., & McCallum, A. (2020) · arXiv

Bayesian Modeling and Causal InferenceMachine Learning and Data Classification

Cited by 33

Learning Soft Linear Constraints with Application to Citation Field Extraction

Anzaroot, S., Passos, A., Belanger, D., & McCallum, A. (2014) · arXiv

Topic Modelingscientometrics and bibliometrics research

Cited by 20

DISAPERE: A Dataset for Discourse Structure in Peer Review Discussions

Kennard, N., O’Gorman, T., Das, R., Sharma, A., Bagchi, C., Clinton, M., Yelugam, P. K., Zamani, H., & McCallum, A. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Discourse Analysis in Language StudiesSentiment Analysis and Opinion Mining

Cited by 17

DISAPERE: A Dataset for Discourse Structure in Peer Review Discussions

Kennard, N., O'Gorman, T., Das, R., Sharma, A., Bagchi, C., Clinton, M., Yelugam, P. K., Zamani, H., & McCallum, A. (2021) · arXiv

Discourse Analysis in Language StudiesSentiment Analysis and Opinion Mining

Cited by 17

Softmax Bottleneck Makes Language Models Unable to Represent Multi-mode Word Distributions

Chang, H.-S., & McCallum, A. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 11

Box-To-Box Transformations for Modeling Joint Hierarchies

Dasgupta, S. S., Li, X. L., Boratko, M., Zhang, D., & McCallum, A. (2021) · Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021)

Advanced Graph Neural NetworksTopic Modeling

Cited by 2