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Lawrence Phillips
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2020 – today
- 2024
- [i8]Peter Mühlbacher, Nikos I. Bosse, Lawrence Phillips:
Towards a Realistic Long-Term Benchmark for Open-Web Research Agents. CoRR abs/2409.14913 (2024) - 2021
- [i7]Francesco Farina, Lawrence Phillips, Nicola J. Richmond:
Intrinsic uncertainties and where to find them. CoRR abs/2107.02526 (2021)
2010 – 2019
- 2019
- [i6]Matthias Bal, Hagen Triendl, Mariana Assmann
, Michael Craig, Lawrence Phillips, Jarvist Moore Frost, Usman Bashir, Noor Shaker, Vid Stojevic:
Sparse hierarchical representation learning on molecular graphs. CoRR abs/1908.02065 (2019) - [i5]Chris Careaga, Brian Hutchinson, Nathan Oken Hodas, Lawrence Phillips:
Metric-Based Few-Shot Learning for Video Action Recognition. CoRR abs/1909.09602 (2019) - [i4]Lawrence Phillips, Garrett B. Goh, Nathan O. Hodas:
Explanatory Masks for Neural Network Interpretability. CoRR abs/1911.06876 (2019) - 2018
- [c6]Svitlana Volkova, Stephen Ranshous, Lawrence Phillips:
Predicting Foreign Language Usage from English-Only Social Media Posts. NAACL-HLT (2) 2018: 608-614 - [i3]Nathan Hilliard, Lawrence Phillips, Scott Howland, Artëm Yankov, Courtney D. Corley, Nathan O. Hodas:
Few-Shot Learning with Metric-Agnostic Conditional Embeddings. CoRR abs/1802.04376 (2018) - 2017
- [c5]Lawrence Phillips, Nathan Oken Hodas:
Assessing the Linguistic Productivity of Unsupervised Deep Neural Networks. CogSci 2017 - [c4]Lawrence Phillips, Kyle Shaffer, Dustin Arendt, Nathan Oken Hodas, Svitlana Volkova:
Intrinsic and Extrinsic Evaluation of Spatiotemporal Text Representations in Twitter Streams. Rep4NLP@ACL 2017: 201-210 - [i2]Lawrence Phillips, Nathan Oken Hodas:
Assessing the Linguistic Productivity of Unsupervised Deep Neural Networks. CoRR abs/1706.01839 (2017) - [i1]Lawrence Phillips, Chase Dowling, Kyle Shaffer, Nathan Oken Hodas, Svitlana Volkova:
Using Social Media to Predict the Future: A Systematic Literature Review. CoRR abs/1706.06134 (2017) - 2015
- [b1]Lawrence Phillips:
The Role of Empirical Evidence in Modeling Speech Segmentation. University of California, Irvine, USA, 2015 - [j1]Lawrence Phillips, Lisa Pearl:
The Utility of Cognitive Plausibility in Language Acquisition Modeling: Evidence From Word Segmentation. Cogn. Sci. 39(8): 1824-1854 (2015) - [c3]Lawrence Phillips, Lisa Pearl:
Utility-based evaluation metrics for models of language acquisition: A look at speech segmentation. CMCL@NAACL-HLT 2015: 68-78 - 2014
- [c2]Lawrence Phillips, Lisa Pearl:
Bayesian inference as a viable cross-linguistic word segmentation strategy: It's all about what's useful. CogSci 2014 - 2012
- [c1]Lawrence Phillips, Lisa Pearl:
"Less is More" in Bayesian Word Segmentation: When cognitively plausible learners outperform the ideal. CogSci 2012
Coauthor Index
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