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2020 – today
- 2024
[c18]Yury Zemlyanskiy, Michiel de Jong, Luke Vilnis, Santiago Ontañón, William W. Cohen, Sumit Sanghai, Joshua Ainslie:
MEMORY-VQ: Compression for Tractable Internet-Scale Memory. NAACL (Short Papers) 2024: 737-744- [i18]Heri Zhao, Jeffrey Hui, Joshua Howland, Nam Nguyen, Siqi Zuo, Andrea Hu, Christopher A. Choquette-Choo, Jingyue Shen, Joe Kelley, Kshitij Bansal, Luke Vilnis, Mateo Wirth, Paul Michel, Peter Choy, Pratik Joshi, Ravin Kumar, Sarmad Hashmi, Shubham Agrawal, Zhitao Gong, Jane Fine, Tris Warkentin, Ale Jakse Hartman, Bin Ni, Kathy Korevec, Kelly Schaefer, Scott Huffman:
CodeGemma: Open Code Models Based on Gemma. CoRR abs/2406.11409 (2024) - 2023
- [c17]Luke Vilnis, Yury Zemlyanskiy, Patrick Murray, Alexandre Tachard Passos, Sumit Sanghai:
Arithmetic Sampling: Parallel Diverse Decoding for Large Language Models. ICML 2023: 35120-35136 - [i17]Yury Zemlyanskiy, Michiel de Jong, Luke Vilnis, Santiago Ontañón, William W. Cohen, Sumit Sanghai, Joshua Ainslie:
MEMORY-VQ: Compression for Tractable Internet-Scale Memory. CoRR abs/2308.14903 (2023) - 2022
- [i16]Luke Vilnis, Yury Zemlyanskiy, Patrick Murray, Alexandre Passos, Sumit Sanghai:
Arithmetic Sampling: Parallel Diverse Decoding for Large Language Models. CoRR abs/2210.15458 (2022) - [i15]Luke Vilnis, Zachary Fisher, Bhargav Kanagal, Patrick Murray, Sumit Sanghai:
ImPaKT: A Dataset for Open-Schema Knowledge Base Construction. CoRR abs/2212.10770 (2022) - 2021
- [c16]Michael Boratko, Dongxu Zhang, Nicholas Monath, Luke Vilnis, Kenneth L. Clarkson, Andrew McCallum:
Capacity and Bias of Learned Geometric Embeddings for Directed Graphs. NeurIPS 2021: 16423-16436 - 2020
- [c15]Dhruvesh Patel, Shib Sankar Dasgupta, Michael Boratko, Xiang Li, Luke Vilnis, Andrew McCallum:
Representing Joint Hierarchies with Box Embeddings. AKBC 2020 - [c14]Shib Sankar Dasgupta, Michael Boratko, Dongxu Zhang, Luke Vilnis, Xiang Li, Andrew McCallum:
Improving Local Identifiability in Probabilistic Box Embeddings. NeurIPS 2020 - [i14]Shib Sankar Dasgupta, Michael Boratko, Dongxu Zhang, Luke Vilnis, Xiang Lorraine Li, Andrew McCallum:
Improving Local Identifiability in Probabilistic Box Embeddings. CoRR abs/2010.04831 (2020)
2010 – 2019
- 2019
- [c13]Xiang Li, Luke Vilnis, Dongxu Zhang, Michael Boratko, Andrew McCallum:
Smoothing the Geometry of Probabilistic Box Embeddings. ICLR 2019 - 2018
- [c12]Shikhar Murty, Patrick Verga, Luke Vilnis, Irena Radovanovic
, Andrew McCallum:
Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking. ACL (1) 2018: 97-109 - [c11]Luke Vilnis, Xiang Li, Shikhar Murty, Andrew McCallum:
Probabilistic Embedding of Knowledge Graphs with Box Lattice Measures. ACL (1) 2018: 263-272 - [c10]Dung Thai, Sree Harsha Ramesh, Shikhar Murty, Luke Vilnis, Andrew McCallum:
Embedded-State Latent Conditional Random Fields for Sequence Labeling. CoNLL 2018: 1-10 - [c9]Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, Luke Vilnis, Ishan Durugkar, Akshay Krishnamurthy, Alex Smola, Andrew McCallum:
Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning. ICLR (Poster) 2018 - [c8]Haw-Shiuan Chang, ZiYun Wang, Luke Vilnis, Andrew McCallum:
Distributional Inclusion Vector Embedding for Unsupervised Hypernymy Detection. NAACL-HLT 2018: 485-495 - [i13]Luke Vilnis, Xiang Li, Shikhar Murty, Andrew McCallum:
Probabilistic Embedding of Knowledge Graphs with Box Lattice Measures. CoRR abs/1805.06627 (2018) - [i12]Shikhar Murty, Patrick Verga, Luke Vilnis, Irena Radovanovic, Andrew McCallum:
Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking. CoRR abs/1807.05127 (2018) - [i11]Dung Thai, Sree Harsha Ramesh, Shikhar Murty, Luke Vilnis, Andrew McCallum:
Embedded-State Latent Conditional Random Fields for Sequence Labeling. CoRR abs/1809.10835 (2018) - 2017
- [c7]Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, Luke Vilnis, Ishan Durugkar, Akshay Krishnamurthy, Alex Smola, Andrew McCallum:
Go for a Walk and Arrive at the Answer: Reasoning Over Knowledge Bases with Reinforcement Learning. AKBC@NIPS 2017 - [c6]Shikhar Murty, Patrick Verga, Luke Vilnis, Andrew McCallum:
Finer Grained Entity Typing with TypeNet. AKBC@NIPS 2017 - [i10]Xiang Li, Luke Vilnis, Andrew McCallum:
Improved Representation Learning for Predicting Commonsense Ontologies. CoRR abs/1708.00549 (2017) - [i9]Dung Thai, Shikhar Murty, Trapit Bansal, Luke Vilnis, David Belanger, Andrew McCallum:
Low-Rank Hidden State Embeddings for Viterbi Sequence Labeling. CoRR abs/1708.00553 (2017) - [i8]Haw-Shiuan Chang, ZiYun Wang, Luke Vilnis, Andrew McCallum:
Unsupervised Hypernym Detection by Distributional Inclusion Vector Embedding. CoRR abs/1710.00880 (2017) - [i7]Shikhar Murty, Patrick Verga, Luke Vilnis, Andrew McCallum:
Finer Grained Entity Typing with TypeNet. CoRR abs/1711.05795 (2017) - [i6]Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, Luke Vilnis, Ishan Durugkar, Akshay Krishnamurthy, Alexander J. Smola, Andrew McCallum:
Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning. CoRR abs/1711.05851 (2017) - 2016
- [c5]Samuel R. Bowman, Luke Vilnis, Oriol Vinyals, Andrew M. Dai, Rafal Józefowicz, Samy Bengio:
Generating Sentences from a Continuous Space. CoNLL 2016: 10-21 - 2015
- [c4]Emma Strubell, Luke Vilnis, Kate Silverstein, Andrew McCallum:
Learning Dynamic Feature Selection for Fast Sequential Prediction. ACL (1) 2015: 146-155 - [c3]Luke Vilnis, David Belanger, Daniel Sheldon, Andrew McCallum:
Bethe Projections for Non-Local Inference. UAI 2015: 892-901 - [c2]Luke Vilnis, Andrew McCallum:
Word Representations via Gaussian Embedding. ICLR 2015 - [i5]Luke Vilnis, David Belanger, Daniel Sheldon, Andrew McCallum:
Bethe Projections for Non-Local Inference. CoRR abs/1503.01397 (2015) - [i4]Emma Strubell, Luke Vilnis, Kate Silverstein, Andrew McCallum:
Learning Dynamic Feature Selection for Fast Sequential Prediction. CoRR abs/1505.06169 (2015) - [i3]Samuel R. Bowman, Luke Vilnis, Oriol Vinyals, Andrew M. Dai, Rafal Józefowicz, Samy Bengio:
Generating Sentences from a Continuous Space. CoRR abs/1511.06349 (2015) - [i2]Arvind Neelakantan, Luke Vilnis, Quoc V. Le, Ilya Sutskever, Lukasz Kaiser, Karol Kurach, James Martens:
Adding Gradient Noise Improves Learning for Very Deep Networks. CoRR abs/1511.06807 (2015) - 2014
- [i1]Emma Strubell, Luke Vilnis, Andrew McCallum:
Training for Fast Sequential Prediction Using Dynamic Feature Selection. CoRR abs/1410.8498 (2014) - 2013
- [c1]Jiaping Zheng, Luke Vilnis, Sameer Singh, Jinho D. Choi, Andrew McCallum:
Dynamic Knowledge-Base Alignment for Coreference Resolution. CoNLL 2013: 153-162
Coauthor Index
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