Luke Vilnis

According to our database, Luke Vilnis authored at least 24 papers between 2013 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

MEMORY-VQ: Compression for Tractable Internet-Scale Memory.

Yury Zemlyanskiy, Michiel de Jong, Luke Vilnis, Santiago Ontañón, William W. Cohen, Sumit Sanghai, Joshua Ainslie

CoRR, 2023

Arithmetic Sampling: Parallel Diverse Decoding for Large Language Models.

Luke Vilnis, Yury Zemlyanskiy, Patrick Murray, Alexandre Tachard Passos, Sumit Sanghai

Proceedings of the International Conference on Machine Learning, 2023

2022

ImPaKT: A Dataset for Open-Schema Knowledge Base Construction.

Luke Vilnis, Zachary Fisher, Bhargav Kanagal, Patrick Murray, Sumit Sanghai

CoRR, 2022

2021

Capacity and Bias of Learned Geometric Embeddings for Directed Graphs.

Michael Boratko, Dongxu Zhang, Nicholas Monath, Luke Vilnis, Kenneth L. Clarkson, Andrew McCallum

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Improving Local Identifiability in Probabilistic Box Embeddings.

Shib Sankar Dasgupta, Michael Boratko, Dongxu Zhang, Luke Vilnis, Xiang Li, Andrew McCallum

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Representing Joint Hierarchies with Box Embeddings.

Dhruvesh Patel, Shib Sankar Dasgupta, Michael Boratko, Xiang Li, Luke Vilnis, Andrew McCallum

Proceedings of the Conference on Automated Knowledge Base Construction, 2020

2019

Smoothing the Geometry of Probabilistic Box Embeddings.

Xiang Li, Luke Vilnis, Dongxu Zhang, Michael Boratko, Andrew McCallum

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Distributional Inclusion Vector Embedding for Unsupervised Hypernymy Detection.

Haw-Shiuan Chang, ZiYun Wang, Luke Vilnis, Andrew McCallum

Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2018

Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning.

Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, Luke Vilnis, Ishan Durugkar, Akshay Krishnamurthy, Alex Smola, Andrew McCallum

Proceedings of the 6th International Conference on Learning Representations, 2018

Embedded-State Latent Conditional Random Fields for Sequence Labeling.

Dung Thai, Sree Harsha Ramesh, Shikhar Murty, Luke Vilnis, Andrew McCallum

Proceedings of the 22nd Conference on Computational Natural Language Learning, 2018

Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking.

Shikhar Murty, Patrick Verga, Luke Vilnis, Irena Radovanovic, Andrew McCallum

Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, 2018

Probabilistic Embedding of Knowledge Graphs with Box Lattice Measures.

Luke Vilnis, Xiang Li, Shikhar Murty, Andrew McCallum

Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, 2018

2017

Unsupervised Hypernym Detection by Distributional Inclusion Vector Embedding.

Haw-Shiuan Chang, ZiYun Wang, Luke Vilnis, Andrew McCallum

CoRR, 2017

Low-Rank Hidden State Embeddings for Viterbi Sequence Labeling.

Dung Thai, Shikhar Murty, Trapit Bansal, Luke Vilnis, David Belanger, Andrew McCallum

CoRR, 2017

Improved Representation Learning for Predicting Commonsense Ontologies.

Xiang Li, Luke Vilnis, Andrew McCallum

CoRR, 2017

Finer Grained Entity Typing with TypeNet.

Shikhar Murty, Patrick Verga, Luke Vilnis, Andrew McCallum

Proceedings of the 6th Workshop on Automated Knowledge Base Construction, 2017

Go for a Walk and Arrive at the Answer: Reasoning Over Knowledge Bases with Reinforcement Learning.

Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, Luke Vilnis, Ishan Durugkar, Akshay Krishnamurthy, Alex Smola, Andrew McCallum

Proceedings of the 6th Workshop on Automated Knowledge Base Construction, 2017

2016

Generating Sentences from a Continuous Space.

Samuel R. Bowman, Luke Vilnis, Oriol Vinyals, Andrew M. Dai, Rafal Józefowicz, Samy Bengio

Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning, 2016

2015

Word Representations via Gaussian Embedding.

Luke Vilnis, Andrew McCallum

Proceedings of the 3rd International Conference on Learning Representations, 2015

Adding Gradient Noise Improves Learning for Very Deep Networks.

Arvind Neelakantan, Luke Vilnis, Quoc V. Le, Ilya Sutskever, Lukasz Kaiser, Karol Kurach, James Martens

CoRR, 2015

Bethe Projections for Non-Local Inference.

Luke Vilnis, David Belanger, Daniel Sheldon, Andrew McCallum

Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 2015

Learning Dynamic Feature Selection for Fast Sequential Prediction.

Emma Strubell, Luke Vilnis, Kate Silverstein, Andrew McCallum

Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, 2015

2014

Training for Fast Sequential Prediction Using Dynamic Feature Selection.

Emma Strubell, Luke Vilnis, Andrew McCallum

CoRR, 2014

2013

Dynamic Knowledge-Base Alignment for Coreference Resolution.

Jiaping Zheng, Luke Vilnis, Sameer Singh, Jinho D. Choi, Andrew McCallum

Proceedings of the Seventeenth Conference on Computational Natural Language Learning, 2013