Luke Vilnis

Publication Activity (10 Years)

Years Active: 2013-2024
Publications (10 Years): 33

Top Topics

Continuous Space

Inference Engine

Feature Selection

Sequence Labeling

Top Venues

Publications

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 (2024)
Luke Vilnis, Yury Zemlyanskiy, Patrick Murray, Alexandre Tachard Passos, Sumit Sanghai
Arithmetic Sampling: Parallel Diverse Decoding for Large Language Models. ICML (2023)
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 (2023)
Luke Vilnis, Zachary Fisher, Bhargav Kanagal, Patrick Murray, Sumit Sanghai
ImPaKT: A Dataset for Open-Schema Knowledge Base Construction. CoRR (2022)
Luke Vilnis, Yury Zemlyanskiy, Patrick Murray, Alexandre Passos, Sumit Sanghai
Arithmetic Sampling: Parallel Diverse Decoding for Large Language Models. CoRR (2022)
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)
Shib Sankar Dasgupta, Michael Boratko, Dongxu Zhang, Luke Vilnis, Xiang Lorraine Li, Andrew McCallum
Improving Local Identifiability in Probabilistic Box Embeddings. CoRR (2020)
Dhruvesh Patel, Shib Sankar Dasgupta, Michael Boratko, Xiang Li, Luke Vilnis, Andrew McCallum
Representing Joint Hierarchies with Box Embeddings. AKBC (2020)
Shib Sankar Dasgupta, Michael Boratko, Dongxu Zhang, Luke Vilnis, Xiang Li, Andrew McCallum
Improving Local Identifiability in Probabilistic Box Embeddings. NeurIPS (2020)
Xiang Li, Luke Vilnis, Dongxu Zhang, Michael Boratko, Andrew McCallum
Smoothing the Geometry of Probabilistic Box Embeddings. ICLR (2019)
Dung Thai, Sree Harsha Ramesh, Shikhar Murty, Luke Vilnis, Andrew McCallum
Embedded-State Latent Conditional Random Fields for Sequence Labeling. CoRR (2018)
Dung Thai, Sree Harsha Ramesh, Shikhar Murty, Luke Vilnis, Andrew McCallum
Embedded-State Latent Conditional Random Fields for Sequence Labeling. CoNLL (2018)
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)
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)
Shikhar Murty, Patrick Verga, Luke Vilnis, Irena Radovanovic, Andrew McCallum
Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking. CoRR (2018)
Haw-Shiuan Chang, ZiYun Wang, Luke Vilnis, Andrew McCallum
Distributional Inclusion Vector Embedding for Unsupervised Hypernymy Detection. NAACL-HLT (2018)
Luke Vilnis, Xiang Li, Shikhar Murty, Andrew McCallum
Probabilistic Embedding of Knowledge Graphs with Box Lattice Measures. ACL (1) (2018)
Luke Vilnis, Xiang Li, Shikhar Murty, Andrew McCallum
Probabilistic Embedding of Knowledge Graphs with Box Lattice Measures. CoRR (2018)
Dung Thai, Shikhar Murty, Trapit Bansal, Luke Vilnis, David Belanger, Andrew McCallum
Low-Rank Hidden State Embeddings for Viterbi Sequence Labeling. CoRR (2017)
Haw-Shiuan Chang, ZiYun Wang, Luke Vilnis, Andrew McCallum
Unsupervised Hypernym Detection by Distributional Inclusion Vector Embedding. CoRR (2017)
Xiang Li, Luke Vilnis, Andrew McCallum
Improved Representation Learning for Predicting Commonsense Ontologies. CoRR (2017)
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)
Shikhar Murty, Patrick Verga, Luke Vilnis, Andrew McCallum
Finer Grained Entity Typing with TypeNet. AKBC@NIPS (2017)
Shikhar Murty, Patrick Verga, Luke Vilnis, Andrew McCallum
Finer Grained Entity Typing with TypeNet. CoRR (2017)
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 (2017)
Samuel R. Bowman, Luke Vilnis, Oriol Vinyals, Andrew M. Dai, Rafal Józefowicz, Samy Bengio
Generating Sentences from a Continuous Space. CoNLL (2016)
Luke Vilnis, Andrew McCallum
Word Representations via Gaussian Embedding. ICLR (2015)
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 (2015)
Emma Strubell, Luke Vilnis, Kate Silverstein, Andrew McCallum
Learning Dynamic Feature Selection for Fast Sequential Prediction. CoRR (2015)
Luke Vilnis, David Belanger, Daniel Sheldon, Andrew McCallum
Bethe Projections for Non-Local Inference. CoRR (2015)
Emma Strubell, Luke Vilnis, Kate Silverstein, Andrew McCallum
Learning Dynamic Feature Selection for Fast Sequential Prediction. ACL (1) (2015)
Luke Vilnis, David Belanger, Daniel Sheldon, Andrew McCallum
Bethe Projections for Non-Local Inference. UAI (2015)
Samuel R. Bowman, Luke Vilnis, Oriol Vinyals, Andrew M. Dai, Rafal Józefowicz, Samy Bengio
Generating Sentences from a Continuous Space. CoRR (2015)
Emma Strubell, Luke Vilnis, Andrew McCallum
Training for Fast Sequential Prediction Using Dynamic Feature Selection. CoRR (2014)
Jiaping Zheng, Luke Vilnis, Sameer Singh, Jinho D. Choi, Andrew McCallum
Dynamic Knowledge-Base Alignment for Coreference Resolution. CoNLL (2013)