Luke Vilnis
Title
Cited by
Cited by
Year
Generating sentences from a continuous space
SR Bowman, L Vilnis, O Vinyals, AM Dai, R Jozefowicz, S Bengio
Conference on Computational Natural Language Learning (CoNLL), 2016
13842016
Adding gradient noise improves learning for very deep networks
A Neelakantan, L Vilnis, QV Le, I Sutskever, L Kaiser, K Kurach, J Martens
International Conference on Learning Representations Workshop (ICLR WS), 2016
3212016
Word Representations via Gaussian Embedding
L Vilnis, A McCallum
International Conference on Learning Representations (ICLR), 2015
2682015
Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning
R Das, S Dhuliawala, M Zaheer, L Vilnis, I Durugkar, A Krishnamurthy, ...
International Conference on Learning Representations (ICLR), 2018
1832018
Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases with Reinforcement Learning
R Das, S Dhuliawala, M Zaheer, L Vilnis, I Durugkar, A Krishnamurthy, ...
NIPS Workshop on Automated Knowledge Base Construction (AKBC), 2017
183*2017
Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking
S Murty, P Verga, L Vilnis, I Radovanovic, A McCallum
51*
Probabilistic Embedding of Knowledge Graphs with Box Lattice Measures
L Vilnis, X Li, S Murty, A McCallum
Annual Meeting of the Association for Computational Linguistics (ACL), 2018
452018
Unsupervised Hypernym Detection by Distributional Inclusion Vector Embedding
HS Chang, ZY Wang, L Vilnis, A McCallum
North American Chapter of the Association for Computational Linguistics (NAACL), 2018
44*2018
Dynamic knowledge-base alignment for coreference resolution
J Zheng, L Vilnis, S Singh, JD Choi, A McCallum
Conference on Computational Natural Language Learning (CoNLL), 2013
292013
Smoothing the geometry of probabilistic box embeddings
X Li, L Vilnis, D Zhang, M Boratko, A McCallum
International Conference on Learning Representations, 2018
252018
Bethe Projections for Non-Local Inference
L Vilnis, D Belanger, D Sheldon, A McCallum
Uncertainty in Artificial Intelligence (UAI), 2015
212015
Adding gradient noise improves learning for very deep networks (2015)
A Neelakantan, L Vilnis, QV Le, I Sutskever, L Kaiser, K Kurach, J Martens
arXiv preprint arXiv:1511.06807, 0
18
Learning Dynamic Feature Selection for Fast Sequential Prediction
E Strubell, L Vilnis, K Silverstein, A McCallum
Annual Meeting of the Association for Computational Linguistics (ACL), 2015
162015
Finer Grained Entity Typing with TypeNet
S Murty, P Verga, L Vilnis, A McCallum
NIPS Workshop on Automated Knowledge Base Construction (AKBC), 2017
152017
Improved Representation Learning for Predicting Commonsense Ontologies
X Li, L Vilnis, A McCallum
ICML Workshop on Deep Structured Prediction (ICML WS), 2017
102017
Optimization and learning in factorie
A Passos, L Vilnis, A McCallum
NIPS Workshop on Optimization for Machine Learning (OPT), 2013
42013
Embedded-State Latent Conditional Random Fields for Sequence Labeling
D Thai, SH Ramesh, S Murty, L Vilnis, A McCallum
arXiv preprint arXiv:1809.10835, 2018
32018
Improving Local Identifiability in Probabilistic Box Embeddings
SS Dasgupta, M Boratko, D Zhang, L Vilnis, XL Li, A McCallum
arXiv preprint arXiv:2010.04831, 2020
12020
Representing joint hierarchies with box embeddings
D Patel, S Sankar
Automated Knowledge Base Construction, 2020
12020
Hierarchical losses and new resources for fine-grained entity typing and linking
L Vilnis, I Radovanovic, A McCallum
arXiv preprint arXiv:1807.05127, 2018
12018
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