Seguir
Luke Vilnis
Luke Vilnis
Research Scientist, Google
Dirección de correo verificada de google.com - Página principal
Título
Citado por
Citado por
Año
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
23522016
Word Representations via Gaussian Embedding
L Vilnis, A McCallum
International Conference on Learning Representations (ICLR), 2015
4972015
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
4962016
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
4462018
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
446*2017
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
1052018
Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking
S Murty, P Verga, L Vilnis, I Radovanovic, A McCallum
95*
Smoothing the geometry of probabilistic box embeddings
X Li, L Vilnis, D Zhang, M Boratko, A McCallum
International Conference on Learning Representations, 2019
712019
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
62*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
332013
Bethe Projections for Non-Local Inference
L Vilnis, D Belanger, D Sheldon, A McCallum
Uncertainty in Artificial Intelligence (UAI), 2015
312015
Improving local identifiability in probabilistic box embeddings
S Dasgupta, M Boratko, D Zhang, L Vilnis, X Li, A McCallum
Advances in Neural Information Processing Systems 33, 182-192, 2020
292020
Adding gradient noise improves learning for very deep networks. arXiv 2015
A Neelakantan, L Vilnis, QV Le, I Sutskever, L Kaiser, K Kurach, J Martens
arXiv preprint arXiv:1511.06807, 2020
212020
Finer Grained Entity Typing with TypeNet
S Murty, P Verga, L Vilnis, A McCallum
NIPS Workshop on Automated Knowledge Base Construction (AKBC), 2017
212017
Representing joint hierarchies with box embeddings
D Patel, S Sankar
Automated Knowledge Base Construction, 2020
182020
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
182015
Improved Representation Learning for Predicting Commonsense Ontologies
X Li, L Vilnis, A McCallum
ICML Workshop on Deep Structured Prediction (ICML WS), 2017
122017
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
52018
Capacity and bias of learned geometric embeddings for directed graphs
M Boratko, D Zhang, N Monath, L Vilnis, KL Clarkson, A McCallum
Advances in Neural Information Processing Systems 34, 16423-16436, 2021
42021
Optimization and learning in factorie
A Passos, L Vilnis, A McCallum
NIPS Workshop on Optimization for Machine Learning (OPT), 2013
42013
El sistema no puede realizar la operación en estos momentos. Inténtalo de nuevo más tarde.
Artículos 1–20