Luke Vilnis
Artificial Intelligence · 2013–2017
A researcher in the Science corpus: 5 papers; trusts 86 researchers and is trusted by 402 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 106 citations into it; #1,905 of 35,345 by trust here, #1,806 by citations
- Natural Language Processing — 69 citations into it; #1,607 of 5,688 by trust here, #3,478 by citations
- Computer Vision and Pattern Recognition — 36 citations into it; #2,230 of 21,575 by trust here, #1,567 by citations
- Model Architectures — 27 citations into it
- Optimization & Training — 26 citations into it; #1,955 of 2,601 by trust here, #812 by citations
- Representation & Self-Supervised Learning — 19 citations into it; #946 of 3,145 by trust here, #2,066 by citations
- Probabilistic & Bayesian Methods — 16 citations into it
- Generative Models — 12 citations into it; #403 of 1,956 by trust here, #228 by citations
Whom Luke Vilnis trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Yoshua Bengio
- Ilya Sutskever
- Diederik P. Kingma
- Ruslan Salakhutdinov
- Alex Krizhevsky
- Yann LeCun
- Alex Graves
- Thorsten Joachims
- Radford M. Neal
Who trusts Luke Vilnis overall
- Oren Barkan
- Aleksandar Bojchevski
- Chunyuan Li
- Zhe Gan
- Chunting Zhou
- Yoshua Bengio
- Yizhe Zhang
- Xuezhe Ma
- Jianfeng Gao
- Ruslan Salakhutdinov
Papers from Luke Vilnis's perspective
Ordered by how strongly the researchers Luke Vilnis trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Generating Sentences from a Continuous Space (2015) — 2,543 citations
- Adding Gradient Noise Improves Learning for Very Deep Networks (2015) — 597 citations
- Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning (2017) — 615 citations
- Word Representations via Gaussian Embedding (2014) — 420 citations
- Dynamic Knowledge-Base Alignment for Coreference Resolution (2013) — 30 citations