Alexandre Passos
Artificial Intelligence · 2011–2022
A researcher in the Science corpus: 8 papers; trusts 68 researchers and is trusted by 35 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 87 citations into it; #6,027 of 35,345 by trust here, #335 by citations
- Natural Language Processing — 68 citations into it; #1,973 of 5,688 by trust here, #1,504 by citations
- Computer Vision and Pattern Recognition — 31 citations into it; #14,486 of 21,575 by trust here, #6,281 by citations
- Optimization & Training — 19 citations into it
- Representation & Self-Supervised Learning — 18 citations into it; #1,760 of 3,145 by trust here, #1,781 by citations
- Large Language Models — 12 citations into it
- Model Architectures — 11 citations into it
- Computer Networks and Communications — 7 citations into it
Whom Alexandre Passos trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yoshua Bengio
- Tomas Mikolov
- Michael I. Jordan
- Geoffrey E. Hinton
- Ilya Sutskever
- Diederik P. Kingma
- Ronan Collobert
- Yann LeCun
- Alex Krizhevsky
- Lev-Arie Ratinov
Who trusts Alexandre Passos overall
- Yongxin Yang
- Jie Yang
- Arne Köhn
- Greg Durrett
- Jason P. C. Chiu
- Timothy M. Hospedales
- Qiang Zhou
- Arvind Neelakantan
- Shengyu Liu
- Ayan Acharya
Papers from Alexandre Passos's perspective
Ordered by how strongly the researchers Alexandre Passos 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)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- ImageNet Large Scale Visual Recognition Challenge
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Scikit-learn: Machine Learning in Python (2011) — 91,720 citations
- Large scale distributed neural network training through online distillation (2018) — 455 citations
- Efficient Non-parametric Estimation of Multiple Embeddings per Word in Vector Space (2014) — 474 citations
- Lexicon Infused Phrase Embeddings for Named Entity Resolution (2014) — 317 citations
- TensorFlow Eager: A Multi-Stage, Python-Embedded DSL for Machine Learning (2019) — 92 citations
- Flexible Modeling of Latent Task Structures in Multitask Learning (2012) — 53 citations
- FRUIT: Faithfully Reflecting Updated Information in Text (2021) — 46 citations
- Scaling Up Models and Data with t5x and seqio (2022) — 224 citations