Muhammad Ghifary

Artificial Intelligence · 2014–2016

A researcher in the Science corpus: 6 papers; trusts 743 researchers and is trusted by 279 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 141 citations into it; #2,731 of 35,345 by trust here, #3,687 by citations
• Computer Vision and Pattern Recognition — 125 citations into it; #2,372 of 21,575 by trust here, #2,836 by citations
• Representation & Self-Supervised Learning — 78 citations into it; #452 of 3,145 by trust here, #576 by citations
• Model Architectures — 29 citations into it
• Optimization & Training — 26 citations into it
• Reinforcement Learning — 26 citations into it; #1,423 of 2,242 by trust here, #2,160 by citations
• Evaluation & Benchmarks — 19 citations into it
• Natural Language Processing — 17 citations into it

Whom Muhammad Ghifary trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Yann LeCun
5. Ilya Sutskever
6. Diederik P. Kingma
7. Kaiming He
8. Pascal Vincent
9. Richard S. Sutton
10. Ross B. Girshick

Who trusts Muhammad Ghifary overall

1. Mei Wang
2. Gabriella Csurka
3. Xingchao Peng
4. Garrett Wilson
5. Fabio Maria Carlucci
6. Jonathan Munro
7. Judy Hoffman
8. Kaiyang Zhou
9. Saeid Motiian
10. Wen Li

Papers from Muhammad Ghifary's perspective

Ordered by how strongly the researchers Muhammad Ghifary trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Domain Generalization for Object Recognition with Multi-task Autoencoders (2015) — 719 citations
• Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation (2016) — 906 citations
• Domain Adaptive Neural Networks for Object Recognition (2014) — 468 citations
• Compatible Value Gradients for Reinforcement Learning of Continuous Deep Policies (2015) — 35 citations
• Scatter Component Analysis: A Unified Framework for Domain Adaptation and Domain Generalization (2015) — 483 citations
• Strongly-Typed Recurrent Neural Networks (2016) — 66 citations