Mahsa Baktash
Computer Vision and Pattern Recognition · 2013–2021
A researcher in the Science corpus: 6 papers; trusts 445 researchers and is trusted by 25 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 117 citations into it; #5,245 of 21,575 by trust here, #5,941 by citations
- Artificial Intelligence — 74 citations into it; #7,082 of 35,345 by trust here, #9,369 by citations
- Representation & Self-Supervised Learning — 64 citations into it; #1,013 of 3,145 by trust here, #1,338 by citations
- Computational Mechanics — 23 citations into it; #7,580 of 10,001 by trust here, #2,574 by citations
- 3D Vision & Pose — 16 citations into it; #560 of 689 by trust here, #380 by citations
- Computer Graphics and Computer-Aided Design — 16 citations into it; #1,440 of 1,584 by trust here, #691 by citations
- Geology — 10 citations into it
- Cancer Research — 8 citations into it
Whom Mahsa Baktash trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Boqing Gong
- Christopher Evans
- Alex Krizhevsky
- Geoffrey E. Hinton
- Arthur Gretton
- Kaiming He
- Kate Saenko
- Yoshua Bengio
- Brian Kulis
- Yann LeCun
Who trusts Mahsa Baktash overall
- Wouter M. Kouw
- Gabriella Csurka
- Muhammad Ghifary
- Wen Li
- Mingming Gong
- Mohammad Rostami
- Saeid Motiian
- Pedro H. O. Pinheiro
- Jindong Wang
- Zheng Xu
Papers from Mahsa Baktash's perspective
Ordered by how strongly the researchers Mahsa Baktash 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)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Unsupervised Domain Adaptation by Domain Invariant Projection (2013) — 462 citations
- Deep Level Sets: Implicit Surface Representations for 3D Shape Inference (2019) — 131 citations
- Domain Adaptation on the Statistical Manifold (2014) — 140 citations
- Learning to Diversify for Single Domain Generalization (2021) — 353 citations
- Implicit Surface Representations As Layers in Neural Networks (2019) — 302 citations
- Robust Domain Generalisation by Enforcing Distribution Invariance (2016) — 63 citations