Svebor Karaman
Computer Vision and Pattern Recognition · 2018–2019
A researcher in the Science corpus: 4 papers; trusts 166 researchers and is trusted by 9 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 40 citations into it; #10,460 of 21,575 by trust here, #10,334 by citations
- Artificial Intelligence — 18 citations into it
- Generative Models — 11 citations into it; #969 of 1,956 by trust here, #1,287 by citations
- Safety, Robustness & Interpretability — 5 citations into it; #1,089 of 2,251 by trust here, #1,496 by citations
Whom Svebor Karaman trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Alex Krizhevsky
- Tero Karras
- Kaiming He
- Diederik P. Kingma
- Individualized Treat
- Yoshua Bengio
- Karen Simonyan
- Christian Szegedy
- Yann LeCun
Who trusts Svebor Karaman overall
- Luisa Verdoliva
- Utkarsh Ojha
- Diego Gragnaniello
- Luca Guarnera
- Ning Yu
- Yuyang Qian
- Sheng-Yu Wang
- Pierre Fernandez
- Momina Masood
- Riccardo Corvi
Papers from Svebor Karaman's perspective
Ordered by how strongly the researchers Svebor Karaman 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)
- Going deeper with convolutions
- ImageNet Large Scale Visual Recognition Challenge
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- GENERATIVE ADVERSARIAL NETS
Their papers
- Detecting and Simulating Artifacts in GAN Fake Images (2019) — 652 citations
- Heated-Up Softmax Embedding (2018) — 48 citations
- Annotating Social Media Data From Vulnerable Populations: Evaluating Disagreement Between Domain Experts and Graduate Student Annotators (2019) — 34 citations
- Annotating Twitter Data from Vulnerable Populations: Evaluating Disagreement Between Domain Experts and Graduate Student Annotators (2018)