Sarah Monazam Erfani

Artificial Intelligence · 2016–2026

A researcher in the Science corpus: 10 papers; trusts 309 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 167 citations into it; #14,428 of 35,345 by trust here, #5,252 by citations
• Computer Vision and Pattern Recognition — 132 citations into it; #14,758 of 21,575 by trust here, #5,711 by citations
• Safety, Robustness & Interpretability — 39 citations into it
• Optimization & Training — 32 citations into it
• Representation & Self-Supervised Learning — 28 citations into it
• Signal Processing — 23 citations into it
• Model Architectures — 19 citations into it
• Evaluation & Benchmarks — 18 citations into it

Whom Sarah Monazam Erfani trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Alex Krizhevsky
4. Yann LeCun
5. Kaiming He
6. Ross B. Girshick
7. Diederik P. Kingma
8. Andrew Zisserman
9. Ilya Sutskever
10. Christian Szegedy

Who trusts Sarah Monazam Erfani overall

1. Lukas Ruff
2. Raghavendra Chalapathy
3. Nivedita Mishra
4. Haohan Wang
5. Thomas Schlegl
6. Guansong Pang
7. Wenpeng Hu
8. Pankaj Kumar Mishra
9. David Stutz
10. Xingjun Ma

Papers from Sarah Monazam Erfani's perspective

Ordered by how strongly the researchers Sarah Monazam Erfani 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
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

• Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality (2018) — 849 citations
• Dimensionality-Driven Learning with Noisy Labels (2018) — 475 citations
• The vulnerability of learning to adversarial perturbation increases with intrinsic dimensionality (2017) — 62 citations
• High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning (2016) — 1,120 citations
• Learning Deep Hidden Nonlinear Dynamics from Aggregate Data (2018) — 11 citations
• Normalized Loss Functions for Deep Learning with Noisy Labels (2020) — 563 citations
• Invoice Haystack: Benchmarking Document Retrieval and Visual Question Answering Under Strong Visual Homogeneity (2026)
• Alpha-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression (2021) — 333 citations
• Summarizing Significant Changes in Network Traffic Using Contrast Pattern Mining (2017) — 8 citations
• Robust Domain Generalisation by Enforcing Distribution Invariance (2016) — 63 citations