B. Ermiş

Artificial Intelligence · 2014–2024

A researcher in the Science corpus: 6 papers; trusts 333 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 161 citations into it; #23,448 of 35,345 by trust here, #27,551 by citations
• Computer Vision and Pattern Recognition — 102 citations into it; #14,492 of 21,575 by trust here, #20,271 by citations
• Meta & Continual Learning — 63 citations into it
• Representation & Self-Supervised Learning — 38 citations into it
• Natural Language Processing — 37 citations into it
• Model Architectures — 25 citations into it
• Radiology, Nuclear Medicine and Imaging — 14 citations into it
• Evaluation & Benchmarks — 9 citations into it

Whom B. Ermiş trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Diederik P. Kingma
5. Kaiming He
6. Yann LeCun
7. Ilya Sutskever
8. Marc'Aurelio Ranzato
9. Ross B. Girshick
10. Radford M. Neal

Who trusts B. Ermiş overall

1. Giovanni Zappella
2. Yanzhe Zhang
3. Liyuan Wang
4. Martin Wistuba
5. Aditya Rawal
6. Xuezhi Wang
7. Cédric Archambeau
8. David M. Blei
9. Da-Wei Zhou
10. Xingxing Zhang

Papers from B. Ermiş's perspective

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

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

Their papers

• On the Challenges of Using Black-Box APIs for Toxicity Evaluation in Research (2023) — 59 citations
• Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation (2024) — 178 citations
• Memory Efficient Continual Learning with Transformers (2022) — 70 citations
• Continual Learning with Transformers for Image Classification (2022) — 27 citations
• Memory Efficient Continual Learning for Neural Text Classification (2022) — 5 citations
• Iterative Splits of Quadratic Bounds for Scalable Binary Tensor Factorization (2014) — 4 citations