Shashank Rajput

Artificial Intelligence · 2019–2023

A researcher in the Science corpus: 8 papers; trusts 159 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 277 citations into it
• Computer Vision and Pattern Recognition — 117 citations into it
• Safety, Robustness & Interpretability — 49 citations into it
• Optimization & Training — 47 citations into it; #2,230 of 2,601 by trust here, #1,904 by citations
• Model Architectures — 35 citations into it
• Federated & Distributed Learning — 33 citations into it
• Computer Networks and Communications — 25 citations into it
• Representation & Self-Supervised Learning — 21 citations into it

Whom Shashank Rajput trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Kaiming He
4. L. Eon Bottou
5. Ilya Sutskever
6. Karen Simonyan
7. Yoshua Bengio
8. Diederik P. Kingma
9. Yurii E. Nesterov
10. Yann LeCun

Who trusts Shashank Rajput overall

1. Amirkeivan Mohtashami
2. Kwangjun Ahn
3. Yucheng Lu
4. Hongwei Li
5. Sebastian U. Stich
6. Martin Jaggi
7. Kangwook Lee
8. Konstantin Mishchenko
9. Sai Praneeth Karimireddy
10. Wentao Guo

Papers from Shashank Rajput's perspective

Ordered by how strongly the researchers Shashank Rajput 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Attack of the Tails: Yes, You Really Can Backdoor Federated Learning (2020) — 829 citations
• Does Data Augmentation Lead to Positive Margin? (2019) — 38 citations
• Looped Transformers as Programmable Computers (2023) — 222 citations
• Recommender Systems with Generative Retrieval (2023) — 342 citations
• LIFT: Language-Interfaced Fine-Tuning for Non-Language Machine Learning Tasks (2022) — 200 citations
• Permutation-Based SGD: Is Random Optimal? (2021) — 16 citations
• Closing the convergence gap of SGD without replacement (2020) — 75 citations
• DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation (2019) — 149 citations