Shantanu Thakoor
Artificial Intelligence · 2018–2025
A researcher in the Science corpus: 8 papers; trusts 36 researchers and is trusted by 2 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 142 citations into it
- Computer Vision and Pattern Recognition — 46 citations into it
- Representation & Self-Supervised Learning — 38 citations into it
- Graph Learning — 25 citations into it
- Model Architectures — 21 citations into it
- Optimization & Training — 20 citations into it
- Statistical and Nonlinear Physics — 16 citations into it
- Safety, Robustness & Interpretability — 13 citations into it; #1,949 of 2,251 by trust here, #2,110 by citations
Whom Shantanu Thakoor trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Geoffrey E. Hinton
- Yoshua Bengio
- Kaiming He
- Jimmy Ba
- Alex Krizhevsky
- Sergey Ioffe
- Max Welling
- Thomas Kipf
- Yann LeCun
Who trusts Shantanu Thakoor overall
- Yixin Liu
- Lirong Wu
- Namkyeong Lee
- Yaochen Xie
- Yonatan Belinkov
- Hengrui Zhang
- Shirui Pan
- Weihua Hu
- Xin Zhou
Papers from Shantanu Thakoor's perspective
Ordered by how strongly the researchers Shantanu Thakoor 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)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Counterfactual Credit Assignment in Model-Free Reinforcement Learning (2020) — 73 citations
- Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context (2024) — 3,818 citations
- Gemma 2: Improving Open Language Models at a Practical Size (2024) — 2,206 citations
- The Marabou Framework for Verification and Analysis of Deep Neural Networks (2019) — 622 citations
- Large-Scale Representation Learning on Graphs via Bootstrapping (2021) — 329 citations
- Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations
- Bootstrapped Representation Learning on Graphs (2021) — 315 citations
- Adversarial Examples for Natural Language Classification Problems (2018) — 114 citations