Siddhant M. Jayakumar
Artificial Intelligence · 2018–2021
A researcher in the Science corpus: 11 papers; trusts 316 researchers and is trusted by 34 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 270 citations into it; #6,862 of 35,345 by trust here, #1,628 by citations
- Reinforcement Learning — 132 citations into it
- Computer Vision and Pattern Recognition — 121 citations into it; #7,916 of 21,575 by trust here, #5,287 by citations
- Model Architectures — 75 citations into it; #870 of 2,278 by trust here, #461 by citations
- Optimization & Training — 61 citations into it
- Natural Language Processing — 50 citations into it
- Cognitive Neuroscience — 27 citations into it
- Computational Theory and Mathematics — 25 citations into it
Whom Siddhant M. Jayakumar trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Yoshua Bengio
- Alex Graves
- Diederik P. Kingma
- Alex Krizhevsky
- Ilya Sutskever
- Kaiming He
- Yann LeCun
- Jimmy Ba
- Ronald J. Williams
Who trusts Siddhant M. Jayakumar overall
- Emilio Parisotto
- Ruslan Salakhutdinov
- Tri Dao
- Murray Shanahan
- Rahaf Aljundi
- Yi Tay
- Kalpesh Krishna
- Christos Kaplanis
- Richard He Bai
- Ofir Press
Papers from Siddhant M. Jayakumar's perspective
Ordered by how strongly the researchers Siddhant M. Jayakumar 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
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Scaling Language Models: Methods, Analysis & Insights from Training Gopher (2021) — 1,640 citations
- Compressive Transformers for Long-Range Sequence Modelling (2019) — 900 citations
- Stabilizing Transformers for Reinforcement Learning (2019) — 486 citations
- Memory-based Parameter Adaptation (2018) — 106 citations
- Mix&Match - Agent Curricula for Reinforcement Learning (2018) — 83 citations
- Information asymmetry in KL-regularized RL (2019) — 107 citations
- Adapting Auxiliary Losses Using Gradient Similarity (2018) — 187 citations
- Been There, Done That: Meta-Learning with Episodic Recall (2018) — 95 citations
- Distilling Policy Distillation (2019) — 179 citations
- Meta-learning of Sequential Strategies (2019) — 120 citations
- Multiplicative Interactions and Where to Find Them (2020) — 160 citations