Sharad Vikram

Artificial Intelligence · 2018–2025

A researcher in the Science corpus: 6 papers; trusts 103 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 124 citations into it; #35,204 of 35,345 by trust here, #9,870 by citations
• Reinforcement Learning — 71 citations into it; #1,466 of 2,242 by trust here, #1,204 by citations
• Computer Vision and Pattern Recognition — 39 citations into it
• Control and Systems Engineering — 37 citations into it
• Probabilistic & Bayesian Methods — 35 citations into it
• Optimization & Training — 27 citations into it
• Representation & Self-Supervised Learning — 18 citations into it; #2,131 of 3,145 by trust here, #1,692 by citations
• Statistical and Nonlinear Physics — 13 citations into it

Whom Sharad Vikram trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Sergey Levine
4. Alex Krizhevsky
5. Alex Graves
6. Yoshua Bengio
7. Radford M. Neal
8. Yann LeCun
9. Max Welling
10. Kaiming He

Who trusts Sharad Vikram overall

1. Sergey Levine
2. Dibya Ghosh
3. Carlos Florensa
4. Carles Gelada
5. Nir Levine
6. Danijar Hafner
7. Alex X. Lee
8. Soroush Nasiriany
9. Abhishek Gupta
10. Pieter Abbeel

Papers from Sharad Vikram's perspective

Ordered by how strongly the researchers Sharad Vikram 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• SOLAR: Deep Structured Latent Representations for Model-Based Reinforcement Learning (2018) — 42 citations
• Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context (2024) — 3,818 citations
• What Are Bayesian Neural Network Posteriors Really Like? (2021) — 474 citations
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations
• How to pick the domain randomization parameters for sim-to-real transfer of reinforcement learning policies? (2019) — 60 citations
• SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning (2018) — 236 citations