Aditya Rawal

Artificial Intelligence · 2016–2022

A researcher in the Science corpus: 6 papers; trusts 262 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 190 citations into it; #12,530 of 35,345 by trust here, #6,229 by citations
• Computer Vision and Pattern Recognition — 106 citations into it
• Meta & Continual Learning — 48 citations into it
• Model Architectures — 48 citations into it
• Natural Language Processing — 37 citations into it
• Reinforcement Learning — 37 citations into it
• Representation & Self-Supervised Learning — 26 citations into it
• Optimization & Training — 19 citations into it

Whom Aditya Rawal trusts overall

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

1. J. Urgen Schmidhuber
2. Kenneth O. Stanley
3. Geoffrey E. Hinton
4. Alex Graves
5. Yoshua Bengio
6. Bram Bakker
7. Alex Krizhevsky
8. Sepp Hochreiter
9. Risto Miikkulainen
10. N. Schraudolph

Who trusts Aditya Rawal overall

1. Andrés Camero
2. A. Elsaid
3. Enrique Alba
4. Jamal Toutouh
5. Travis Desell
6. Andrea Soltoggio
7. Steven A. Benson
8. Fatima El Jamiy
9. Thomas Bäck
10. Alexander Ororbia

Papers from Aditya Rawal's perspective

Ordered by how strongly the researchers Aditya Rawal 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. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Evolving Deep Neural Networks (2017) — 962 citations
• Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions (2020) — 155 citations
• Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity (2018) — 106 citations
• Evolving Deep LSTM-based Memory Networks using an Information Maximization Objective (2016) — 47 citations
• Memory Efficient Continual Learning with Transformers (2022) — 70 citations
• Continual Learning with Transformers for Image Classification (2022) — 27 citations