Prasanna Balaprakash

Artificial Intelligence · 2010–2021

A researcher in the Science corpus: 3 papers; trusts 83 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 41 citations into it; #12,350 of 35,345 by trust here, #19,873 by citations
• Computer Vision and Pattern Recognition — 23 citations into it; #19,750 of 21,575 by trust here, #20,756 by citations
• Meta & Continual Learning — 23 citations into it
• Statistical and Nonlinear Physics — 17 citations into it
• Representation & Self-Supervised Learning — 12 citations into it
• Statistics and Probability — 12 citations into it
• Computational Mechanics — 9 citations into it
• Optimization & Training — 8 citations into it

Whom Prasanna Balaprakash trusts overall

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

1. Longxin Lin
2. Geoffrey E. Hinton
3. Victor M. Becerra
4. Alex Krizhevsky
5. Diederik P. Kingma
6. Yoshua Bengio
7. Sepp Hochreiter
8. Mehrdad Farajtabar
9. Yann LeCun
10. John C. Duchi

Who trusts Prasanna Balaprakash overall

1. Frank Hutter
2. Manuel López-Ibáñez
3. Holger H. Hoos
4. Philipp Probst
5. Marc Claesen

Papers from Prasanna Balaprakash's perspective

Ordered by how strongly the researchers Prasanna Balaprakash trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Formalizing the Generalization-Forgetting Trade-off in Continual Learning (2021) — 41 citations
• Reduced-order modeling of advection-dominated systems with recurrent neural networks and convolutional autoencoders (2020) — 319 citations
• F-Race and Iterated F-Race: An Overview (2010) — 455 citations