Piyush Rai

Artificial Intelligence · 2011–2022

A researcher in the Science corpus: 17 papers; trusts 1,418 researchers and is trusted by 53 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 364 citations into it; #5,961 of 35,345 by trust here, #6,422 by citations
• Computer Vision and Pattern Recognition — 323 citations into it; #9,257 of 21,575 by trust here, #9,563 by citations
• Representation & Self-Supervised Learning — 153 citations into it
• Meta & Continual Learning — 113 citations into it; #242 of 1,041 by trust here, #253 by citations
• Radiology, Nuclear Medicine and Imaging — 69 citations into it; #384 of 3,265 by trust here, #1,136 by citations
• Generative Models — 39 citations into it
• Optimization & Training — 32 citations into it
• Reinforcement Learning — 29 citations into it; #980 of 2,242 by trust here, #1,371 by citations

Whom Piyush Rai trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Kaiming He
5. Yoshua Bengio
6. Yann LeCun
7. Karen Simonyan
8. Ilya Sutskever
9. Ross B. Girshick
10. Ruslan Salakhutdinov

Who trusts Piyush Rai overall

1. Yu Zhang
2. Qiang Yang
3. Feiping Nie
4. Mohammad Rostami
5. Vinay Kumar Verma
6. Mingxia Liu
7. Yongxin Yang
8. Da-Wei Zhou
9. André Ricardo Gonçalves
10. Yongqin Xian

Papers from Piyush Rai's perspective

Ordered by how strongly the researchers Piyush Rai 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. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Flexible Modeling of Latent Task Structures in Multitask Learning (2012) — 53 citations
• Generalized Zero-Shot Learning via Synthesized Examples (2017) — 488 citations
• Deep Generative Models for Relational Data with Side Information (2017) — 12 citations
• A Simple Exponential Family Framework for Zero-Shot Learning (2017) — 206 citations
• Spacing Loss for Discovering Novel Categories (2022) — 18 citations
• Novel Class Discovery without Forgetting (2022) — 61 citations
• Few-Shot Lifelong Learning (2021) — 159 citations
• Efficient Feature Transformations for Discriminative and Generative Continual Learning (2021) — 93 citations
• Rectification-based Knowledge Retention for Continual Learning (2021) — 51 citations
• Hypernetworks for Continual Semi-Supervised Learning (2021) — 17 citations
• Calibrating CNNs for Lifelong Learning (2020) — 78 citations
• A Meta-Learning Framework for Generalized Zero-Shot Learning (2019) — 19 citations
• Bayesian Structure Adaptation for Continual Learning (2019) — 35 citations
• Zero-Shot Learning via Class-Conditioned Deep Generative Models (2017) — 154 citations
• Simultaneously Leveraging Output and Task Structures for Multiple-Output Regression (2012) — 71 citations
• Online Learning of Multiple Tasks and Their Relationships (2011) — 132 citations
• Co-regularized Multi-view Spectral Clustering (2011) — 1,307 citations