Vinay Kumar Verma

Artificial Intelligence · 2017–2024

A researcher in the Science corpus: 9 papers; trusts 972 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 242 citations into it; #9,485 of 35,345 by trust here, #9,870 by citations
• Computer Vision and Pattern Recognition — 226 citations into it
• Representation & Self-Supervised Learning — 91 citations into it
• Meta & Continual Learning — 82 citations into it; #1,021 of 1,041 by trust here, #432 by citations
• Radiology, Nuclear Medicine and Imaging — 49 citations into it; #668 of 3,265 by trust here, #1,970 by citations
• Generative Models — 33 citations into it
• Model Architectures — 25 citations into it
• Reinforcement Learning — 23 citations into it

Whom Vinay Kumar Verma 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. Karen Simonyan
6. Yoshua Bengio
7. Yann LeCun
8. Ilya Sutskever
9. Ruslan Salakhutdinov
10. Andrew Zisserman

Who trusts Vinay Kumar Verma overall

1. Yongqin Xian
2. Chandan Gautam
3. Ashish Mishra
4. Subhankar Ghosh
5. Chang-Bin Zhang
6. Piyush Rai
7. Dhanajit Brahma
8. Wenlin Wang
9. Jun Shu
10. Da-Wei Zhou

Papers from Vinay Kumar Verma's perspective

Ordered by how strongly the researchers Vinay Kumar Verma 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

• Generalized Zero-Shot Learning via Synthesized Examples (2017) — 488 citations
• A Simple Exponential Family Framework for Zero-Shot Learning (2017) — 206 citations
• Convolutional Prompting meets Language Models for Continual Learning (2024) — 45 citations
• Efficient Feature Transformations for Discriminative and Generative Continual Learning (2021) — 93 citations
• Continual Learning using a Bayesian Nonparametric Dictionary of Weight Factors (2021)
• 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
• Zero-Shot Learning via Class-Conditioned Deep Generative Models (2017) — 154 citations