Vineeth Nallure Balasubramanian

Artificial Intelligence · 2014–2022

A researcher in the Science corpus: 16 papers; trusts 1,858 researchers and is trusted by 54 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 508 citations into it; #4,592 of 35,345 by trust here, #5,059 by citations
• Computer Vision and Pattern Recognition — 478 citations into it; #3,805 of 21,575 by trust here, #3,489 by citations
• Representation & Self-Supervised Learning — 156 citations into it
• Meta & Continual Learning — 137 citations into it; #233 of 1,041 by trust here, #475 by citations
• Optimization & Training — 74 citations into it
• Model Architectures — 65 citations into it
• Generative Models — 48 citations into it
• Efficiency & Compression — 45 citations into it

Whom Vineeth Nallure Balasubramanian trusts overall

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

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

Who trusts Vineeth Nallure Balasubramanian overall

1. K. J. Joseph
2. Liyuan Wang
3. Lihua Lei
4. Arjun Ashok
5. Maxime Cauchois
6. Eyke Hüllermeier
7. Jorge Armando Mendez Mendez
8. Andrew Kyle Lampinen
9. Xingxing Zhang
10. Zhipeng Bao

Papers from Vineeth Nallure Balasubramanian's perspective

Ordered by how strongly the researchers Vineeth Nallure Balasubramanian 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. 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

• Towards Open World Object Detection (2021) — 621 citations
• Grad-CAM++: Generalized Gradient-Based Visual Explanations for Deep Convolutional Networks (2017) — 3,283 citations
• Charting the Right Manifold: Manifold Mixup for Few-shot Learning (2019) — 386 citations
• Deep Model Compression: Distilling Knowledge from Noisy Teachers (2016) — 192 citations
• Attentive Semantic Video Generation Using Captions (2017) — 77 citations
• VisDrone-DET2019: The Vision Meets Drone Object Detection in Image Challenge Results (2019) — 1,010 citations
• Zero-Shot Task Transfer (2019) — 54 citations
• Sync-DRAW: Automatic Video Generation using Deep Recurrent Attentive Architectures (2016) — 79 citations
• Class-Incremental Learning with Cross-Space Clustering and Controlled Transfer (2022) — 33 citations
• Spacing Loss for Discovering Novel Categories (2022) — 18 citations
• Novel Class Discovery without Forgetting (2022) — 61 citations
• Energy-based Latent Aligner for Incremental Learning (2022) — 57 citations
• Incremental Object Detection via Meta-Learning (2020) — 133 citations
• Meta-Consolidation for Continual Learning (2020) — 63 citations
• Neural Network Attributions: A Causal Perspective (2019) — 168 citations
• Conformal Prediction for Reliable Machine Learning: Theory, Adaptations and Applications (2014) — 304 citations