S. Gadre

Computer Vision and Pattern Recognition · 2021–2024

A researcher in the Science corpus: 9 papers; trusts 811 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 367 citations into it
• Artificial Intelligence — 270 citations into it
• Representation & Self-Supervised Learning — 54 citations into it
• Aerospace Engineering — 47 citations into it
• Optimization & Training — 38 citations into it
• Robotics & Control — 32 citations into it
• Computational Mechanics — 28 citations into it
• Reinforcement Learning — 25 citations into it

Whom S. Gadre trusts overall

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

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

Who trusts S. Gadre overall

1. Apoorv Khandelwal
2. Chen Huang
3. Arjun Majumdar
4. Naoki Yokoyama
5. Yining Hong
6. Boyuan Chen
7. Krishna Murthy Jatavallabhula
8. Luca Weihs
9. Christoph Schuhmann
10. Sai H. Vemprala

Papers from S. Gadre's perspective

Ordered by how strongly the researchers S. Gadre 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. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
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. Going deeper with convolutions

Their papers

• Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time (2022) — 1,632 citations
• CLIP on Wheels: Zero-Shot Object Navigation as Object Localization and Exploration (2022) — 118 citations
• DataComp: In search of the next generation of multimodal datasets (2023) — 734 citations
• OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models (2023) — 642 citations
• Continuous Scene Representations for Embodied AI (2022) — 62 citations
• CoWs on Pasture: Baselines and Benchmarks for Language-Driven Zero-Shot Object Navigation (2022) — 324 citations
• DataComp-LM: In search of the next generation of training sets for language models (2024) — 374 citations
• Objaverse-XL: A Universe of 10M+ 3D Objects (2023) — 873 citations
• Act the Part: Learning Interaction Strategies for Articulated Object Part Discovery (2021) — 65 citations