Yash Goyal

Computer Vision and Pattern Recognition · 2015–2022

A researcher in the Science corpus: 9 papers; trusts 827 researchers and is trusted by 1,036 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 246 citations into it; #1,329 of 21,575 by trust here, #1,034 by citations
• Artificial Intelligence — 192 citations into it; #1,666 of 35,345 by trust here, #1,433 by citations
• Multimodal Learning — 101 citations into it; #106 of 1,211 by trust here, #112 by citations
• Natural Language Processing — 62 citations into it; #2,807 of 5,688 by trust here, #2,243 by citations
• Safety, Robustness & Interpretability — 30 citations into it; #1,150 of 2,251 by trust here, #1,290 by citations
• Evaluation & Benchmarks — 29 citations into it
• Model Architectures — 20 citations into it
• Large Language Models — 18 citations into it

Whom Yash Goyal trusts overall

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

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

Who trusts Yash Goyal overall

1. Ronghang Hu
2. Jaemin Cho
3. Zhe Gan
4. Drew A. Hudson
5. Damien Teney
6. Mohit Bansal
7. Quanshi Zhang
8. Zhou Yu
9. Linjie Li
10. Devi Parikh

Papers from Yash Goyal's perspective

Ordered by how strongly the researchers Yash Goyal 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
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

• Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering (2016) — 4,360 citations
• Yin and Yang: Balancing and Answering Binary Visual Questions (2015) — 373 citations
• CloudCV: Large-Scale Distributed Computer Vision as a Cloud Service (2015) — 51 citations
• Towards Transparent AI Systems: Interpreting Visual Question Answering Models (2016) — 76 citations
• Counterfactual Visual Explanations (2019) — 596 citations
• Explaining Classifiers with Causal Concept Effect (CaCE) (2019) — 213 citations
• Resolving Language and Vision Ambiguities Together: Joint Segmentation & Prepositional Attachment Resolution in Captioned Scenes (2016) — 31 citations
• MAPL: Parameter-Efficient Adaptation of Unimodal Pre-Trained Models for Vision-Language Few-Shot Prompting (2022) — 59 citations
• Interpreting Visual Question Answering Models (2016) — 12 citations