Vedanuj Goswami

Artificial Intelligence · 2019–2024

A researcher in the Science corpus: 12 papers; trusts 1,189 researchers and is trusted by 40 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 419 citations into it; #9,864 of 35,345 by trust here, #1,325 by citations
• Computer Vision and Pattern Recognition — 346 citations into it; #4,747 of 21,575 by trust here, #2,926 by citations
• Natural Language Processing — 179 citations into it
• Multimodal Learning — 141 citations into it; #334 of 1,211 by trust here, #205 by citations
• Evaluation & Benchmarks — 98 citations into it
• Large Language Models — 75 citations into it; #680 of 799 by trust here, #149 by citations
• Model Architectures — 46 citations into it
• Representation & Self-Supervised Learning — 42 citations into it

Whom Vedanuj Goswami trusts overall

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

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

Who trusts Vedanuj Goswami overall

1. Ronghang Hu
2. Linjie Li
3. Yen-Chun Chen
4. Jiasen Lu
5. Zhe Gan
6. Kurt Shuster
7. Jaemin Cho
8. Soravit Changpinyo
9. Amita Kamath
10. Zhu Zhang

Papers from Vedanuj Goswami's perspective

Ordered by how strongly the researchers Vedanuj Goswami 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. Long Short-Term Memory
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

• 12-in-1: Multi-Task Vision and Language Representation Learning (2019) — 509 citations
• Llama 2: Open Foundation and Fine-Tuned Chat Models (2023) — 17,670 citations
• FLAVA: A Foundational Language And Vision Alignment Model (2021) — 987 citations
• The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes (2020) — 939 citations
• Are we pretraining it right? Digging deeper into visio-linguistic pretraining (2020) — 48 citations
• Tricks for Training Sparse Translation Models (2021) — 23 citations
• No Language Left Behind: Scaling Human-Centered Machine Translation (2022) — 1,884 citations
• The Llama 3 Herd of Models (2024) — 17,085 citations
• When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards (2024) — 153 citations
• MoVie: Revisiting Modulated Convolutions for Visual Counting and Beyond (2021) — 12 citations
• Creative Sketch Generation (2020) — 93 citations
• Only Time Can Tell: Discovering Temporal Data for Temporal Modeling (2019) — 98 citations