Akhilesh Deepak Gotmare

Artificial Intelligence · 2018–2023

A researcher in the Science corpus: 6 papers; trusts 389 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 209 citations into it; #16,787 of 35,345 by trust here, #3,816 by citations
• Computer Vision and Pattern Recognition — 122 citations into it; #16,195 of 21,575 by trust here, #5,082 by citations
• Natural Language Processing — 93 citations into it
• Large Language Models — 48 citations into it
• Information Systems — 44 citations into it
• Multimodal Learning — 40 citations into it; #960 of 1,211 by trust here, #440 by citations
• Optimization & Training — 37 citations into it; #2,291 of 2,601 by trust here, #1,841 by citations
• Representation & Self-Supervised Learning — 32 citations into it; #2,694 of 3,145 by trust here, #939 by citations

Whom Akhilesh Deepak Gotmare trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Karen Simonyan
4. Alex Krizhevsky
5. Diederik P. Kingma
6. Kaiming He
7. Ilya Sutskever
8. Yann LeCun
9. Sepp Hochreiter
10. Ronald J. Williams

Who trusts Akhilesh Deepak Gotmare overall

1. Maithra Raghu
2. Thao Nguyen
3. Aniruddh Raghu
4. Ximing Lu
5. Liyuan Liu
6. Lei Yang
7. Sean Welleck
8. Junnan Li
9. Xiaobo Liang
10. Jing Xu

Papers from Akhilesh Deepak Gotmare's perspective

Ordered by how strongly the researchers Akhilesh Deepak Gotmare 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. Learning Multiple Layers of Features from Tiny Images

Their papers

• Align before Fuse: Vision and Language Representation Learning with Momentum Distillation (2021) — 2,847 citations
• GeDi: Generative Discriminator Guided Sequence Generation (2020) — 516 citations
• A Closer Look at Deep Learning Heuristics: Learning rate restarts, Warmup and Distillation (2018) — 319 citations
• CodeT5+: Open Code Large Language Models for Code Understanding and Generation (2023) — 742 citations
• CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning (2022) — 502 citations
• Using Mode Connectivity for Loss Landscape Analysis (2018) — 31 citations