Sachin Mehta
Computer Vision and Pattern Recognition · 2018–2023
A researcher in the Science corpus: 8 papers; trusts 1,521 researchers and is trusted by 44 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 325 citations into it; #4,159 of 21,575 by trust here, #5,507 by citations
- Artificial Intelligence — 257 citations into it; #5,030 of 35,345 by trust here, #7,657 by citations
- Model Architectures — 91 citations into it
- Efficiency & Compression — 81 citations into it
- Optimization & Training — 57 citations into it
- Evaluation & Benchmarks — 52 citations into it
- Representation & Self-Supervised Learning — 26 citations into it
- Media Technology — 17 citations into it
Whom Sachin Mehta trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Alex Krizhevsky
- Geoffrey E. Hinton
- Diederik P. Kingma
- Ross B. Girshick
- Christian Szegedy
- Ilya Sutskever
- Yoshua Bengio
- Yann LeCun
- Karen Simonyan
Who trusts Sachin Mehta overall
- Mohammad Rastegari
- Muhammad Maaz
- Gaurav Menghani
- Ao Wang
- Yanyu Li
- Xinyu Liu
- Jierun Chen
- Xiangtai Li
- Arthur Douillard
- Wenqiang Zhang
Papers from Sachin Mehta's perspective
Ordered by how strongly the researchers Sachin Mehta trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer (2021) — 2,383 citations
- ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation (2018) — 898 citations
- ESPNetv2: A Light-Weight, Power Efficient, and General Purpose Convolutional Neural Network (2018) — 473 citations
- CVNets: High Performance Library for Computer Vision (2022) — 30 citations
- Reinforce Data, Multiply Impact: Improved Model Accuracy and Robustness with Dataset Reinforcement (2023) — 16 citations
- TiC-CLIP: Continual Training of CLIP Models (2023) — 52 citations
- Separable Self-attention for Mobile Vision Transformers (2022) — 456 citations
- DeLighT: Deep and Light-weight Transformer (2021) — 99 citations