Mohammad Lotfollahi
Artificial Intelligence · 2017–2023
A researcher in the Science corpus: 6 papers; trusts 10 researchers and is trusted by 5 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 62 citations into it; #31,218 of 35,345 by trust here, #26,682 by citations
- Computer Vision and Pattern Recognition — 27 citations into it
- Computer Networks and Communications — 12 citations into it; #8,648 of 9,886 by trust here, #6,788 by citations
- Representation & Self-Supervised Learning — 12 citations into it
- Hardware and Architecture — 7 citations into it
- Optimization & Training — 6 citations into it
Whom Mohammad Lotfollahi trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yoshua Bengio
- Diederik P. Kingma
- Geoffrey E. Hinton
- Karen Simonyan
- Alex Krizhevsky
- Ilya Sutskever
- Andrew William Moore
- Yann LeCun
- Jimmy Ba
- Lutz Prechelt
Who trusts Mohammad Lotfollahi overall
- Shahbaz Rezaei
- Yusuf H. Roohani
- Chaoyun Zhang
- Qian Mao
- Giuseppe Aceto
- Daniel S. Berman
- Chang Liu
- Lukas Heumos
- Gang Xiong
- Xin Liu
Papers from Mohammad Lotfollahi's perspective
Ordered by how strongly the researchers Mohammad Lotfollahi 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)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- scGen predicts single-cell perturbation responses (2019) — 653 citations
- Squidpy: a scalable framework for spatial omics analysis (2022) — 911 citations
- Mapping single-cell data to reference atlases by transfer learning (2021) — 497 citations
- Predicting cellular responses to complex perturbations in high‐throughput screens (2023) — 316 citations
- A Python library for probabilistic analysis of single-cell omics data (2022) — 658 citations
- Deep packet: a novel approach for encrypted traffic classification using deep learning (2017) — 1,082 citations