Fartash Faghri
Artificial Intelligence · 2015–2024
A researcher in the Science corpus: 8 papers; trusts 628 researchers and is trusted by 37 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 193 citations into it; #9,135 of 35,345 by trust here, #6,014 by citations
- Computer Vision and Pattern Recognition — 184 citations into it; #6,520 of 21,575 by trust here, #5,987 by citations
- Safety, Robustness & Interpretability — 45 citations into it; #624 of 2,251 by trust here, #404 by citations
- Multimodal Learning — 38 citations into it; #391 of 1,211 by trust here, #411 by citations
- Representation & Self-Supervised Learning — 33 citations into it; #1,274 of 3,145 by trust here, #885 by citations
- Optimization & Training — 30 citations into it
- Model Architectures — 23 citations into it
- Natural Language Processing — 16 citations into it
Whom Fartash Faghri trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Alex Krizhevsky
- Diederik P. Kingma
- Geoffrey E. Hinton
- Yoshua Bengio
- Karen Simonyan
- Ross B. Girshick
- Jimmy Ba
- Ilya Sutskever
- Yann LeCun
Who trusts Fartash Faghri overall
- Kurt Shuster
- Freda Shi
- Niluthpol Chowdhury Mithun
- Hongchen Tan
- Gen Li
- Douwe Kiela
- Jianwei Yang
- Samuel Humeau
- Florian Tramèr
- Jiayuan Mao
Papers from Fartash Faghri's perspective
Ordered by how strongly the researchers Fartash Faghri 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
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- VSE++: Improved Visual-Semantic Embeddings (2017) — 189 citations
- Adversarial Manipulation of Deep Representations (2015) — 297 citations
- Technical Report on the CleverHans v2.1.0 Adversarial Examples Library (2016) — 544 citations
- DataComp-LM: In search of the next generation of training sets for language models (2024) — 374 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
- Adversarial Spheres (2018) — 358 citations
- VSE++: Improving Visual-Semantic Embeddings with Hard Negatives (2017) — 1,393 citations