H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 59 Citations 66,130 143 World Ranking 1702 National Ranking 941

Research.com Recognitions

Awards & Achievements

2017 - Fellow of Alfred P. Sloan Foundation

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Computer vision

His primary scientific interests are in Artificial intelligence, Pattern recognition, Object detection, Convolutional neural network and Machine learning. His studies deal with areas such as Context, Computer vision and Natural language processing as well as Artificial intelligence. In his study, Simple and Algorithm is inextricably linked to Code, which falls within the broad field of Pattern recognition.

His Object detection study combines topics in areas such as Artificial neural network, Frame rate and Pascal. Ali Farhadi has researched Convolutional neural network in several fields, including Depth map, Binary number and Task. His study in Machine learning is interdisciplinary in nature, drawing from both Feature extraction and Image retrieval.

His most cited work include:

  • You Only Look Once: Unified, Real-Time Object Detection (8892 citations)
  • YOLO9000: Better, Faster, Stronger (4607 citations)
  • YOLOv3: An Incremental Improvement. (4443 citations)

What are the main themes of his work throughout his whole career to date?

Ali Farhadi spends much of his time researching Artificial intelligence, Human–computer interaction, Context, Machine learning and Natural language processing. His work deals with themes such as Computer vision and Pattern recognition, which intersect with Artificial intelligence. His study looks at the relationship between Pattern recognition and fields such as Object detection, as well as how they intersect with chemical problems.

His Human–computer interaction study integrates concerns from other disciplines, such as Embodied cognition, Code, Feature learning, Key and Reinforcement learning. His Context study deals with Inference intersecting with Benchmark and Sentence. Ali Farhadi interconnects Training set and State in the investigation of issues within Machine learning.

He most often published in these fields:

  • Artificial intelligence (73.58%)
  • Human–computer interaction (17.92%)
  • Context (19.34%)

What were the highlights of his more recent work (between 2019-2021)?

  • Human–computer interaction (17.92%)
  • Artificial intelligence (73.58%)
  • Code (15.09%)

In recent papers he was focusing on the following fields of study:

Human–computer interaction, Artificial intelligence, Code, Set and Embodied cognition are his primary areas of study. His work carried out in the field of Human–computer interaction brings together such families of science as Context, Point, Representation and Feature learning. He has included themes like Machine learning and Natural language processing in his Artificial intelligence study.

His work carried out in the field of Code brings together such families of science as Convolutional neural network, Heuristic and Pruning. As a member of one scientific family, Ali Farhadi mostly works in the field of Embodied cognition, focusing on Pascal and, on occasion, Robot. His work in Artificial neural network covers topics such as Algorithm which are related to areas like Convolution.

Between 2019 and 2021, his most popular works were:

  • Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping (115 citations)
  • Soft Threshold Weight Reparameterization for Learnable Sparsity (31 citations)
  • What’s Hidden in a Randomly Weighted Neural Network? (28 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Computer vision

Ali Farhadi mainly focuses on Artificial intelligence, Human–computer interaction, Embodied cognition, Code and Artificial neural network. As part of his studies on Artificial intelligence, Ali Farhadi often connects relevant areas like Natural language processing. The concepts of his Embodied cognition study are interwoven with issues in Robot and Pascal.

The Code study combines topics in areas such as Autonomous agent, Inference and Pruning. He interconnects Contrast and Pointwise in the investigation of issues within Artificial neural network. His Feature learning study is associated with Pattern recognition.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Top Publications

You Only Look Once: Unified, Real-Time Object Detection

Joseph Redmon;Santosh Divvala;Ross Girshick;Ali Farhadi.
computer vision and pattern recognition (2016)

15365 Citations

YOLOv3: An Incremental Improvement.

Joseph Redmon;Ali Farhadi.
arXiv: Computer Vision and Pattern Recognition (2018)

7920 Citations

YOLO9000: Better, Faster, Stronger

Joseph Redmon;Ali Farhadi.
computer vision and pattern recognition (2017)

7253 Citations

XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks

Mohammad Rastegari;Vicente Ordonez;Joseph Redmon;Ali Farhadi;Ali Farhadi.
european conference on computer vision (2016)

2627 Citations

Describing objects by their attributes

Ali Farhadi;Ian Endres;Derek Hoiem;David Forsyth.
computer vision and pattern recognition (2009)

1749 Citations

Unsupervised deep embedding for clustering analysis

Junyuan Xie;Ross Girshick;Ali Farhadi.
international conference on machine learning (2016)

1129 Citations

Every picture tells a story: generating sentences from images

Ali Farhadi;Mohsen Hejrati;Mohammad Amin Sadeghi;Peter Young.
european conference on computer vision (2010)

960 Citations

Bidirectional Attention Flow for Machine Comprehension

Min Joon Seo;Aniruddha Kembhavi;Ali Farhadi;Hannaneh Hajishirzi.
international conference on learning representations (2016)

879 Citations

Target-driven visual navigation in indoor scenes using deep reinforcement learning

Yuke Zhu;Roozbeh Mottaghi;Eric Kolve;Joseph J. Lim.
international conference on robotics and automation (2017)

645 Citations

Recognition using visual phrases

Mohammad Amin Sadeghi;Ali Farhadi.
computer vision and pattern recognition (2011)

452 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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