What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Operating system
Abhinav Gupta mainly focuses on Artificial intelligence, Machine learning, Object detection, Pattern recognition and Object.
His Artificial intelligence study frequently links to other fields, such as Computer vision.
He combines subjects such as Robot, Pascal and Training set with his study of Machine learning.
His Object detection study combines topics in areas such as Semi-supervised learning, Feature extraction and Knowledge acquisition.
The various areas that Abhinav Gupta examines in his Pattern recognition study include Artificial neural network, Surface, Normal and Cluster analysis.
His Minimum bounding box study in the realm of Object connects with subjects such as Hollywood.
His most cited work include:
- Non-local Neural Networks (2476 citations)
- Unsupervised Visual Representation Learning by Context Prediction (1106 citations)
- The Stanford Dash multiprocessor (909 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Artificial intelligence, Machine learning, Pattern recognition, Computer vision and Task.
His Image, Object, Object detection, Representation and Artificial neural network investigations are all subjects of Artificial intelligence research.
His Object detection research incorporates themes from Pascal and Feature learning.
His studies in Machine learning integrate themes in fields like Contextual image classification, Training set and Inference.
Many of his studies involve connections with topics such as Visualization and Pattern recognition.
His study focuses on the intersection of Task and fields such as Human–computer interaction with connections in the field of Imitation learning and Set.
He most often published in these fields:
- Artificial intelligence (66.78%)
- Machine learning (22.03%)
- Pattern recognition (18.53%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (66.78%)
- Human–computer interaction (11.54%)
- Reinforcement learning (11.19%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Artificial intelligence, Human–computer interaction, Reinforcement learning, Image and Robot.
His research integrates issues of Frame, Structure and Computer vision in his study of Artificial intelligence.
His work in Human–computer interaction tackles topics such as Task which are related to areas like Set, Teleoperation and Structure from motion.
His Image research is multidisciplinary, incorporating perspectives in Pixel, Knowledge graph, Object, Variety and Pattern recognition.
His Object research is multidisciplinary, relying on both Representation and Information retrieval.
His Robot study incorporates themes from Artificial neural network, Imitation, Baseline and Transformer.
Between 2019 and 2021, his most popular works were:
- Discovering Motor Programs by Recomposing Demonstrations (11 citations)
- Implicit Mesh Reconstruction from Unannotated Image Collections (9 citations)
- Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement Learning (7 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Operating system
Abhinav Gupta spends much of his time researching Artificial intelligence, Robot, Reinforcement learning, Control and Scaling.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Structure, Surface and Pattern recognition.
His Robot research integrates issues from Imitation and Structure from motion.
The study incorporates disciplines such as Motor primitives, Theoretical computer science, Limit and Decomposition in addition to Reinforcement learning.
His Control research incorporates elements of Machine learning, Latent variable, Frame and Inference.
His Scaling research is multidisciplinary, incorporating elements of Margin, Adaptability, Curriculum and Evolutionary learning.
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.
