The scientist’s investigation covers issues in Artificial intelligence, Robot, Robotics, Control engineering and Evolutionary algorithm. His Convolutional neural network study, which is part of a larger body of work in Artificial intelligence, is frequently linked to Field, bridging the gap between disciplines. His work on Soft robotics as part of general Robot study is frequently connected to Forward locomotion, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
Hod Lipson interconnects Rigidity, Distributed computing, Adaptive system and Self-reconfiguring modular robot in the investigation of issues within Robotics. His work in Control engineering addresses subjects such as Mobile robot, which are connected to disciplines such as Implementation and Legged robot. The various areas that Hod Lipson examines in his Evolutionary algorithm study include Evolutionary computation, Photonic integrated circuit and Optics.
His primary areas of study are Artificial intelligence, Robot, Robotics, Evolutionary algorithm and Machine learning. His Artificial intelligence study frequently draws parallels with other fields, such as Computer vision. His Robot research is multidisciplinary, incorporating elements of Control engineering, Control theory and Human–computer interaction.
His Evolutionary algorithm study frequently draws connections between adjacent fields such as Symbolic regression. His Object research incorporates themes from Sketch and Computer graphics.
His scientific interests lie mostly in Artificial intelligence, Robot, Artificial neural network, Composite material and Laser. Hod Lipson has included themes like Machine learning and Pattern recognition in his Artificial intelligence study. His work carried out in the field of Machine learning brings together such families of science as Point and Domain knowledge.
The concepts of his Robot study are interwoven with issues in Distributed computing, Task, Task and Human–computer interaction. While the research belongs to areas of Artificial neural network, Hod Lipson spends his time largely on the problem of Theoretical computer science, intersecting his research to questions surrounding Training set, Embedding, Word, Face and Generative model. His Composite material research focuses on subjects like Actuator, which are linked to Silicone.
Hod Lipson focuses on Artificial intelligence, Deep learning, Robot, Composite material and Plant disease. The Artificial intelligence study combines topics in areas such as Machine learning, Drone and Computer vision. Hod Lipson has included themes like Artificial neural network, Convolutional neural network, Blight and Test set in his Deep learning study.
His research in the fields of Robotics overlaps with other disciplines such as Particle. His Robotics research integrates issues from Control engineering, Control and Position. His Composite material study integrates concerns from other disciplines, such as Characterization, Actuator and Laser power scaling.
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.
How transferable are features in deep neural networks
Jason Yosinski;Jeff Clune;Yoshua Bengio;Hod Lipson.
neural information processing systems (2014)
Distilling Free-Form Natural Laws from Experimental Data
Michael Schmidt;Hod Lipson.
Fabricated: The New World of 3D Printing
Hod Lipson;Melba Kurman.
Understanding Neural Networks Through Deep Visualization
Jason Yosinski;Jeff Clune;Anh Mai Nguyen;Thomas J. Fuchs.
arXiv: Computer Vision and Pattern Recognition (2015)
Automatic design and manufacture of robotic lifeforms
Hod Lipson;Jordan B. Pollack.
Universal robotic gripper based on the jamming of granular material
Eric Brown;Nicholas Rodenberg;John Amend;Annan Mozeika.
Proceedings of the National Academy of Sciences of the United States of America (2010)
Modular Self-Reconfigurable Robot Systems [Grand Challenges of Robotics]
M. Yim;Wei-Min Shen;B. Salemi;D. Rus.
IEEE Robotics & Automation Magazine (2007)
Resilient machines through continuous self-modeling.
Josh Bongard;Victor Zykov;Hod Lipson.
The evolutionary origins of modularity
Jeff Clune;Jeff Clune;Jean-Baptiste Mouret;Hod Lipson.
Proceedings of The Royal Society B: Biological Sciences (2013)
Rapid 3D printing of anatomically accurate and mechanically heterogeneous aortic valve hydrogel scaffolds
L A Hockaday;K H Kang;N W Colangelo;P Y C Cheung.
Profile was last updated on December 6th, 2021.
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The ranking h-index is inferred from publications deemed to belong to the considered discipline.
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