Hod Lipson

Columbia University
0000-0003-0769-4618
https://www.hodlipson.com/
Publications343
h-index67
Citations25,365
Highly Influential Citations1,223
Author pages are created from data sourced from our academic publisher partnerships and public sources.

Recommended Authors

  • J. Bongard
  • 218 Publications • 6,744 Citations
  • D. Rus
  • 860 Publications • 41,774 Citations
  • J. Pollack
  • 403 Publications • 14,801 Citations
  • J. Clune
  • 117 Publications • 16,927 Citations
  • Publications
  • Influence
How transferable are features in deep neural networks?
TLDR
This paper quantifies the generality versus specificity of neurons in each layer of a deep convolutional neural network and reports a few surprising results, including that initializing a network with transferred features from almost any number of layers can produce a boost to generalization that lingers even after fine-tuning to the target dataset.
Distilling Free-Form Natural Laws from Experimental Data
TLDR
This work proposes a principle for the identification of nontriviality, and demonstrated this approach by automatically searching motion-tracking data captured from various physical systems, ranging from simple harmonic oscillators to chaotic double-pendula, and discovered Hamiltonians, Lagrangians, and other laws of geometric and momentum conservation.
Understanding Neural Networks Through Deep Visualization
TLDR
This work introduces several new regularization methods that combine to produce qualitatively clearer, more interpretable visualizations of convolutional neural networks.
Fabricated: The New World of 3D Printing
Fabricated tells the story of 3D printers, humble manufacturing machines that are bursting out of the factory and into homes, businesses, schools, kitchens, hospitals, even the fashion catwalk.The…
Resilient Machines Through Continuous Self-Modeling
TLDR
A robot is described that can recover from change autonomously, through continuous self-modeling, and this concept may help develop more robust machines and shed light on self- modeling in animals.
The evolutionary origins of modularity
TLDR
It is demonstrated that the ubiquitous, direct selection pressure to reduce the cost of connections between network nodes causes the emergence of modular networks.
Modular Self-Reconfigurable Robot Systems [Grand Challenges of Robotics]
TLDR
Several of the key directions for the future of modular self-reconfigurable robotic systems, including the design, fabrication, motion planning, and control of autonomous kinematic machines with variable morphology are shown.
Optimization-based reconstruction of a 3D object from a single freehand line drawing
TLDR
This paper describes an optimization-based algorithm for reconstructing a 3D model from a single, inaccurate, 2D edge-vertex graph that supports a wide scope of general objects containing flat and cylindrical faces.
Universal robotic gripper based on the jamming of granular material
TLDR
It is shown that volume changes of less than 0.5% suffice to grip objects reliably and hold them with forces exceeding many times their weight, and opens up new possibilities for the design of simple, yet highly adaptive systems that excel at fast gripping of complex objects.
Automated reverse engineering of nonlinear dynamical systems
  • J. Bongard, Hod Lipson
  • Computer Science, Medicine
    Proceedings of the National Academy of Sciences
  • 12 June 2007
TLDR
This work introduces for the first time a method that can automatically generate symbolic equations for a nonlinear coupled dynamical system directly from time series data, applicable to any system that can be described using sets of ordinary nonlinear differential equations.
...
1
2
3
4
5
...