Tired of Matlab? Lush is an easy-to-learn, open-source object-oriented programming language designed for researchers, experimenters, and engineers working in large-scale numerical and graphic applications. Lush combines three languages in one: a very simple to use, loosely-typed interpreted language, a strongly-typed compiled language with the same syntax, and the C language, which can be freely mixed with the other languages within a single source file, and even within a single function.
If you do research and development in signal processing, image processing, machine learning, computer vision, bio-informatics, data mining, statistics, or artificial intelligence , and feel limited by Matlab and other existing tools, Lush is for you. If you want a simple environment to experiment with graphics, video, and sound , Lush is for you. G-Scholar Profile. Contact Information. Working Paper. Pamphlets and opinions. Students and Postdocs.
Bragging Zone. Older Content. Quick Links. Computational and Biological Learning Lab. Talks and Tutorials. Deep Learning. Relational Regression. Mobile Robotics. The purpose of the LAGR project , funded by the US government, is to design vision and learning algorithms to allow mobile robots to navigate in complex outdoors environment solely from camera input.
Penn, Stanford. The government periodically runs competitions between the teams. The software from each team is loaded and run by the goverment team on their robot. The robot is given the GPS coordinates of a goal to which it must drive as fast as possible. The terrain is unknown in advance. The robot is run three times through the test course.
The software can use the knowledge acquired during the early runs to improve the performance on the latter runs. Energy-Based Models. Energy-Based Models EBMs capture dependencies between variables by associating a scalar energy to each configuration of the variables. Inference consists in clamping the value of observed variables and finding configurations of the remaining variables that minimize the energy. Learning consists in finding an energy function in which observed configurations of the variables are given lower energies than unobserved ones.
