MIL - The University of Tennessee
the university of tennessee machine intelligence lab
  
Danny Budik, M.Sc. EE
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Research Reinforcement Learning and Approximate Dynamic Programming 
  
  Neural Networks
  My research focused on FPGA-based architectures for large-scale recurrent neural networks. In particular, I studied real-time learning algorithms that lend themselves to hardware implementation on FPGA platforms.
  
Selected Publications
  
[1]     D. Budik, I. Elhanany, "TRTRL: A Localized Resource-Efficient Learning Algorithm for Recurrent Neural Networks," 2006 IEEE International Midwest Symposium on Circuits & Systems (MWSCAS), San Juan, Puerto Rico, August, 2006 [pdf]