Probabilistic Graphical Models and Structured Prediction Project
CS6424/ECE6424 Spring 2016

Project Report

This page contains some of the papers submitted as final project reports for CS6424/ECE6424 Probabilistic Graphical Models and Structured Prediction in the spring semester of 2016.

The work presented here is done by students on short time constraints, so it may be preliminary or have inconclusive results. I encouraged the students to choose projects that complemented or integrated with their own research, so it is possible the project has continued since this report was submitted. If you are interested in the topic, I encourage you to contact the authors to see the latest developments on these ideas.

• Inference with Deep Neural Networks
  Bijaya Adhikari^*, William Ryan Doan^*, Jason Granstedt^*
• Predicting Graph Structure Using Local Information and Path-Based Measures in Networks: A Metric Learning Approach
  Subhodip Biswas, (Bert Huang)
• Priority Grafting: Prioritizing Edge Selection with Structure Mining
  Walid Chaabene, (Bert Huang)
• On Helping Stroke Rehabilitation with Faster R-CNN
  Jinwoo Choi
• Autonomous Exploration Of Benthic Environment Using Gaussian Process Filter
  Abhilash Chowdhary
• Consistency-Based Approximate Inference for Learning Graphical Models
  Shuangfei Fan^*, Sirui Yao^*, (Bert Huang)
• Enforcing Consistent Predictions in Visual Question Answering models
  Aroma Mahendru
• Efficient Training of MRFs with Latent Variables using Paired-Dual Learning
  Elaheh Raisi, (Bert Huang)