Inferential Machine Learning: Towards Human-collaborative Vision and Language Models
Presenters
Ghassan AlRegib is currently the John and McCarty Chair Professor in the School of Electrical and Computer Engineering at the Georgia Institute of Technology. He received the ECE Outstanding Junior Faculty Member Award, in 2008 and the 2017 Denning Faculty Award for Global Engagement. His research group, the Omni Lab for Intelligent Visual Engineering and Science (OLIVES) works on research projects related to machine learning, image and video processing, image and video understanding, seismic interpretation, machine learning for ophthalmology, and video analytics. He has participated in several service activities within the IEEE. He served as the TP co-Chair for ICIP 2020. He is an IEEE Fellow.
Mohit Prabhushankar received his Ph.D. degree in electrical engineering from the Georgia Institute of Technology (Georgia Tech), Atlanta, Georgia, 30332, USA, in 2021. He is currently a Postdoctoral Research Fellow in the School of Electrical and Computer Engineering at the Georgia Institute of Technology in the Omni Lab for Intelligent Visual Engineering and Science (OLIVES). He is working in the fields of image processing, machine learning, active learning, healthcare, and robust and explainable AI. He is the recipient of the Best Paper award at ICIP 2019 and Top Viewed Special Session Paper Award at ICIP 2020. He is the recipient of the ECE Outstanding Graduate Teaching Award, the CSIP Research award, and of the Roger P Webb ECE Graduate Research Excellence award, all in 2022.
Program Schedule and Content
Recent Relevant Publications
- AlRegib, Ghassan, and Mohit Prabhushankar. “Explanatory Paradigms in Neural Networks: Towards relevant and contextual explanations.” IEEE Signal Processing Magazine 39.4 (2022): 59-72.
- M. Prabhushankar, and G. AlRegib, “Introspective Learning : A Two-Stage Approach for Inference in Neural Networks,” in Advances in Neural Information Processing Systems (NeurIPS), New Orleans, LA,, Nov. 29 – Dec. 1 2022.
- J. Lee, M. Prabhushankar, and G. AlRegib, “Gradient-Based Adversarial and Out-of-Distribution Detection,” in International Conference on Machine Learning (ICML) Workshop on New Frontiers in Adversarial Machine Learning, Baltimore, MD, Jul. 2022.
- M. Prabhushankar and G. AlRegib, “Extracting Causal Visual Features for Limited Label Classification,” IEEE International Conference on Image Processing (ICIP), Anchorage, AK, Sept 2021.
- J. Lee and G. AlRegib, “Open-Set Recognition with Gradient-Based Representations,” IEEE International Conference on Image Processing (ICIP), Anchorage, AK, submitted on Jan. 18 2021.
- G. Kwon, M. Prabhushankar, D. Temel, and G. AlRegib, “Backpropagated Gradient Representations for Anomaly Detection,” in Proceedings of the European Conference on Computer Vision (ECCV), SEC, Glasgow, Aug. 23-28 2020.
- M. Prabhushankar, G. Kwon, D. Temel, and G. AlRegib, “Contrastive Explanations in Neural Networks,” in IEEE International Conference on Image Processing (ICIP), Abu Dhabi, United Arab Emirates, Oct. 2020. (Top Viewed Special Session Paper Award)
- Y. Sun, M. Prabhushankar, and G. AlRegib, “Implicit Saliency in Deep Neural Networks,” in IEEE International Conference on Image Processing (ICIP), Abu Dhabi, United Arab Emirates, Oct. 2020.
- G. Kwon, M. Prabhushankar, D. Temel, and G. AlRegib, “Novelty Detection Through Model-Based Characterization of Neural Networks,” in IEEE International Conference on Image Processing (ICIP), Abu Dhabi, United Arab Emirates, Oct. 2020.
- J. Lee and G. AlRegib, “Gradients as a Measure of Uncertainty in Neural Networks,” in IEEE International Conference on Image Processing (ICIP), Abu Dhabi, United Arab Emirates, Oct. 2020.
- M. Prabhushankar*, G. Kwon*, D. Temel and G. AIRegib, “Distorted Representation Space Characterization Through Backpropagated Gradients,” 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan, 2019, pp. 2651-2655. (* : equal contribution, Best Paper Award (top 0.1%))
