- Task-driven learning for model optimization and/or statistical inference.
- Novel architectures and algorithms for bi-level optimization and/or PDEs .
- Problem-specific deep architectures for solving model optimization and statistical inference.
- Integration of optimization-based, statistical learning, and inference models with deep learning models.
- Sparse representation motivated deep architectures.
- Structured regression motivated deep architectures.
- Conditional random forest motivated recurrent neural networks.
- Novel interpretative frameworks on the working mechanism of representative deep learning models.
- Theoretical analysis of deep learning models and algorithms: convergence, optimality, generalization, stability, and sensitivity analysis.
- Applications based on the above described models and algorithms: (1) image enhancement, restoration and synthesis; (2) optical flow, stereo matching, camera localization, and normal estimation; (3) visual recognition, detection, and segmentation, and scene understanding; (4) pattern classification, clustering and dimensionality reduction; (5) medical image analysis and other novel application domains.
Saturday, 13 May 2017
CFP: IEEE TNNLS Special Issue on Discriminative Learning for Model Optimization and Statistical Inference
Posted by IEEE CIS at 07:33