Optimization & Theory

A Convex Regularizer for Reducing Color Artifact in Color Image Recovery CVPR 2013 Adaptive dropout for training deep neural networks NeurIPS 2013 A General Iterative Shrinkage and Thresholding Algorithm for Non-convex Regularized Optimization Problems ICML 2013 Beyond Hard Negative Mining: Efficient Detector Learning via Block-Circulant Decomposition ICCV 2013 BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables NeurIPS 2013 Blind Calibration in Compressed Sensing using Message Passing Algorithms NeurIPS 2013 Blind Deconvolution of Widefield Fluorescence Microscopic Data by Regularization of the Optical Transfer Function (OTF) CVPR 2013 Convex Two-Layer Modeling NeurIPS 2013 Deep content-based music recommendation NeurIPS 2013 Deformable Graph Matching CVPR 2013 Depth Super Resolution by Rigid Body Self-Similarity in 3D CVPR 2013 Dirty Statistical Models NeurIPS 2013 Dropout Training as Adaptive Regularization NeurIPS 2013 Dynamic Structured Model Selection ICCV 2013 Estimating LASSO Risk and Noise Level NeurIPS 2013 Exact and Stable Recovery of Pairwise Interaction Tensors NeurIPS 2013 Fast, Accurate Detection of 100,000 Object Classes on a Single Machine CVPR 2013 Fast algorithms for sparse principal component analysis based on Rayleigh quotient iteration ICML 2013 Faster Ridge Regression via the Subsampled Randomized Hadamard Transform NeurIPS 2013 Fast Template Evaluation with Vector Quantization NeurIPS 2013 Generalizing Analytic Shrinkage for Arbitrary Covariance Structures NeurIPS 2013 Go-ICP: Solving 3D Registration Efficiently and Globally Optimally ICCV 2013 Groupwise Registration via Graph Shrinkage on the Image Manifold CVPR 2013 Guaranteed Sparse Recovery under Linear Transformation ICML 2013 Learning Separable Filters CVPR 2013