Multi-Distance Support Matrix Machine (MDSMM)
Real-world data such as digital images, MRI scans and electroencephalography signals are naturally represented as matrices with structural information. Most existing classifiers aim to capture these structures by regularizing the regression matrix to be low-rank or sparse. Some other methodologies introduce factorization technique to explore nonlinear relationships of matrix data in kernel space. In this paper, we propose a multi-distance support matrix machine (MDSMM), which provides a principled way of solving matrix classification problems. The multi-distance is introduced to capture the correlation within matrix data, by means of intrinsic information in rows and columns of input data. A complex hyperplane is established upon these values to separate distinct classes. We further study the generalization bounds for i.i.d. processes and non i.i.d. process based on both SVM and SMM classifiers. For typical hypothesis classes where matrix norms are constrained, MDSMM achieves a faster learning rate than traditional classifiers. We also provide a more general approach for samples without prior knowledge. We demonstrate the merits of the proposed method by conducting exhaustive experiments on both simulation study and a number of real-word datasets. …

Densely Supervised Grasp Detector (DSGD)
This paper presents Densely Supervised Grasp Detector (DSGD), a deep learning framework which combines CNN structures with layer-wise feature fusion and produces grasps and their confidence scores at different levels of the image hierarchy (i.e., global-, region-, and pixel-levels). Specifically, at the global-level, DSGD uses the entire image information to predict a grasp and its confidence score. At the region-level, DSGD uses a region proposal network to identify salient regions in the image and predicts a grasp for each salient region. At the pixel-level, DSGD uses a fully convolutional network and predicts a grasp and its confidence at every pixel. The grasp with the highest confidence score is selected as the output of DSGD. This selection from hierarchically generated grasp candidates overcomes limitations of the individual models. DSGD outperforms state-of-the-art methods on the Cornell grasp dataset in terms of grasp accuracy. Evaluation on a multi-object dataset and real-world robotic grasping experiments show that DSGD produces highly stable grasps on a set of unseen objects in new environments. It achieves 96% grasp detection accuracy and 90% robotic grasping success rate with real-time inference speed. …

Neural Block Sampling
Efficient Monte Carlo inference often requires manual construction of model-specific proposals. We propose an approach to automated proposal construction by training neural networks to provide fast approximations to block Gibbs conditionals. The learned proposals generalize to occurrences of common structural motifs both within a given model and across models, allowing for the construction of a library of learned inference primitives that can accelerate inference on unseen models with no model-specific training required. We explore several applications including open-universe Gaussian mixture models, in which our learned proposals outperform a hand-tuned sampler, and a real-world named entity recognition task, in which our sampler’s ability to escape local modes yields higher final F1 scores than single-site Gibbs. …

Knowledge Space Theory
Knowledge space theory by Doignon and Falmagne (1999) <doi:10.1007/978-3-642-58625-5> is a set- and order-theoretical framework which proposes mathematical formalisms to operationalize knowledge structures in a particular domain. …