Binbin Lin

TL;DR: This study shows that the recognition accuracy of MNIST handwriting digits degrades from ~97 % to ~65 %, and proposes the mitigation strategies, which include the smart programming schemes for achieving linear weight update, a dummy column to eliminate the off-state current, and the use of multiple cells for each weight element to alleviate the impact of device variations.

TL;DR: A novel method for the cross media retrieval task, named Parallel Field Alignment Retrieval (PFAR), which integrates a manifold alignment framework from the perspective of vector fields, and can effectively preserve the metric of data manifolds and project their relationship into intermediate latent semantic spaces during the process of manifold alignment.

TL;DR: A novel read and write scheme is designed to accelerate the training process, which realizes fully parallel operations of the weighted sum and the weight update, and a set of reverse scaling rules is proposed on the resistive cross-point array to achieve high learning accuracy.

TL;DR: The proposed hardware consists of an array with resistive random access memory and CMOS peripheral circuits, which perform matrix-vector multiplication and dictionary update in a fully parallel fashion, at the speed that is independent of the matrix dimension.

TL;DR: A novel supervised sparse representation and dictionary learning framework based on stochastic coordinate coding (SCC) algorithm for task fMRI data analysis, in which certain brain networks are learning with known information such as pre‐defined temporal patterns and spatial network patterns, and at the same time other networks are learned automatically from data.