Faster Dynamic Graph CNN: Faster Deep Learning on 3D Point Cloud Data

Dynamic Graph CNN for Learning on Point Clouds

Point clouds provide a flexible and scalable geometric representation suitable for countless applications in computer graphics; they also comprise the raw output of most 3D data acquisition devices. Hence, the design of intelligent computational models that act directly on point clouds is critical, especially when efficiency considerations or noise preclude the possibility of expensive denoisin...

Faster Deterministic Fully-Dynamic Graph Connectivity

We give new deterministic bounds for fully-dynamic graph connectivity. Our data structure supports updates (edge insertions/deletions) in O(log n/ log logn) amortized time and connectivity queries in O(log n/ log logn) worst-case time, where n is the number of vertices of the graph. This improves the deterministic data structures of Holm, de Lichtenberg, and Thorup (STOC 1998, J.ACM 2001) and T...

Vehicle Detection Using Alex Net and Faster R-CNN Deep Learning Models: A Comparative Study

Faster Learning for Dynamic Recurrent Backpropagation

The backpropagation learning algorithm for feedforward networks (Rumelhart et al. 1986) has recently been generalized to recurrent networks (Pineda 1989). The algorithm has been further generalized by Pearlmutter (1989) to recurrent networks that produce time-dependent trajectories. The latter method requires much more training time than the feedforward or static recurrent algorithms. Furthermo...

500+ Times Faster Than Deep Learning (A Case Study Exploring Faster Methods for Text Mining StackOverflow)

Deep learning methods are useful for high-dimensional data and are becoming widely used in many areas of so‰ware engineering. Deep learners utilizes extensive computational power and can take a long time to train– making it dicult to widely validate and repeat and improve their results. Further, they are not the best solution in all domains. For example, recent results show that for €nding rel...