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Found 5 papers, 2 papers with code
A Proposed Hierarchy of Deep Learning Tasks
As the pace of deep learning innovation accelerates, it becomes increasingly important to organize the space of problems by relative difficultly.
Deep Learning Scaling is Predictable, Empirically
As DL application domains grow, we would like a deeper understanding of the relationships between training set size, computational scale, and model accuracy improvements to advance the state-of-the-art.
A New Parallel Algorithm for Two-Pass Connected Component Labeling
In the second phase, we assign the final labels to the pixels.
Scalable Bayesian Optimization Using Deep Neural Networks
Bayesian optimization is an effective methodology for the global optimization of functions with expensive evaluations.
Ranked #156 on
Image Classification
on CIFAR-10
Parallel Maximum Clique Algorithms with Applications to Network Analysis and Storage
We propose a fast, parallel maximum clique algorithm for large sparse graphs that is designed to exploit characteristics of social and information networks.
Social and Information Networks Distributed, Parallel, and Cluster Computing Discrete Mathematics Data Structures and Algorithms Physics and Society 05C69 G.2.2
