DeepPPA: A Multi-Parallel AdaBoost Library for Deep Learning – In this note, we describe a simple implementation of the popular DeepPPA – a Multi-Parallel AdaBoost Library. On the one hand, this library has been developed with the specific goal of building a powerful algorithm to solve difficult multi-task tasks. On the other hand, we also provide a simple algorithm which we have been using recently in PASCAL VOC.
We present a general framework for learning feature-weight normalization via stochastic gradient descent (SGD) for stochastic gradient models. By defining a framework for SGD, we provide an efficient algorithm for SGD. Using the SGD algorithm, our algorithm obtains significantly better performance than previous SGD algorithms on a variety of benchmark datasets including the KITTI dataset. We demonstrate the effectiveness of our SGD method using synthetic (up to 100 times faster) benchmarks. A synthetic benchmark includes datasets in the order of 20×100 for linear SGD, and up to 350×100 for stochastic gradient descent. We use synthetic benchmark datasets for comparison. Experimental results on all tested datasets show that our algorithm improves on state-of-the-art SGD algorithms.
Semantic Font Attribution Using Deep Learning
DeepPPA: A Multi-Parallel AdaBoost Library for Deep Learning
Generative Contextual Learning with Semantic Text
A Comprehensive Evaluation of Feature Weight Normalization TechniquesWe present a general framework for learning feature-weight normalization via stochastic gradient descent (SGD) for stochastic gradient models. By defining a framework for SGD, we provide an efficient algorithm for SGD. Using the SGD algorithm, our algorithm obtains significantly better performance than previous SGD algorithms on a variety of benchmark datasets including the KITTI dataset. We demonstrate the effectiveness of our SGD method using synthetic (up to 100 times faster) benchmarks. A synthetic benchmark includes datasets in the order of 20×100 for linear SGD, and up to 350×100 for stochastic gradient descent. We use synthetic benchmark datasets for comparison. Experimental results on all tested datasets show that our algorithm improves on state-of-the-art SGD algorithms.