Scalable Bayesian Learning using Conditional Mutual Information – A key issue in machine learning is in understanding how one can use large-scale datasets, such as web data, to improve their ability to improve a machine learning algorithm. In this paper, we present a method for building and deploying machine learning based machine learning algorithm algorithms for large-scale applications. Several machine learning algorithms such as convolutional recurrent neural networks or multi-layer recurrent networks are used. The main innovation of the proposed method is to use parallelized convolutional neural networks (CNNs) for training. Our method leverages the importance of parallelism (using a large number of GPUs) during training and fine-tuning the CNN. We also propose an effective method for constructing large-scale parallelized CNNs. We evaluate our method on real-world datasets from healthcare, sports, and social media. Experimental results show that the parallelization results provide the best performance compared to the single-layer training and fine-tuning strategies.
The paper presents a novel neural computational model combining deep learning with supervised learning. We propose a new model to capture discriminative temporal dynamics in a deep learning framework; by leveraging the structure of the recurrent network. The structure provides an efficient way of modeling the semantic domain, which makes the learning process extremely efficient. The model is evaluated on three challenging object detection benchmarks: VOT 2007-2012, VOT 2008-2010 and VOT 2017. The performance of the model compares favorably to both the baseline models and the state-of-the-art methods, as well as the recently proposed Recurrent Deep Network. In addition, the model is able to handle the semantic domain in a very light way. For instance, it outperforms the baseline model on several challenging object detection benchmark.
Unifying statistical and stylistic features in digital signature algorithms
Combining Multi-Dimensional and Multi-DimensionalLS Measurements for Unsupervised Classification
Scalable Bayesian Learning using Conditional Mutual Information
Evolving inhomogeneity in the presence of external noise using sparsity-based nonlinear adaptive interpolationThe paper presents a novel neural computational model combining deep learning with supervised learning. We propose a new model to capture discriminative temporal dynamics in a deep learning framework; by leveraging the structure of the recurrent network. The structure provides an efficient way of modeling the semantic domain, which makes the learning process extremely efficient. The model is evaluated on three challenging object detection benchmarks: VOT 2007-2012, VOT 2008-2010 and VOT 2017. The performance of the model compares favorably to both the baseline models and the state-of-the-art methods, as well as the recently proposed Recurrent Deep Network. In addition, the model is able to handle the semantic domain in a very light way. For instance, it outperforms the baseline model on several challenging object detection benchmark.