A new model of the central tendency towards drift in synapses – The neural networks (NN) have recently shown remarkable potential to improve the prediction performance of deep neural networks (DNNs). However, most existing neural networks models can only deal with sparse networks. We make the challenge of learning sparse model to handle high-dimensional data more difficult. This paper addresses the problem by proposing an efficient neural network architecture for the purpose of high-dimensional data analysis using a sparse network. First, we extend the classical DNN approach of learning sparse data to the new sparse network architecture that adapts to a high-dimensional data set. Then we extend the model’s learning process using data from a single low-dimensional component into a multimodal network which can learn to predict a low-dimensional dimension that it can use to estimate the prediction accuracy. Finally, we conduct an experiment where high-dimensional data from a single CNN can be used to model a high-dimensional image. The empirical test data, generated in four dimensions, are shown to be different from the previous ones, showing that the new method consistently achieves similar or better performance than the previous one.
This paper presents a novel learning-based framework to identify the causal structure (i.e., the influence of several factors, like social, cultural and technical) in an individual’s performance. We propose a novel algorithm to recover the causal relation from data captured from different domains: a product of one domain, another product from another domain, and so on. Experiments using a public dataset of US adults show that, in comparison to other methods, our proposed framework outperforms state-of-the-art methods on a variety of benchmarks.
Towards Automatic Producing, Analytical and Streaming Data in Real-time
Robust Subspace Modeling with Multi-view Feature Space Representation
A new model of the central tendency towards drift in synapses
Deep Learning for Robotic Surgery Identification
Multi-modal Multi-domain Attention for Automatic Quality Assessment of Health ProductsThis paper presents a novel learning-based framework to identify the causal structure (i.e., the influence of several factors, like social, cultural and technical) in an individual’s performance. We propose a novel algorithm to recover the causal relation from data captured from different domains: a product of one domain, another product from another domain, and so on. Experiments using a public dataset of US adults show that, in comparison to other methods, our proposed framework outperforms state-of-the-art methods on a variety of benchmarks.
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