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Found 9 papers, 0 papers with code
Word2rate: training and evaluating multiple word embeddings as statistical transitions
We used a modified version of the negative sampling objective for our context words, modelling the context embeddings as a Taylor series of rate matrices.
Dependently Typed Knowledge Graphs
Reasoning over knowledge graphs is traditionally built upon a hierarchy of languages in the Semantic Web Stack.
Biologically Plausible Sequence Learning with Spiking Neural Networks
Motivated by the celebrated discrete-time model of nervous activity outlined by McCulloch and Pitts in 1943, we propose a novel continuous-time model, the McCulloch-Pitts network (MPN), for sequence learning in spiking neural networks.
Variational Probability Flow for Biologically Plausible Training of Deep Neural Networks
The quest for biologically plausible deep learning is driven, not just by the desire to explain experimentally-observed properties of biological neural networks, but also by the hope of discovering more efficient methods for training artificial networks.
Mobile Big Data Analytics Using Deep Learning and Apache Spark
The proliferation of mobile devices, such as smartphones and Internet of Things (IoT) gadgets, results in the recent mobile big data (MBD) era.
Deep Activity Recognition Models with Triaxial Accelerometers
Despite the widespread installation of accelerometers in almost all mobile phones and wearable devices, activity recognition using accelerometers is still immature due to the poor recognition accuracy of existing recognition methods and the scarcity of labeled training data.
Toward a Robust Sparse Data Representation for Wireless Sensor Networks
Our contributions that address three major issues include: 1) an effective method that extracts population sparsity of the data, 2) a sparsity ratio guarantee scheme, and 3) a customized learning algorithm of the sparsifying dictionary.
Marginal likelihood and model selection for Gaussian latent tree and forest models
We clarify how in this case real log-canonical thresholds can be computed using polyhedral geometry, and we show how to apply the general theory to the Laplace integrals associated with Gaussian latent tree and forest models.
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs).
