We consider the problem of sequential estimation unknowns state-space and deep models that include functions latent processes models. The proposed approach relies on Gaussian are implemented via random feature-based processes. In these models, we have two sets unknowns, highly nonlinear (the values processes) conditionally linear constant parameters processes). present a method based particle f...

In this paper, we study discriminatively trained deep convolutional networks for the task of visual tracking. Our tracker utilizes both motion and appearance features extracted from a pre-trained dual stream deep convolution network. By using optical flow and deep networks to implement a dual appearance and motion stream to inform tracking, our tracker outperforms current state of the art track...

The suggestion is advanced that the remarkably low static stability of Antarctic surface waters may arise from a feedback loop involving global deep-water temperatures. If deep-water temperatures are too warm, this promotes Antarctic convection, thereby strengthening the inflow of Antarctic Bottom Water into the ocean interior and cooling the deep ocean. If deep waters are too cold, this promot...

Nowadays, deep learning and convolutional neural networks (CNNs) have become widespread tools in many biomedical engineering studies. CNN is an end-to-end tool which makes processing procedure integrated, but in some situations, this processing tool requires to be fused with machine learning methods to be more accurate. In this paper, a hybrid approach based on deep features extracted from Wave...

Since the advent of deep learning, it has been used to solve various problems using many different architectures. The application of such deep architectures to auditory data is also not uncommon. However, these architectures do not always adequately consider the temporal dependencies in data. We thus propose a new generic architecture called the Deep Belief Network Bidirectional Long ShortTerm ...

Recently, studies on deep Reservoir Computing (RC) highlighted the role of layering in deep recurrent neural networks (RNNs). In this paper, the use of linear recurrent units allows us to bring more evidence on the intrinsic hierarchical temporal representation in deep RNNs through frequency analysis applied to the state signals. The potentiality of our approach is assessed on the class of Mult...

Rechargeable lithium-ion batteries are currently the most viable option for energy storage systems in electric vehicle (EV) applications due to their high specific energy, falling costs, and acceptable cycle life. However, accurately predicting parameters of complex, nonlinear battery remains challenging, given diverse aging mechanisms, cell-to-cell variations, dynamic operating conditions. The...

Deep kernel learning combines the non-parametric flexibility of kernel methods with the inductive biases of deep learning architectures. We propose a novel deep kernel learning model and stochastic variational inference procedure which generalizes deep kernel learning approaches to enable classification, multi-task learning, additive covariance structures, and stochastic gradient training. Spec...