LSTM Fully Convolutional Networks for Time Series Classification [PDF]
IEEE Access, 2017 Fully convolutional neural networks (FCNs) have been shown to achieve the state-of-the-art performance on the task of classifying time series sequences.
Fazle Karim +3 more
semanticscholar +1 more source
A Short-Term Load Forecasting Method Using Integrated CNN and LSTM Network
IEEE Access, 2021 In this study, a new technique is proposed to forecast short-term electrical load. Load forecasting is an integral part of power system planning and operation.
S. Rafi +3 more
semanticscholar +1 more source
Convolutional LSTM Networks for Subcellular Localization of Proteins [PDF]
, 2015 Machine learning is widely used to analyze biological sequence data. Non-sequential models such as SVMs or feed-forward neural networks are often used although they have no natural way of handling sequences of varying length.
A Graves +19 more
core +3 more sources
An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition [PDF]
Computer Vision and Pattern Recognition, 2019 Skeleton-based action recognition is an important task that requires the adequate understanding of movement characteristics of a human action from the given skeleton sequence.
Chenyang Si +4 more
semanticscholar +1 more source
Max-Pooling Loss Training of Long Short-Term Memory Networks for Small-Footprint Keyword Spotting
, 2017 We propose a max-pooling based loss function for training Long Short-Term Memory (LSTM) networks for small-footprint keyword spotting (KWS), with low CPU, memory, and latency requirements.
Fu, Gengshen +8 more
core +1 more source
Persistence pays off: Paying Attention to What the LSTM Gating Mechanism Persists [PDF]
, 2018 Language Models (LMs) are important components in several Natural Language Processing systems. Recurrent Neural Network LMs composed of LSTM units, especially those augmented with an external memory, have achieved state-of-the-art results. However, these
Kelleher, John D., Salton, Giancarlo D.
core +3 more sources
Compressing Recurrent Neural Networks with Tensor Ring for Action Recognition
, 2018 Recurrent Neural Networks (RNNs) and their variants, such as Long-Short Term Memory (LSTM) networks, and Gated Recurrent Unit (GRU) networks, have achieved promising performance in sequential data modeling.
Bai, Kun +6 more
core +1 more source
Thermal and Surface Radiosity Analysis of an Underfloor Heating System in a Bioclimatic Habitat
Energies, 2021 This paper addresses the modeling of convective and radiative heat transfer to achieve an acceptable level of indoor temperature. The results presented were obtained in a pilot project in which an energy-efficient house was built on a site located west ...
Abdelkader Laafer +3 more
doaj +1 more source
Cross-modal Recurrent Models for Weight Objective Prediction from Multimodal Time-series Data
, 2017 We analyse multimodal time-series data corresponding to weight, sleep and steps measurements. We focus on predicting whether a user will successfully achieve his/her weight objective.
Bellahsen, Otmane +8 more
core +1 more source
Parasites & Vectors, 2014 Background Pyrethroid resistance has been slower to emerge in Anopheles arabiensis than in An. gambiae s.s and An. funestus and, consequently, studies are only just beginning to unravel the genes involved. Permethrin resistance in An. arabiensis in Lower
Johnson Matowo +7 more
doaj +1 more source