Results 21 to 30 of about 15,297,754 (301)
On the Initialization of Long Short-Term Memory Networks [PDF]
, 2019 Weight initialization is important for faster convergence and stability of deep neural networks training. In this paper, a robust initialization method is developed to address the training instability in long short-term memory (LSTM) networks. It is based on a normalized random initialization of the network weights that aims at preserving the variance ...
Mostafa Mehdipour-Ghazi +6 more
openaire +3 more sources
, 2021 Data-intelligent algorithms tailored for short-term energy forecasting can generate meaningful information on the future variability of solar energy developments.
Deo, Ravinesh C. +4 more
core +1 more source
, 2021 Long-term memory is the mechanism which enables us to store information and experiences in a lasting fashion, for possible retrieval at some point in the future. This ability to create and retrieve memories is fundamental to all aspects of cognition, and
Groome, D., Law, R.
core +2 more sources
Ensemble long short-term memory (EnLSTM) network
CoRR, 2020 In this study, we propose an ensemble long short-term memory (EnLSTM) network, which can be trained on a small dataset and process sequential data. The EnLSTM is built by combining the ensemble neural network (ENN) and the cascaded long short-term memory (C-LSTM) network to leverage their complementary strengths.
Yuntian Chen, Dongxiao Zhang
openaire +2 more sources
Short- and long-term temporal network prediction based on network memory
Applied Network Science, 2023 Temporal networks are networks whose topology changes over time. Two nodes in a temporal network are connected at a discrete time step only if they have a contact/interaction at that time.
Li Zou, Alberto Ceria, Huijuan Wang
doaj +1 more source
Dependency-based long short term memory network for drug-drug interaction extraction
BMC Bioinformatics, 2017 Background Drug-drug interaction extraction (DDI) needs assistance from automated methods to address the explosively increasing biomedical texts. In recent years, deep neural network based models have been developed to address such needs and they have ...
Wei Wang +5 more
doaj +1 more source
Intrinsically Sparse Long Short-Term Memory Networks
CoRR, 2019 Long Short-Term Memory (LSTM) has achieved state-of-the-art performances on a wide range of tasks. Its outstanding performance is guaranteed by the long-term memory ability which matches the sequential data perfectly and the gating structure controlling the information flow.
Liu, Shiwei +2 more
openaire +3 more sources
Long short-term memory networks in memristor crossbars
CoRR, 2018 Recent breakthroughs in recurrent deep neural networks with long short-term memory (LSTM) units has led to major advances in artificial intelligence. State-of-the-art LSTM models with significantly increased complexity and a large number of parameters, however, have a bottleneck in computing power resulting from limited memory capacity and data ...
Can Li 0024 +16 more
openaire +3 more sources
Long Short-Term Memory Network Design for Analog Computing [PDF]
ACM Journal on Emerging Technologies in Computing Systems, 2019 We present an analog-integrated circuit implementation of long short-term memory network, which is compatible with digital CMOS technology. We have used multiple-input floating gate MOSFETs as both the front-end to obtain converted analog signals and the differential pairs in proposed analog multipliers.
Zhou Zhao +3 more
openaire +2 more sources
Cloud Affected Solar UV Predictions with Three-Phase Wavelet Hybrid Convolutional Long Short-Term Memory Network Multi-Step Forecast System [PDF]
, 2022 Harmful exposure to erythemally-effective ultraviolet radiation (UVR) poses high health risks such as malignant keratinocyte cancers and eye-related diseases.
Deo, Ravinesh C. +5 more
core +1 more source