Results 11 to 20 of about 439,227 (216)
BarekengCoal remains vital for global energy despite recent demand fluctuations due to the COVID-19 pandemic and geopolitical tensions. The International Energy Agency (IEA) projected a decline in global coal demand starting in early 2024, driven by increasing ...
Indra Rivaldi Siregar +4 more
doaj +2 more sources
Long Short-Term Memory Neural Equalizer [PDF]
IEEE Transactions on Signal and Power Integrity, 2022 In this work we propose a neuromorphic hardware based signal equalizer by based on the deep learning implementation. The proposed neural equalizer is plasticity trainable equalizer which is different from traditional model designed based DFE. A trainable Long Short-Term memory neural network based DFE architecture is proposed for signal recovering and ...
Wang, Zihao +5 more
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CoRR, 2021 This paper is based on a machine learning project at the Norwegian University of Science and Technology, fall 2020. The project was initiated with a literature review on the latest developments within time-series forecasting methods in the scientific community over the past five years.
Christian Bakke Vennerød +2 more
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Lipreading with long short-term memory [PDF]
2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016 Accepted for publication at ICASSP ...
Michael Wand 0002 +2 more
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Quantum Long Short-Term Memory
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022 Long short-term memory (LSTM) is a kind of recurrent neural networks (RNN) for sequence and temporal dependency data modeling and its effectiveness has been extensively established. In this work, we propose a hybrid quantum-classical model of LSTM, which we dub QLSTM.
Samuel Yen-Chi Chen +2 more
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Continual Learning Long Short Term Memory [PDF]
Findings of the Association for Computational Linguistics: EMNLP 2020, 2020 Catastrophic forgetting in neural networks indicates the performance decreasing of deep learning models on previous tasks while learning new tasks. To address this problem, we propose a novel Continual Learning Long Short Term Memory (CL-LSTM) cell in Recurrent Neural Network (RNN) in this paper.
Xin Guo 0007 +6 more
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Explaining short-term memory phenomena with long-term memory theory: Is a special state involved? [PDF]
, 2023 The idea that some recently encountered items reside in a special state where they do not have to be retrieved has come to be a critical component of short-term memory theories.
Humphreys, Michael S. +7 more
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Short-term Load Forecasting with Distributed Long Short-Term Memory
2023 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), 2023 5 pages, 4 figures, 2023 ISGT ...
Yi Dong 0002 +3 more
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On separating long- and short-term memories in hyperdimensional computing [PDF]
Frontiers in Neuroscience, 2023 Operations on high-dimensional, fixed-width vectors can be used to distribute information from several vectors over a single vector of the same width. For example, a set of key-value pairs can be encoded into a single vector with multiplication and addition of the corresponding key and value vectors: the keys are bound to their values with component ...
Jeffrey L. Teeters +4 more
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Questioning short-term memory and its measurement: why digit span measures long-term associative learning [PDF]
, 2015 Traditional accounts of verbal short-term memory explain differences in performance for different types of verbal material by reference to inherent characteristics of the verbal items making up memory sequences.
Macken, B +3 more
core +1 more source