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Recurrent Neural Networks for Edge Intelligence
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Large-Scale Machine Learning with Stochastic Gradient Descent
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TensorD: A tensor decomposition library in TensorFlow
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inferredcites →https://doi.org/10.1016/j.neunet.2009.10.009openalex-snapshot
Temporal-Kernel Recurrent Neural Networks
inferredcites →https://doi.org/10.1016/j.patrec.2014.04.011openalex-snapshot
Human activity recognition from 3D data: A review
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On the momentum term in gradient descent learning algorithms
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Learning representations by back-propagating errors
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Neural networks and physical systems with emergent collective computational abilities.
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Backpropagation through time: what it does and how to do it
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Learning long-term dependencies with gradient descent is difficult
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Bidirectional recurrent neural networks
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Knowing When to Look: Adaptive Attention via a Visual Sentinel for Image Captioning
inferredcites →https://doi.org/10.1109/cvpr.2017.683openalex-snapshot
Residual Attention Network for Image Classification
inferredcites →https://doi.org/10.1109/cvpr.2018.00171openalex-snapshot
MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks
inferredcites →https://doi.org/10.1109/cvpr.2018.00977openalex-snapshot
Learning Compact Recurrent Neural Networks with Block-Term Tensor Decomposition
inferredcites →https://doi.org/10.1109/icassp.2016.7472823openalex-snapshot
On the compression of recurrent neural networks with an application to LVCSR acoustic modeling for embedded speech recognition
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Deep Learning Model for Text Recognition in Images
inferredcites →https://doi.org/10.1109/icimia48430.2020openalex-snapshot
2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA)
inferredcites →https://doi.org/10.1109/icnn.1996.548916openalex-snapshot
Learning task-dependent distributed representations by backpropagation through structure
inferredcites →https://doi.org/10.1109/ijcnn.2000.859363openalex-snapshot
Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks - IJCNN 2000
inferredcites →https://doi.org/10.1109/inss.2010.5573462openalex-snapshot
Collecting complex activity datasets in highly rich networked sensor environments
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Squeezing Deep Learning into Mobile and Embedded Devices
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Context dependent recurrent neural network language model
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LSTM: A Search Space Odyssey
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Show and Tell: Lessons Learned from the 2015 MSCOCO Image Captioning Challenge
inferredcites →https://doi.org/10.1137/07070111xopenalex-snapshot
Tensor Decompositions and Applications
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Connectionist temporal classification
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Efficient methods and hardware for deep learning
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Gated Orthogonal Recurrent Units: On Learning to Forget
inferredcites →https://doi.org/10.1609/aaai.v33i01.33014683openalex-snapshot
Compressing Recurrent Neural Networks with Tensor Ring for Action Recognition
inferredcites →https://doi.org/10.21437/interspeech.2005-69openalex-snapshot
Comparison of keyword spotting approaches for informal continuous speech
inferredcites →https://doi.org/10.21437/interspeech.2014-80openalex-snapshot
Long short-term memory recurrent neural network architectures for large scale acoustic modeling
inferredcites →https://doi.org/10.48550/arxiv.1910.01740openalex-snapshot
AntMan: Sparse Low-Rank Compression to Accelerate RNN inference
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