A review of recurrent neural networks: LSTM cells and network architectures
Y Yu, X Si, C Hu, J Zhang - Neural computation, 2019 - direct.mit.edu
Recurrent neural networks (RNNs) have been widely adopted in research areas concerned
with sequential data, such as text, audio, and video. However, RNNs consisting of sigma …
with sequential data, such as text, audio, and video. However, RNNs consisting of sigma …
[HTML][HTML] A state-of-the-art survey on deep learning theory and architectures
In recent years, deep learning has garnered tremendous success in a variety of application
domains. This new field of machine learning has been growing rapidly and has been …
domains. This new field of machine learning has been growing rapidly and has been …
An empirical evaluation of generic convolutional and recurrent networks for sequence modeling
For most deep learning practitioners, sequence modeling is synonymous with recurrent
networks. Yet recent results indicate that convolutional architectures can outperform …
networks. Yet recent results indicate that convolutional architectures can outperform …
Clip4clip: An empirical study of clip for end to end video clip retrieval and captioning
Video clip retrieval and captioning tasks play an essential role in multimodal research and
are the fundamental research problem for multimodal understanding and generation. The …
are the fundamental research problem for multimodal understanding and generation. The …
[PDF][PDF] End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF
X Ma - arxiv preprint arxiv:1603.01354, 2016 - njuhugn.github.io
State-of-the-art sequence labeling systems traditionally require large amounts of task-
specific knowledge in the form of hand-crafted features and data pre-processing. In this …
specific knowledge in the form of hand-crafted features and data pre-processing. In this …
Deep learning and its applications to machine health monitoring
Abstract Since 2006, deep learning (DL) has become a rapidly growing research direction,
redefining state-of-the-art performances in a wide range of areas such as object recognition …
redefining state-of-the-art performances in a wide range of areas such as object recognition …
Deep learning in neural networks: An overview
J Schmidhuber - Neural networks, 2015 - Elsevier
In recent years, deep artificial neural networks (including recurrent ones) have won
numerous contests in pattern recognition and machine learning. This historical survey …
numerous contests in pattern recognition and machine learning. This historical survey …
Deep convolutional and lstm recurrent neural networks for multimodal wearable activity recognition
Human activity recognition (HAR) tasks have traditionally been solved using engineered
features obtained by heuristic processes. Current research suggests that deep convolutional …
features obtained by heuristic processes. Current research suggests that deep convolutional …
The history began from alexnet: A comprehensive survey on deep learning approaches
Deep learning has demonstrated tremendous success in variety of application domains in
the past few years. This new field of machine learning has been growing rapidly and applied …
the past few years. This new field of machine learning has been growing rapidly and applied …
An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition
Image-based sequence recognition has been a long-standing research topic in computer
vision. In this paper, we investigate the problem of scene text recognition, which is among …
vision. In this paper, we investigate the problem of scene text recognition, which is among …