[HTML][HTML] 循环神经网络研究综述
刘建伟， 宋志妍 - 控制与决策, 2022 - kzyjc.alljournals.cn
循环神经网络是神经网络序列模型的主要实现形式, 近几年得到迅速发展, 其是机器翻译,
机器问题回答, 序列视频分析的标准处理手段, 也是对于手写体自动合成, 语音处理和图像生成等 …
机器问题回答, 序列视频分析的标准处理手段, 也是对于手写体自动合成, 语音处理和图像生成等 …
Highway long short-term memory rnns for distant speech recognition
In this paper, we extend the deep long short-term memory (DL-STM) recurrent neural
networks by introducing gated direct connections between memory cells in adjacent layers …
networks by introducing gated direct connections between memory cells in adjacent layers …
Disentangled graph neural networks for session-based recommendation
Session-based recommendation (SBR) has drawn increasingly research attention in recent
years, due to its great practical value by only exploiting the limited user behavior history in …
years, due to its great practical value by only exploiting the limited user behavior history in …
Deep learning reservoir porosity prediction based on multilayer long short-term memory network
The cost of obtaining a complete porosity value using traditional coring methods is relatively
high, and as the drilling depth increases, the difficulty of obtaining the porosity value also …
high, and as the drilling depth increases, the difficulty of obtaining the porosity value also …
Low latency acoustic modeling using temporal convolution and LSTMs
Bidirectional long short-term memory (BLSTM) acoustic models provide a significant word
error rate reduction compared to their unidirectional counterpart, as they model both the past …
error rate reduction compared to their unidirectional counterpart, as they model both the past …
Cascaded encoders for unifying streaming and non-streaming ASR
End-to-end (E2E) automatic speech recognition (ASR) models, by now, have shown
competitive performance on several benchmarks. These models are structured to either …
competitive performance on several benchmarks. These models are structured to either …
Attention-based bidirectional gated recurrent unit neural networks for well logs prediction and lithology identification
L Zeng, W Ren, L Shan - Neurocomputing, 2020 - Elsevier
Many old oilfields have missed or distorted well logs data, which is due to long history of
shutdown, poor borehole conditions, damaged instrument, and other reasons. These bring …
shutdown, poor borehole conditions, damaged instrument, and other reasons. These bring …
Scalable training of deep learning machines by incremental block training with intra-block parallel optimization and blockwise model-update filtering
K Chen, Q Huo - … conference on acoustics, speech and signal …, 2016 - ieeexplore.ieee.org
We present a new approach to scalable training of deep learning machines by incremental
block training with intra-block parallel optimization to leverage data parallelism and …
block training with intra-block parallel optimization to leverage data parallelism and …
Streaming transformer-based acoustic models using self-attention with augmented memory
Transformer-based acoustic modeling has achieved great suc-cess for both hybrid and
sequence-to-sequence speech recogni-tion. However, it requires access to the full …
sequence-to-sequence speech recogni-tion. However, it requires access to the full …
Ultra short‐term probability prediction of wind power based on LSTM network and condition normal distribution
Considering the inevitable prediction errors in the traditional point predictions of wind power,
in this paper, a new ultra short‐term probability prediction method for wind power is …
in this paper, a new ultra short‐term probability prediction method for wind power is …