For this reason, the 2010 i2b2/VA Natural Language Processing Challenges for Clinical Records introduced a concept extraction task aimed at identifying and classifying concepts into predefined categories (i.e., treatments, tests and problems). LSTM layer; GRU layer; SimpleRNN layer Bidirectional LSTM-CRF for Clinical Concept Extraction Automated extraction of concepts from patient clinical records is an essential facilitator of clinical research. In this paper, we trained EEG data from canines on a double Bidirectional LSTM layer followed by a fully connected layer. .. The audio streams are converted into acoustic feature, i.e. • The proposed LSTM-BIT achieved 4.24–47.15% lower RMSE than other methods. 2.1 LSTM Networks Recurrent neural networks (RNN) have been employed to produce promising results on a variety of tasks including language model [ Mikolov et al.2010 , Mikolov et al.2011 ] and speech recognition [ Graves et al.2005 ] . • The proposed method integrated advanced deep learning and transfer learning methods. This paper proposes a deep bidirectional long short-term memory approach in modeling the long contextual, nonlinear mapping between audio and visual streams for video-realistic talking head. In training stage, an audio-visual stereo database is firstly recorded as a subject talking to a camera.
A deep bidirectional LSTM approach for video-realistic talking head ... Abstract This paper proposes a deep bidirectional long short-term memory approach in modeling the long contextual, nonlinear mapping between audio and visual streams for video-realistic talking head. The paper comprehensively studied different missing data problems for building energy. Long short-term memory ( LSTM ) is an artificial recurrent neural network (RNN) architecture  used in the field of deep learning . • Random missing, continuous missing, and large proportional missing are all discussed. The Long Short-Term Memory (LSTM) cell can process data sequentially and keep its hidden state through time. In training stage, an audio-visual stereo database is firstly recorded as a subject talking to a camera. In Section2of this paper, we review related work and introduce TimeBank-Dense. In this section, we describe the models used in this paper: LSTM, BI-LSTM, CRF, LSTM-CRF and BI-LSTM-CRF.
The nal conclusion is made in Section5. We dis-cuss the cross-sentence link problem and the ar-chitectures of our E-E, E-T and E-D classiers in Section3.
Keras documentation. Keras API reference / Layers API / Recurrent layers Recurrent layers. In Section4, the experiments are per-formed on TimeBank-Dense and we compare our model to the baseline and two state-of-the-art sys-tems.