The Long Short-Term Memory and Other Gated RNNs As of this writing, the most effective sequence models used in practical applications are calledgated RNNs. These include thelong short-term memoryand networks ba
Nevertheless, LSTM and GRU still struggle to handle very long-term memory modeling in very long documents, and thus cannot copy or recall information from a long-distance past. Recently, the Rotational Unit of Memory (RUM)[74]has been proposed as a representation unit for RNNs that unifies ...
Sequence modelingLong short-term memory networkAccurate and efficient models for rainfall鈥搑unoff (RR) simulations are crucial for flood risk management. Recently, the success of the recurrent neural network (RNN) applied to sequential models has motivated groups to pursue RR modeling using RNN. ...
We denote the FLDCRF single-label sequence model (see Fig. 3b) as FLDCRF-s. FLDCRF-s outperforms LDCRF on UCI gesture phase data [38] and UCI opportunity data [35] (see Section 6). LSTMs [19] are the most popular kind of Recurrent Neural Networks (RNN) for sequence modeling, lar...
These tasks include polyphonic music modeling, word- and character-level language modeling, as well as synthetic stress tests that had been deliberately designed and frequently used to benchmark RNNs.Our evaluation is thus set up to compare convolutional and recurrent approaches to sequence modeling ...
The time-domain separation systems often receive input sequences consisting of a huge number of time steps, which introduces challenges for modeling extremely long sequences. Conventional RNNs are not effective for modeling such long sequences due to optimization difficulties, while 1-D CNNs cannot...
Unlike the time-frequency domain approaches, the time-domain separation systems often receive input sequences consisting of a huge number of time steps, which introduces challenges for modeling extremely long sequences. Conventional recurrent neural networks (RNNs) are...
Attention mechanisms have become an integral part of compelling sequence modeling and transduction models in various tasks, allowing modeling of dependencies without regard to their distance in the input or output sequences [2, 19]. In all but a few cases [27], however, such attention mechanisms...
对于post-attention sequence model 我们仅使用了基本的 RNN 这意味着,RNN捕获的the state 只输出 hidden state \(s^{\langle t\rangle}\). 这个任务中, 我们使用一个LSTM 代替基本RNN. 因此,LSTM 有 hidden state \(s^{\langle t\rangle}\), 也有 cell state \(c^{\langle t\rangle}\). ...
Recurrent Neural Networks (RNNs) offer fast inference on long sequences but are hard to optimize and slow to train. Deep state-space models (SSMs) have recently been shown to perform remarkably well on long sequence modeling tasks, and have the added benefits of fast parallelizable training ...