Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| LSTM Multimodal× | LSTM× | |
|---|---|---|
| Domeniu | Învățare profundă | Învățare profundă |
| Familie | Machine learning | Machine learning |
| Anul apariției≠ | 2016 | 1997 |
| Autorul original≠ | Rajagopalan et al. and various concurrent works (2016–2018) | Hochreiter, S. & Schmidhuber, J. |
| Tip≠ | Recurrent neural network architecture | Recurrent neural network (gated memory cell) |
| Sursa seminală≠ | Rajagopalan, S., Tran, L., Rozgic, V., Narayanan, S., Kumar, A., & Ramakrishna, S. (2016). Extending Long Short-Term Memory for Multi-View Structured Learning. In Proceedings of ECCV 2016. Springer. link ↗ | Hochreiter, S. & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780. DOI ↗ |
| Denumiri alternative | MM-LSTM, multimodal recurrent network, multi-input LSTM, multimodal sequence model | LSTM (Uzun Kısa Dönem Bellek Ağı), long short-term memory, LSTM network, recurrent neural network with memory cells |
| Înrudite≠ | 4 | 5 |
| Rezumat≠ | Multimodal LSTM extends the standard Long Short-Term Memory network to jointly process sequential data from multiple input modalities — such as text, audio, and video — within a unified recurrent architecture. By fusing representations from different sources before or within the LSTM cells, it captures temporal dependencies that span and cross modalities, making it a foundational approach for tasks like sentiment analysis, video captioning, and affective computing. | LSTM (Long Short-Term Memory) is a recurrent neural network architecture, introduced by Sepp Hochreiter and Jürgen Schmidhuber in 1997, that can learn long-term dependencies in sequential data and is widely used for time-series and sequence prediction. It keeps an internal memory that lets information persist across many time steps. |
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