विधियों की तुलना करें

	Multimodal LSTM	एलएसटीएम
क्षेत्र	गहन अधिगम	गहन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2016	1997
प्रवर्तक	Rajagopalan et al. and various concurrent works (2016–2018)	Hochreiter, S. & Schmidhuber, J.
प्रकार	Recurrent neural network architecture	Recurrent neural network (gated memory cell)
मौलिक स्रोत	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.	Hochreiter, S. & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780.
उपनाम	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
संबंधित	4	5
सारांश	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.