Results 1 to 10 of about 60,490 (168)
LSTM-in-LSTM for generating long descriptions of images [PDF]
Computational Visual Media, 2016 In this paper, we propose an approach for generating rich fine-grained textual descriptions of images. In particular, we use an LSTM-in-LSTM (long short-term memory) architecture, which consists of an inner LSTM and an outer LSTM. The inner LSTM effectively encodes the long-range implicit contextual interaction between visual cues (i.e., the ...
Jun Xiao, Siliang Tang, Jun Song
exaly +2 more sources
CoRR, 2021 13 pages (8.5 without references) + 17 pages ...
Pieter-Jan Hoedt +7 more
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Deep Learning Based Models: Basic LSTM, Bi LSTM, Stacked LSTM, CNN LSTM and Conv LSTM to Forecast Agricultural Commodities Prices [PDF]
, 2021 Abstract The literature argues that an accurate price prediction of agricultural goods is a quintessence to assure a good functioning of the economy all over the world. Research reveals that studies with application of deep learning in the tasks of agricultural price forecast on short historical agricultural prices data are very scarce and ...
R. Murugesan +2 more
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Proceedings of the 11th International Conference on Natural Language Generation, 2018 Hypertagging, or supertagging for surface realization, is the process of assigning lexical categories to nodes in an input semantic graph. Previous work has shown that hypertagging significantly increases realization speed and quality by reducing the search space of the realizer.
Reid Fu, Michael White
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Next-LSTM: a novel LSTM-based image captioning technique
International Journal of System Assurance Engineering and Management, 2023 Abstract Recently, Image Captioning has evolved into an immensely popular area in the field of Computer Vision. It strives to generate natural language sentences in order to describe the salient parts of a given image. Research in this area is active and various Machine learning-based Image Captioning models have been proposed in the literature.
Priya Singh, Chandan Kumar, Ayush Kumar
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Proceedings of the AAAI Conference on Artificial Intelligence, 2020 Long Short-Term Memory (LSTM) infers the long term dependency through a cell state maintained by the input and the forget gate structures, which models a gate output as a value in [0,1] through a sigmoid function. However, due to the graduality of the sigmoid function, the sigmoid gate is not flexible in representing multi-modality or skewness. Besides,
Kyungwoo Song +3 more
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Speaker Diarization with LSTM [PDF]
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018 Published at ICASSP ...
Quan Wang +4 more
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CoRR, 2019 Many advances in Natural Language Processing have been based upon more expressive models for how inputs interact with the context in which they occur. Recurrent networks, which have enjoyed a modicum of success, still lack the generalization and systematicity ultimately required for modelling language.
Gábor Melis +2 more
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Field Data Forecasting Using LSTM and Bi-LSTM Approaches [PDF]
Applied Sciences, 2021 Water, an essential resource for crop production, is becoming increasingly scarce, while cropland continues to expand due to the world’s population growth. Proper irrigation scheduling has been shown to help farmers improve crop yield and quality, resulting in more sustainable water consumption.
Suebsombut, Paweena +4 more
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CoRR, 2018 We propose Nested LSTMs (NLSTM), a novel RNN architecture with multiple levels of memory. Nested LSTMs add depth to LSTMs via nesting as opposed to stacking. The value of a memory cell in an NLSTM is computed by an LSTM cell, which has its own inner memory cell.
Joel Ruben Antony Moniz +1 more
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