Journal ArticleDOI
Learning long-term dependencies with gradient descent is difficult
TLDR
This work shows why gradient based learning algorithms face an increasingly difficult problem as the duration of the dependencies to be captured increases, and exposes a trade-off between efficient learning by gradient descent and latching on information for long periods.Abstract:
Recurrent neural networks can be used to map input sequences to output sequences, such as for recognition, production or prediction problems. However, practical difficulties have been reported in training recurrent neural networks to perform tasks in which the temporal contingencies present in the input/output sequences span long intervals. We show why gradient based learning algorithms face an increasingly difficult problem as the duration of the dependencies to be captured increases. These results expose a trade-off between efficient learning by gradient descent and latching on information for long periods. Based on an understanding of this problem, alternatives to standard gradient descent are considered. >read more
Citations
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Journal ArticleDOI
Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation
TL;DR: A hybrid algorithm that combines similar days (SD) selection, empirical mode decomposition (EMD), and long short-term memory (LSTM) neural networks to construct a prediction model (i.e., SD-EMD-L STM) for short- term load forecasting is presented.
Posted Content
Going Deeper into Action Recognition: A Survey
TL;DR: A comprehensive review of the notable steps taken towards recognizing human actions can be found in this article, where the authors start with the pioneering methods that use handcrafted representations, and then, navigate into the realm of deep learning based approaches.
Journal ArticleDOI
Deep Learning and Its Applications in Biomedicine.
Chensi Cao,Feng Liu,Hai Tan,Deshou Song,Wenjie Shu,Weizhong Li,Yiming Zhou,Xiaochen Bo,Zhi Xie +8 more
TL;DR: An overview of deep learning techniques and some of the state-of-the-art applications in the biomedical field is provided, including medical image classification, genomic sequence analysis, as well as protein structure classification and prediction.
Posted Content
Jointly Modeling Embedding and Translation to Bridge Video and Language
TL;DR: A novel unified framework, named Long Short-Term Memory with visual-semantic Embedding (LSTM-E), which can simultaneously explore the learning of LSTM and visual- semantic embedding and outperforms several state-of-the-art techniques in predicting Subject-Verb-Object (SVO) triplets.
Journal ArticleDOI
Application of Long Short-Term Memory (LSTM) Neural Network for Flood Forecasting
TL;DR: In this paper, a Long Short-Term Memory (LSTM) neural network model was used for flood forecasting, where the daily discharge and rainfall were used as input data, and characteristics of the data sets which may influence the model performance were also of interest.
References
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Journal ArticleDOI
Optimization by Simulated Annealing
TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
Book ChapterDOI
Learning internal representations by error propagation
TL;DR: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion.
Book
Learning internal representations by error propagation
TL;DR: In this paper, the problem of the generalized delta rule is discussed and the Generalized Delta Rule is applied to the simulation results of simulation results in terms of the generalized delta rule.
Journal ArticleDOI
A learning algorithm for continually running fully recurrent neural networks
Ronald J. Williams,David Zipser +1 more
TL;DR: The exact form of a gradient-following learning algorithm for completely recurrent networks running in continually sampled time is derived and used as the basis for practical algorithms for temporal supervised learning tasks.
Journal ArticleDOI
Minimizing multimodal functions of continuous variables with the “simulated annealing” algorithm—Corrigenda for this article is available here
TL;DR: A new global optimization algorithm for functions of continuous variables is presented, derived from the “Simulated Annealing” algorithm recently introduced in combinatorial optimization, which is quite costly in terms of function evaluations, but its cost can be predicted in advance, depending only slightly on the starting point.