A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Jürgen Schmidhuber, Daan Wierstra, Matteo Gagliolo, et al.
Evolino combines evolution of recurrent neural network hidden weights with linear output weight computation, enabling LSTM to solve tasks where gradient methods fail.
Graves, Alex
Proposes an LSTM-based recurrent neural network for denoising and predicting GNSS time series, achieving 50% noise reduction and 1.1 mm MSE prediction error.
Danna Lesley Cruz Reyes, Juan Camilo Camargo Prieto, Andres David Leon Hernandez, et al.
This paper compares BERT and LSTM deep learning models for spam email classification on the Enron dataset, achieving 97% accuracy with BERT slightly outperforming LSTM but at higher computational cost.
Karim Abbasi, Parvin Razzaghi, Antti Poso, et al.
DeepCDA uses LSTM and CNNs with adversarial domain adaptation to predict compound-protein binding affinity across different data distributions.
Yong Yu, Xiaosheng Si, Changhua Hu, et al.
This paper reviews LSTM cells and their variants, categorizes LSTM networks into LSTM-dominated and integrated types, and discusses applications and future directions.