Hochreiter developed the long short-term memory (LSTM) neural network architecture in his diploma thesis in 1991 leading to the main publication in 1997.

[3][4] LSTM overcomes the problem of numerical instability in training recurrent neural networks (RNNs) that prevents them from learning from long sequences (vanishing or exploding gradient).

[3][8][9] In 2007, Hochreiter and others successfully applied LSTM with an optimized architecture to very fast protein homology detection without requiring a sequence alignment.

[17] Hochreiter introduced modern Hopfield networks with continuous states[18] and applied them to the task of immune repertoire classification.

[6][20] Hochreiter has been involved in the development of factor analysis methods with application to bioinformatics, including FABIA for biclustering,[21] HapFABIA for detecting short segments of identity by descent[22] and FARMS for preprocessing and summarizing high-density oligonucleotide DNA microarrays to analyze RNA gene expression.