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README.md

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 # Memory-Fusion-Network
-Code for Memory Fusion Network, AAAI 2018
+Code for Memory Fusion Network (MFN), AAAI 2018
+
+This repository includes data, code and pretrained models for the AAAI 2018 paper, "Memory Fusion Network for Multi-view Sequential Learning"
+
+Requirements:
+Python 2.7
+PyTorch 0.4.0
+numpy 1.13.3
+sklearn 0.20.0
+
+Data: we have included preprocessed data from the CMU-MOSI dataset for multimodal sentiment analysis. These are found in data/X_train.h5, data/y_train.h5 etc. To be consistent with previously reported results on the CMU-MOSI dataset, we used the exact same dataset as used in the baselines. We are in the process of integrate the model with the latest version of the CMU-MOSI and CMU-MOSEI datasets which can be found at https://github.com/A2Zadeh/CMU-MultimodalSDK/
+
+Code: training code for both MFN and EF-LSTM (early fusion LSTM) are included in test_mosi.py
+
+Pretrained models: pretrained MFN models optimized for MAE (Mean Absolute Error) and binary classification accuracy can be found in best/mfn_mae.pt, and best/mfn_acc.pt
+
+You can run the code by typing "python test_mosi.py" in the command line. This loads the pretrained model best/mfn_mae.pt which gives a CMU-MOSI test set MAE of 0.954, and the pretrained model best/mfn_acc.pt which gives a CMU-MOSI test set binary classification accuracy of 77.4%.
+
+Next steps: we are in the process of integrating the model with the latest version of the CMU-MOSI and CMU-MOSEI datasets which can be found at https://github.com/A2Zadeh/CMU-MultimodalSDK/
+
+If you use this code, please cite our paper:
+
+@article{zadeh2018memory,
+ title={Memory Fusion Network for Multi-view Sequential Learning},
+ author={Zadeh, Amir and Liang, Paul Pu and Mazumder, Navonil and Poria, Soujanya and Cambria, Erik and Morency, Louis-Philippe},
+ journal={Proceedings of the Thirty-Second {AAAI} Conference on Artificial Intelligence},
+ year={2018}
+}