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Explainable weather forecasts through an LSTM-CBR twin system. (2023)
Conference Proceeding
PIRIE, C., SURESH, M., SALIMI, P., PALIHAWADANA, C. and NANAYAKKARA, G. 2022. Explainable weather forecasts through an LSTM-CBR twin system. In Reuss, P. and Schönborn, J. (eds.) ICCBR-WS 2022: proceedings of the 30th International conference on Case-based reasoning workshops 2022 (ICCBR-WS 2022) co-located with the 30th International conference on Case-based reasoning 2022 (ICCBR 2022), 12-15 September 2022, Nancy, France. Aachen: CEUR workshop proceedings [online], 3389, pages 256-260. Available from: https://ceur-ws.org/Vol-3389/ICCBR_2022_XCBR_Challenge_RGU.pdf

In this paper, we explore two methods for explaining LSTM-based temperature forecasts using previous 14 day progressions of humidity and pressure. First, we propose and evaluate an LSTM-CBR twin system that generates nearest-neighbors that can be vis... Read More about Explainable weather forecasts through an LSTM-CBR twin system..

CBR for interpretable response selection in conversational modelling. (2022)
Conference Proceeding
SURESH, M. 2022. CBR for interpretable response selection in conversational modelling. In Reuss, P. and Schönborn, J. (eds). ICCBR-DC 2022: proceedings of the 30th International conference on case-based reasoning doctorial consortium 2022 (ICCBR-DC 2022), co-located with the 30th International conference on case-based reasoning 2022 (ICCBR 2022), 12-15 September 2022, Nancy, France. Aachen: CEUR workshop proceedings [online], 3418, pages 28-33. Available from: https://ceur-ws.org/Vol-3418/ICCBR_2022_DC_paper25.pdf

Current state-of-the-art dialogue systems are increasingly complex. When used in applications such as motivational interviewing, the lack of interpretability is a concern. CBR offers to bridge this gap by using the most similar past cases to decide t... Read More about CBR for interpretable response selection in conversational modelling..