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To quantify uncertainty, conformal prediction methods are gaining continuously more interest and have already been successfully applied to various domains.
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Probabilistic forecasting
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Development of a large-sample watershed-scale hydrometeorological data set for the contiguous USA: data set characteristics and assessment of regional variability in hydrologic model performance
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
The limits of distribution-free conditional predictive inference
Foygel Barber, R., Candes, E. J., Ramdas, A., and Tibshirani, R. J · 2021
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Adaptive conformal inference under distribution shift
Gibbs, I. and Candes, E. J · 2021
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A note on leveraging synergy in multiple meteorological data sets with deep learning for rainfall–runoff modeling
Kratzert, F., Klotz, D., Hochreiter, S., and Nearing, G. S · 2021
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Global transpiration data from sap flow measurements: the sapfluxnet database
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Hopfield networks is all you need
Ramsauer, H., Schäfl, B., Lehner, J., Seidl, P., Widrich, M., Gruber, L., Holzleitner, M., Pavlović, M., Sandve, G. K., Greiff, V., Kreil, D., Kopp, M., Klambauer, G., Brandstetter, J., and Hochreiter, S · 2021
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Gal, Y. and Ghahramani, Z · 2016
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Dense associative memory for pattern recognition
Krotov, D. and Hopfield, J. J · 2016
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The CAMELS data set: catchment attributes and meteorology for large-sample studies
Addor, N., Newman, A. J., Mizukami, N., and Clark, M. P · 2017
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Benchmarking of a physically based hydrologic model
Newman, A. J., Mizukami, N., Clark, M. P., Wood, A. W., Nijssen, B., and Nearing, G · 2017
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Conformal prediction of biological activity of chemical compounds
Toccaceli, P., Nouretdinov, I., and Gammerman, A · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I · 2017
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Cautionary tales on air-quality improvement in Beijing
Zhang, S., Guo, B., Dong, A., He, J., Xu, Z., and Chen, S. X · 2017
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Deep and confident prediction for time series at Uber
Zhu, L. and Laptev, N · 2017
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Conformal time-series forecasting
Stankevičiūtė, K., M Alaa, A., and van der Schaar, M · 2021
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Modeling regime shifts in multiple time series
Tajeuna, E. G., Bouguessa, M., and Wang, S · 2021
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Retrosynthesis prediction based on graph relation network
Dong, Z., Chen, Z., and Wang, Q · 2022
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Conformal prediction beyond exchangeability
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CLOOB: Modern Hopfield Networks with infoLOOB outperform CLIP
Fürst, A., Rumetshofer, E., Lehner, J., Tran, V. T., Tang, F., Ramsauer, H., Kreil, D. P., Kopp, M. K., Klambauer, G., Bitto-Nemling, A., and Hochreiter, S · 2022
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Darts: User-friendly modern machine learning for time series
Herzen, J., Lässig, F., Piazzetta, S. G., Neuer, T., Tafti, L., Raille, G., Van Pottelbergh, T., Pasieka, M., Skrodzki, A., Huguenin, N., et al · 2022
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Ensemble conformalized quantile regression for probabilistic time series forecasting
Jensen, V., Bianchi, F. M., and Anfinsen, S. N · 2022
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Uncertainty estimation with deep learning for rainfall–runoff modeling
Klotz, D., Kratzert, F., Gauch, M., Keefe Sampson, A., Brandstetter, J., Klambauer, G., Hochreiter, S., and Nearing, G · 2022
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NeuralHydrology — a Python library for deep learning research in hydrology
Kratzert, F., Gauch, M., Nearing, G., and Klotz, D · 2022
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Adaptive sampling for probabilistic forecasting under distribution shift
Masserano, L., Rangapuram, S. S., Kapoor, S., Nirwan, R. S., Park, Y., and Bohlke-Schneider, M · 2022
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History compression via language models in reinforcement learning
Paischer, F., Adler, T., Patil, V., Bitto-Nemling, A., Holzleitner, M., Lehner, S., Eghbal-zadeh, H., and Hochreiter, S · 2022
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CLOOME: contrastive learning unlocks bioimaging databases for queries with chemical structures
Sanchez-Fernandez, A., Rumetshofer, E., Hochreiter, S., and Klambauer, G · 2022
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Hopular: Modern Hopfield Networks for tabular data
Schäfl, B., Gruber, L., Bitto-Nemling, A., and Hochreiter, S · 2022
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Copula conformal prediction for multi-step time series forecasting
Sun, S. and Yu, R · 2022
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Recurrent neural network-induced gaussian process
Sun, X., Kim, S., and Choi, J.-I · 2022
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Predictive inference with feature conformal prediction
Teng, J., Wen, C., Zhang, D., Bengio, Y., Gao, Y., and Yuan, Y · 2022
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Txt2Img-MHN: Remote sensing image generation from text using Modern Hopfield Networks
Xu, Y., Yu, W., Ghamisi, P., Kopp, M., and Hochreiter, S · 2022
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Adaptive conformal predictions for time series
Zaffran, M., Féron, O., Goude, Y., Josse, J., and Dieuleveut, A · 2022
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Conformal prediction: a unified review of theory and new challenges
Fontana, M., Zeni, G., and Vantini, S · 2023
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Bayesian transformed gaussian processes
Zhu, X., Huang, L., Lee, E. H., Ibrahim, C. A., and Bindel, D · 2023
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