Advanced Lectures on Machine Learning

A review of uncertainty quantification in deep learning: Techniques, applications and challenges

Abdar, M., Pourpanah, F., Hussain, S., Rezazadegan, D., Liu, L., Ghavamzadeh, M., Fieguth, P., Cao, X., Khosravi, A., Acharya, U. R., et al. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.05.008 · Cited by 3,111

Adiabatic quantum computation

Albash, T., & Lidar, D. A. (2018) · Reviews of Modern Physics · doi:10.1103/revmodphys.90.015002 · Cited by 1,546

Fields of Experts

Roth, S., & Black, M. J. (2009) · International Journal of Computer Vision · doi:10.1007/s11263-008-0197-6 · Cited by 847

Modeling causes of death: an integrated approach using CODEm

Foreman, K. J., Lozano, R., Lopez, A. D., & Murray, C. J. (2012) · Population Health Metrics · doi:10.1186/1478-7954-10-1 · Cited by 564

Supervised Machine Learning for Population Genetics: A New Paradigm

Schrider, D. R., & Kern, A. D. (2018) · Trends in Genetics · doi:10.1016/j.tig.2017.12.005 · Cited by 453

A survey on face detection in the wild: Past, present and future

Zafeiriou, S., Zhang, C., & Zhang, Z. (2015) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2015.03.015 · Cited by 458

Smart energy systems for sustainable smart cities: Current developments, trends and future directions

O’Dwyer, E., Pan, I., Acha, S., & Shah, N. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.01.024 · Cited by 410

An improved CNN-LSTM model-based state-of-health estimation approach for lithium-ion batteries

Xu, H., Wu, L., Xiong, S., Li, W., Garg, A., & Gao, L. (2023) · Energy · doi:10.1016/j.energy.2023.127585 · Cited by 366

The dropout learning algorithm

Baldi, P., & Sadowski, P. (2014) · Artificial Intelligence · doi:10.1016/j.artint.2014.02.004 · Cited by 340

Review and big data perspectives on robust data mining approaches for industrial process modeling with outliers and missing data

Zhu, J., Ge, Z., Song, Z., & Gao, F. (2018) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2018.09.003 · Cited by 308

DeepChrome: deep-learning for predicting gene expression from histone modifications

Singh, R., Lanchantin, J., Robins, G., & Qi, Y. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw427 · Cited by 305

Daily streamflow forecasting by machine learning methods with weather and climate inputs

Rasouli, K., Hsieh, W. W., & Cannon, A. J. (2012) · Journal of Hydrology · doi:10.1016/j.jhydrol.2011.10.039 · Cited by 298

Lithium-ion battery remaining useful life estimation with an optimized Relevance Vector Machine algorithm with incremental learning

Liu, D., Zhou, J., Pan, D., Peng, Y., & Peng, X. (2015) · Measurement · doi:10.1016/j.measurement.2014.11.031 · Cited by 266

Design of inferential sensors in the process industry: A review of Bayesian methods

Khatibisepehr, S., Huang, B., & Khare, S. (2013) · Journal of Process Control · doi:10.1016/j.jprocont.2013.05.007 · Cited by 262

Variational Quantum Simulation of General Processes

Endo, S., Sun, J., Li, Y., Benjamin, S. C., & Yuan, X. (2020) · Physical Review Letters · doi:10.1103/physrevlett.125.010501 · Cited by 237

Proteochemometric modeling as a tool to design selective compounds and for extrapolating to novel targets

van Westen, G. J. P., Wegner, J. K., IJzerman, A. P., van Vlijmen, H. W. T., & Bender, A. (2011) · MedChemComm · doi:10.1039/c0md00165a · Cited by 176

On the Existence of Simpler Machine Learning Models

Semenova, L., Rudin, C., & Parr, R. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533232 · Cited by 90

Conversion and time-to-conversion predictions of mild cognitive impairment using low-rank affinity pursuit denoising and matrix completion

Thung, K.-H., Yap, P.-T., Adeli, E., Lee, S.-W., & Shen, D. (2018) · Medical Image Analysis · doi:10.1016/j.media.2018.01.002 · Cited by 89

A data-fusion framework for lithium battery health condition Estimation Based on differential thermal voltammetry

Li, X., Yuan, C., Wang, Z., & Xie, J. (2022) · Energy · doi:10.1016/j.energy.2021.122206 · Cited by 56

Hybrid statistical modelling of the frequency response function of industrial robots

Nguyen, V., & Melkote, S. (2021) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2021.102134 · Cited by 51

An axiomatic approach to intrinsic dimension of a dataset

Pestov, V. (2008) · Neural Networks · doi:10.1016/j.neunet.2007.12.030 · Cited by 45