Deep-learning seismology by Mousavi, S. Mostafa, Beroza, Gregory C.

Privacy-Preserving Deep Learning on Machine Learning as a Service - A Comprehensive Survey by Tanuwidjaja, Harry Chandra, Choi, Rakyong, Baek, Seunggeun, Kim, Kwangjo

Deep Learning in Chemistry by Mater, Adam C, Coote, Michelle L

Fast-Convergent Federated Learning by Nguyen, Hung T., Sehwag, Vikash, Hosseinalipour, Seyyedali, Brinton, Christopher G., Chiang, Mung, Vincent Poor, H.

Code Difference Guided Adversarial Example Generation for Deep Code Models by Tian, Zhao, Chen, Junjie, Jin, Zhi

Using Machine Learning Algorithms to Predict People’s Intention to Use Mobile Learning Platforms During the COVID-19 Pandemic: Machine Learning Approach by Akour, Iman, Alshurideh, Muhammad, Al Kurdi, Barween, Al Ali, Amel, Salloum, Said

Research trends in measurement and intervention tools for self-regulated learning for e-learning environments—systematic review (2008–2018) by Araka, Eric, Maina, Elizaphan, Gitonga, Rhoda, Oboko, Robert

‘Oh, was that “experiential learning”?!’ Spaces, synergies and surprises with Kolb’s learning cycle by Tomkins, Leah, Ulus, Eda

Online learning: Adoption, continuance, and learning outcome—A review of literature by Panigrahi, Ritanjali, Srivastava, Praveen Ranjan, Sharma, Dheeraj

Web 3.0 – Based personalisation of learning objects in virtual learning environments by Kurilovas, Eugenijus, Kubilinskiene, Svetlana, Dagiene, Valentina

Learning to Detect by Samuel, Neev, Diskin, Tzvi, Wiesel, Ami

Narrative categorization in digital game‐based learning: Engagement, motivation & learning by Breien, Fredrik S., Wasson, Barbara

Editorial for “Transfer Learning Strategy Based on Unsupervised Learning and Ensemble Learning for Breast Cancer Molecular Subtype Prediction Using Dynamic Contrast Enhanced MRI” by Geitung, Jonn‐Terje

Quantum Generative Adversarial Learning by Lloyd, Seth, Weedbrook, Christian

There is No End to Learning, but How Does it Begin? A Meta‐Analysis of the Team Learning Pathway by Marlow, Shannon L., Lacerenza, Christina N.

Swarm Learning for decentralized and confidential clinical machine learning by Warnat-Herresthal, Stefanie, Schultze, Hartmut, Shastry, Krishnaprasad Lingadahalli, Manamohan, Sathyanarayanan, Mukherjee, Saikat, Garg, Vishesh, Sarveswara, Ravi, Händler, Kristian, Pickkers, Peter, Aziz, N. Ahmad, Ktena, Sofia, Tran, Florian, Bitzer, Michael, Ossowski, Stephan, Casadei, Nicolas, Herr, Christian, Petersheim, Daniel, Behrends, Uta, Kern, Fabian, Fehlmann, Tobias, Schommers, Philipp, Lehmann, Clara, Augustin, Max, Rybniker, Jan, Altmüller, Janine, Mishra, Neha, Bernardes, Joana P., Krämer, Benjamin, Bonaguro, Lorenzo, Schulte-Schrepping, Jonas, De Domenico, Elena, Siever, Christian, Kraut, Michael, Desai, Milind, Monnet, Bruno, Saridaki, Maria, Siegel, Charles Martin, Drews, Anna, Nuesch-Germano, Melanie, Theis, Heidi, Heyckendorf, Jan, Schreiber, Stefan, Kim-Hellmuth, Sarah, Nattermann, Jacob, Skowasch, Dirk, Kurth, Ingo, Keller, Andreas, Bals, Robert, Nürnberg, Peter, Rieß, Olaf, Rosenstiel, Philip, Netea, Mihai G., Theis, Fabian, Mukherjee, Sach, Backes, Michael, Aschenbrenner, Anna C., Ulas, Thomas, Breteler, Monique M. B., Giamarellos-Bourboulis, Evangelos J., Kox, Matthijs, Becker, Matthias, Cheran, Sorin, Woodacre, Michael S., Goh, Eng Lim, Schultze, Joachim L.

Let’s not forget: Learning analytics are about learning by Gašević, Dragan, Dawson, Shane, Siemens, George

Deep learning in biomedicine by Wainberg, Michael, Merico, Daniele, Delong, Andrew, Frey, Brendan J

Code smell detection by deep direct-learning and transfer-learning by Sharma, Tushar, Efstathiou, Vasiliki, Louridas, Panos, Spinellis, Diomidis

Deep Reinforcement Learning: A Survey by Wang, Xu, Wang, Sen, Liang, Xingxing, Zhao, Dawei, Huang, Jincai, Xu, Xin, Dai, Bin, Miao, Qiguang