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Frontiers in Artificial Intelligence|February 14, 2022
Improving Adversarial Robustness via Attention and Adversarial Logit PairingXingjian Li, Dou Goodman, Ji Liu, et al.Frontiers in Artificial Intelligence|October 22, 2021
Identification of Scams in Initial Coin Offerings With Machine LearningBedil Karimov, Piotr WójcikFrontiers in Artificial Intelligence|October 28, 2021
Minimal Cycle Representatives in Persistent Homology Using Linear Programming: An Empirical Study With User's GuideLu Li, Connor Thompson, Gregory Henselman-Petrusek, et al.Frontiers in Artificial Intelligence|October 1, 2021
On Consensus-Optimality Trade-offs in Collaborative Deep LearningZhanhong Jiang, Aditya Balu, Chinmay Hegde, et al.Frontiers in Artificial Intelligence|October 4, 2021
Teaching Multiple Inverse Reinforcement LearnersFrancisco S Melo, Manuel LopesFrontiers in Artificial Intelligence|April 18, 2022
Scaling and Disagreements: Bias, Noise, and AmbiguityAlexandra Uma, Dina Almanea, Massimo PoesioFrontiers in Artificial Intelligence|April 11, 2022
Is Class-Incremental Enough for Continual Learning?Andrea Cossu, Gabriele Graffieti, Lorenzo Pellegrini, et al.Frontiers in Artificial Intelligence|April 11, 2022
Estimating Successful Internal Mobility: A Comparison Between Structural Equation Models and Machine Learning AlgorithmsFrancesco Bossi, Francesco Di Gruttola, Antonio Mastrogiorgio, et al.Frontiers in Artificial Intelligence|April 11, 2022
Critical Analysis of Deconfounded Pretraining to Improve Visio-Linguistic ModelsNathan Cornille, Katrien Laenen, Marie-Francine MoensFrontiers in Artificial Intelligence|March 30, 2022
A Machine Learning Approach to Personalize Computerized Cognitive Training InterventionsMelina Vladisauskas, Laouen M L Belloli, Diego Fernández Slezak, et al.Pageof 266