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The Data Efficiency of Deep Learning Is Degraded by Unnecessary Input Dimensions., , , and . Frontiers Comput. Neurosci., (2022)On the Capability of Neural Networks to Generalize to Unseen Category-Pose Combinations., , , , , , , , and . CoRR, (2020)Modularity Trumps Invariance for Compositional Robustness., , , and . CoRR, (2023)Removable and/or Repeated Units Emerge in Overparametrized Deep Neural Networks., , , , , , and . CoRR, (2019)Computational commonalities of image understanding algorithms.. ETH Zurich, Zürich, Switzerland, (2015)base-search.net (ftethz:oai:www.research-collection.ethz.ch:20.500.11850/102018).Random Binary Mappings for Kernel Learning and Efficient SVM., , and . CoRR, (2013)SEEDS: Superpixels Extracted via Energy-Driven Sampling., , , and . CoRR, (2013)Predicting When Eye Fixations Are Consistent., , and . AAAI Spring Symposia, AAAI Press, (2017)On Active Video Summarization: Customized Summaries via On-Line Interaction with the User., , , and . AAAI Spring Symposia, AAAI Press, (2017)The Foes of Neural Network's Data Efficiency Among Unnecessary Input Dimensions., , , and . CoRR, (2021)