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Federated embodied agent learning protects the data privacy of individual visual environments by keeping data locally at each client (the individual environment) during training.
Learning to navigate unseen environments: Back translation with environmental dropout
Hao Tan, Licheng Yu, and Mohit Bansal. 2019 · 1904
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Asynchronous methods for deep reinforcement learning
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Adversarial machine learning
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Machine learning with adversaries: Byzantine tolerant gradient descent
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Targeted backdoor attacks on deep learning systems using data poisoning
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Stacked cross attention for image-text matching
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The hidden vulnerability of distributed learning in byzantium
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Spatiotemporal attacks for embodied agents
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REVERIE: remote embodied visual referring expression in real indoor environments
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Robustnav: Towards benchmarking robustness in embodied navigation
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Vln bert: A recurrent vision-and-language bert for navigation
Yicong Hong, Qi Wu, Yuankai Qi, Cristian Rodriguez-Opazo, and Stephen Gould. 2021 · 2021
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Learning transferable visual models from natural language supervision
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How to backdoor federated learning
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On evaluation of embodied navigation agents
Peter Anderson, Angel Chang, Devendra Singh Chaplot, Alexey Dosovitskiy, Saurabh Gupta, Vladlen Koltun, Jana Kosecka, Jitendra Malik, Roozbeh Mottaghi, Manolis Savva, et al. 2018a
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How much can CLIP benefit vision-and-language tasks?
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Neurotoxin: Durable backdoors in federated learning
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