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Learning representations of multimodal data that are both informative and robust to missing modalities at test time remains a challenging problem due to the inherent heterogeneity of data obtained from different channels.
Auto-encoding variational bayes
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Continuous control with deep reinforcement learning
Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., and Wierstra, D · 2015
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Rethinking self-supervised learning: Small is beautiful
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Multibench: Multiscale benchmarks for multimodal representation learning
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Multimodal vae active inference controller
Meo, C. and Lanillos, P · 2021
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Delaunay component analysis for evaluation of data representations
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Geomca: Geometric evaluation of data representations
Poklukar, P., Varava, A., and Kragic, D
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Multimodal transformer for unaligned multimodal language sequences
Tsai, Y.-H. H., Bai, S., Liang, P. P., Kolter, J. Z., Morency, L.-P., and Salakhutdinov, R
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Learning factorized multimodal representations
Tsai, Y.-H. H., Liang, P. P., Zadeh, A., Morency, L.-P., and Salakhutdinov, R
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How to sense the world: Leveraging hierarchy in multimodal perception for robust reinforcement learning agents
Vasco, M., Yin, H., Melo, F. S., and Paiva, A
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Leveraging hierarchy in multimodal generative models for effective cross-modality inference
Vasco, M., Yin, H., Melo, F. S., and Paiva, A
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Tremblay, J.-F., Manderson, T., Noca, A., Dudek, G., and Meger, D · 2021
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