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Reasoning from sequences of raw sensory data is a ubiquitous problem across fields ranging from medical devices to robotics.
An introduction to the kalman filter
Welch, G., Bishop, G., et al · 1995
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Between mdps and semi-mdps: A framework for temporal abstraction in reinforcement learning
Sutton, R. S., Precup, D., and Singh, S · 1999
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Hierarchical memory-based reinforcement learning
Gardiol, N. H · 2000
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The impact of the mit-bih arrhythmia database
Moody, G. B. and Mark, R. G · 2001
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Real time control of urban wastewater systems—where do we stand today?
Schütze, M., Campisano, A., Colas, H., Schilling, W., and Vanrolleghem, P. A · 2004
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Nonlinear filters: beyond the kalman filter
Daum, F · 2005
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Optimal state estimation: Kalman, H infinity, and nonlinear approaches
Simon, D · 2006
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
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Accelerometer-based on-body sensor localization for health and medical monitoring applications
Amini, N., Sarrafzadeh, M., Vahdatpour, A., and Xu, W · 2011
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State estimation and control of electric loads to manage real-time energy imbalance
Mathieu, J. L., Koch, S., and Callaway, D. S · 2012
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The opportunity challenge: A benchmark database for on-body sensor-based activity recognition
Chavarriaga, R., Sagha, H., Calatroni, A., Digumarti, S. T., Tröster, G., Millán, J. d. R., and Roggen, D · 2013
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Vision meets robotics: The kitti dataset
Geiger, A., Lenz, P., Stiller, C., and Urtasun, R · 2013
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S1 and s2 heart sound recognition using deep neural networks
Chen, T.-E., Yang, S.-I., Ho, L.-T., Tsai, K.-H., Chen, Y.-H., Chang, Y.-F., Lai, Y.-H., Wang, S.-S., Tsao, Y., and Wu, C.-C · 2016
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A deep learning framework for character motion synthesis and editing
Holden, D., Saito, J., and Komura, T · 2016
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Recognizing end-diastole and end-systole frames via deep temporal regression network
Kong, B., Zhan, Y., Shin, M., Denny, T., and Zhang, S · 2016
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Hierarchical deep reinforcement learning: Integrating temporal abstraction and intrinsic motivation
Kulkarni, T. D., Narasimhan, K., Saeedi, A., and Tenenbaum, J · 2016
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The curious robot: Learning visual representations via physical interactions
Pinto, L., Gandhi, D., Han, Y., Park, Y.-L., and Gupta, A · 2016
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Audio set: An ontology and human-labeled dataset for audio events
Gemmeke, J. F., Ellis, D. P., Freedman, D., Jansen, A., Lawrence, W., Moore, R. C., Plakal, M., and Ritter, M · 2017
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Factorization tricks for lstm networks
Kuchaiev, O. and Ginsburg, B · 2017
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1 year, 1000 km: The oxford robotcar dataset
Maddern, W., Pascoe, G., Linegar, C., and Newman, P · 2017
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Unimib shar: A dataset for human activity recognition using acceleration data from smartphones
Micucci, D., Mobilio, M., and Napoletano, P · 2017
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Total capture: 3d human pose estimation fusing video and inertial sensors
Trumble, M., Gilbert, A., Malleson, C., Hilton, A., and Collomosse, J · 2017
Cited alongside, same era.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
Cited alongside, same era.
Gelsight: High-resolution robot tactile sensors for estimating geometry and force
Yuan, W., Dong, S., and Adelson, E. H · 2017
Cited alongside, same era.
More than a feeling: Learning to grasp and regrasp using vision and touch
Calandra, R., Owens, A., Jayaraman, D., Lin, J., Yuan, W., Malik, J., Adelson, E. H., and Levine, S · 2018
Cited alongside, same era.
Scalability in perception for autonomous driving: Waymo open dataset
Sun, P., Kretzschmar, H., Dotiwalla, X., Chouard, A., Patnaik, V., Tsui, P., Guo, J., Zhou, Y., Chai, Y., Caine, B., et al · 2020
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Ptb-xl, a large publicly available electrocardiography dataset
Wagner, P., Strodthoff, N., Bousseljot, R.-D., Kreiseler, D., Lunze, F. I., Samek, W., and Schaeffter, T · 2020
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Reskin: versatile, replaceable, lasting tactile skins
Bhirangi, R., Hellebrekers, T., Majidi, C., and Gupta, A · 2021
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Deep learning for sensor-based human activity recognition: Overview, challenges, and opportunities
Chen, K., Zhang, D., Yao, L., Guo, B., Yu, Z., and Liu, Y · 2021
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Neural rough differential equations for long time series
Morrill, J., Salvi, C., Kidger, P., and Foster, J · 2021
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Chen, C., Zhao, P., Lu, C. X., Wang, W., Markham, A., and Trigoni, N · 2018
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
Cited alongside, same era.
Adaptive linear quadratic attitude tracking control of a quadrotor uav based on imu sensor data fusion
Koksal, N., Jalalmaab, M., and Fidan, B · 2018
Cited alongside, same era.
An any-resolution pressure localization scheme using a soft capacitive sensor skin
Sonar, H. A., Yuen, M. C., Kramer-Bottiglio, R., and Paik, J · 2018
Cited alongside, same era.
A new silicone structure for uskin—a soft, distributed, digital 3-axis skin sensor and its integration on the humanoid robot icub
Tomo, T. P., Regoli, M., Schmitz, A., Natale, L., Kristanto, H., Somlor, S., Jamone, L., Metta, G., and Sugano, S · 2018
Cited alongside, same era.
Speech commands: A dataset for limited-vocabulary speech recognition
Warden, P · 2018
Cited alongside, same era.
Ridi: Robust imu double integration
Yan, H., Shan, Q., and Furukawa, Y · 2018
Cited alongside, same era.
Rusch, T. K., Mishra, S., Erichson, N. B., and Mahoney, M. W · 2021
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Hihar: A hierarchical hybrid deep learning architecture for wearable sensor-based human activity recognition
Thu, N. T. H. and Han, D. S · 2021
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Hungry hungry hippos: Towards language modeling with state space models
Fu, D. Y., Dao, T., Saab, K. K., Thomas, A. W., Rudra, A., and Ré, C · 2022
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Vector: A versatile event-centric benchmark for multi-sensor slam
Gao, L., Liang, Y., Yang, J., Wu, S., Wang, C., Chen, J., and Kneip, L · 2022
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It’s raw! audio generation with state-space models
Goel, K., Gu, A., Donahue, C., and Ré, C · 2022
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Closed-form continuous-time neural networks
Hasani, R., Lechner, M., Amini, A., Liebenwein, L., Ray, A., Tschaikowski, M., Teschl, G., and Rus, D · 2022
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Mega: moving average equipped gated attention
Ma, X., Zhou, C., Kong, X., He, J., Gui, L., Neubig, G., May, J., and Zettlemoyer, L · 2022
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Simplified state space layers for sequence modeling
Smith, J. T., Warrington, A., and Linderman, S. W · 2022
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Holo-dex: Teaching dexterity with immersive mixed reality
Arunachalam, S. P., Güzey, I., Chintala, S., and Pinto, L · 2023
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All the feels: A dexterous hand with large-area tactile sensing
Bhirangi, R., DeFranco, A., Adkins, J., Majidi, C., Gupta, A., Hellebrekers, T., and Kumar, V · 2023
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Mamba: Linear-time sequence modeling with selective state spaces
Gu, A. and Dao, T · 2023
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Resurrecting recurrent neural networks for long sequences
Orvieto, A., Smith, S. L., Gu, A., Fernando, A., Gulcehre, C., Pascanu, R., and De, S · 2023
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Hyena hierarchy: Towards larger convolutional language models
Poli, M., Massaroli, S., Nguyen, E., Fu, D. Y., Dao, T., Baccus, S., Bengio, Y., Ermon, S., and Ré, C · 2023
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S4sleep: Elucidating the design space of deep-learning-based sleep stage classification models
Wang, T. and Strodthoff, N · 2023
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