2019
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Main content start
Results for: 2019
- Abuzaid, Firas, Peter Kraft, Sahaana Suri, Edward Gan, Eric Xu, Atul Shenoy, Asvin Anathanarayan, John Sheu, Erik Meijer, Xi Wu, Jeffery Naughton, Peter Bailis, and Matei Zaharia. “DIFF: A Relational Interface for Large-Scale Data Explanation”. International Conference on Very Large Data Bases (VLDB), 2019.
- Senanayke, Ransalu, Maneekwan Toyungyernsub, Mingyu Wang, Mykel Kochenderfer, and Mac Schwager. “Directional Primitives for Uncertainty-Aware Motion Prediction in Urban Environments”. Robotics: Science and Systems Workshop on Scene and Situation Understanding for Autonomous Driving, 2019, 2019.
- Wang, Mingyu, and Mac Schwager. “Distributed Collision Avoidance of Multiple Robots With Probabilistic Buffered Voronoi Cells”. International Symposium on Multi-Robot and Multi-Agent Systems, 2019.
- Fang, Kuan, Yuke Zhu, Animesh Garg, Silvio Savarese, and Fei-Fei Li. “Dynamics Learning With Cascaded Variational Inference for Multi-Step Manipulation”. Conference on Robot Learning (CoRL), 2019.
- Biyik, Erdem, Jonathan Margoliash, Shahrouz Alimo, and Dorsa Sadigh. “Efficient and Safe Exploration in Deterministic Markov Decision Processes With Unknown Transition Models”. American Control Conference (ACC), 2019.
- Tai, Kai Sheng, Peter Bailis, and Gregory Vailant. “Equivariant Transformer Networks”. International Conference on Machine Learning (ICML), 2019.
- Itkina, Masha, Boris Ivanovic, Rensalu Senanayake, Mykel Kochenderfer, and Marco Pavone. “Evidential Disambiguation of Latent Multimodality in Conditional Variational Autoencoders”. Workshop on Bayesian Deep Learning, Advances in Neural Information Processing Systems, 2019.
- Liu, Xingyu, Charles Qi, and Leonidas Guibas. “FlowNet3D: Learning Scene Flow in 3D Point Clouds”. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
- Wang, Mingyu, Zijian Wang, John Talbot, Christian Gerdes, and Mac Schwager. “Game Theoretic Planning for Self-Driving Cars in Competitive Scenarios”. Robotics: Science and Systems (RSS), 2019.
- Song, Yang, and Stefano Ermon. “Generative Modeling by Estimating Gradients of the Data Distribution”, Stanford University, Computer Science Department.
- Li, Eric, and Roberto Martin-Martin. “HRL4IN: Hierarchical Reinforcement Learning ForInteractive Navigation With Mobile Manipulators”. Conference on Robot Learning (CoRL), 2019.
- Stroppa, Fabio, Ming Luo, Giada Gerboni, Margaret Coad, Julie Walker, and Allison Okamura. “Human-Centered Control of a Growing Soft Robot for Object Manipulation”. Work-In-Progress for 2019 IEEE World Haptics Conference (WHC), 2019.
- Zhou, Sharon, Mitchell Gordon, Ranjay Krishna, Austin Narcomey, Fei-Fei Li, and Michael Bernstein. “HYPE: A Benchmark for Human EYe Perceptual Evaluation of Generative Models”. Advances in Neural Information Processing Systems, 2019.
- Kwon, Minae, Mengix Li, Alexandre Bucquet, and Dorsa Sadigh. “Influencing Leading and Following in Human-Robot Teams”. Robotics: Science and Systems (RSS), 2019.
- Krishna, Ranjay, Michael Bernstein, and Fei-Fei Li. “Information Maximizing Visual Question Generation”. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
- Semmens, Robert, Nikolas Martelaro, Pushyami Kaveti, Simon Stent, and Wendy Ju. “Is Now A Good Time? An Empirical Study of Vehicle-Driver Communication Timing”. ACM Conference for Human Factors in Computing, 2019.
- Wang, Jui-Hsien, and Doug James. “KleinPAT: Optimal Mode Conflation for Time-Domain Precomputation of Acoustic Transfer”, SIGGRAPH 2019.
- Wang, He, Soeren Pirk, Ersin Yumer, Vladimir Kim, Ozan Sener, Srinath Sridhar, and Leonidas Guibas. “Learning a Generative Model for Multi-Step Human–Object Interactions from Videos”. Computer Graphics Forum, 2019.
- Cao, Kaidi, Colin Wei, Adrien Gaidon, Nikos Arechiga, and Tengyu Ma. “Learning Imbalanced Datasets With Label-Distribution-Aware Margin Loss”. Advances in Neural Information Processing Systems, 2019.
- Ferreira, Fabio, Lin Shao, Tamim Asfour, and Jeannette Bohg. “Learning Visual Dynamics Models of Rigid Objects Using Relational Inductive Biases”. NeurIPS 2019 Graph Representation Learning Workshop, 2019.