Seuraa
Martin Engelcke
Martin Engelcke
Google DeepMind
Vahvistettu sähköpostiosoite verkkotunnuksessa deepmind.com - Kotisivu
Nimike
Viittaukset
Viittaukset
Vuosi
3D Semantic Segmentation with Submanifold Sparse Convolutional Networks
B Graham, M Engelcke, L van der Maaten
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018
18242018
Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks
M Engelcke, D Rao, DZ Wang, CH Tong, I Posner
IEEE International Conference on Robotics and Automation (ICRA), 2017
7082017
GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations
M Engelcke, AR Kosiorek, O Parker Jones, I Posner
International Conference on Learning Representations (ICLR), 2020
3082020
On the Limitations of Representing Functions on Sets
E Wagstaff, FB Fuchs, M Engelcke, I Posner, M Osborne
International Conference on Machine Learning (ICML), 2019
2282019
GENESIS-V2: Inferring Unordered Object Representations without Iterative Refinement
M Engelcke, OP Jones, I Posner
Advances in Neural Information Processing Systems (NeurIPS), 2021
1262021
Analyzing spatially-sparse data based on submanifold sparse convolutional neural networks
BT Graham, LJP van der Maaten, MH Engelcke
US Patent 11,544,550, 2023
862023
Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55
L Yi, L Shao, M Savva, H Huang, Y Zhou, Q Wang, B Graham, M Engelcke, ...
arXiv preprint arXiv:1710.06104, 2017
742017
Universal Approximation of Functions on Sets
E Wagstaff, FB Fuchs, M Engelcke, MA Osborne, I Posner
Journal of Machine Learning Research 23 (151), 1-56, 2022
732022
RELATE: Physically Plausible Multi-Object Scene Synthesis Using Structured Latent Spaces
S Ehrhardt, O Groth, A Monszpart, M Engelcke, I Posner, N Mitra, ...
Advances in Neural Information Processing Systems (NeurIPS), 2020
592020
APEX: Unsupervised, Object-Centric Scene Segmentation and Tracking for Robot Manipulation
Y Wu, OP Jones, M Engelcke, I Posner
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021
222021
A Neural Network and Method of Using a Neural Network to Detect Objects in an Environment
M Engelcke, D Rao, DZ Wang, CH Tong, I Posner
US Patent App. 16/334,815, 2020
222020
Reconstruction Bottlenecks in Object-Centric Generative Models
M Engelcke, O Parker Jones, I Posner
Workshop on Object-Oriented Learning at ICML 2020, 2020
182020
Reaching Through Latent Space: From Joint Statistics to Path Planning in Manipulation
CM Hung, S Zhong, W Goodwin, OP Jones, M Engelcke, I Havoutis, ...
IEEE Robotics and Automation Letters 7 (2), 5334-5341, 2022
162022
Scaling Instructable Agents Across Many Simulated Worlds
M Abi Raad, A Ahuja, C Barros, F Besse, A Bolt, A Bolton, B Brownfield, ...
arXiv preprint arXiv:2404.10179, 2024
152024
First Steps: Latent-Space Control with Semantic Constraints for Quadruped Locomotion
AL Mitchell, M Engelcke, O Parker Jones, D Surovik, S Gangapurwala, ...
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020
122020
VAE-Loco: Versatile Quadruped Locomotion by Learning a Disentangled Gait Representation
AL Mitchell, WX Merkt, M Geisert, S Gangapurwala, M Engelcke, ...
IEEE Transactions on Robotics, 2023
72023
Next Steps: Learning a Disentangled Gait Representation for Versatile Quadruped Locomotion
AL Mitchell, W Merkt, M Geisert, S Gangapurwala, M Engelcke, OP Jones, ...
IEEE International Conference on Robotics and Automation (ICRA), 2022
62022
Can foundation models actively gather information in interactive environments to test hypotheses?
NR Ke, DP Sawyer, H Soyer, M Engelcke, DP Reichert, DA Hudson, ...
arXiv preprint arXiv:2412.06438, 2024
22024
Efficient Object Detection and Discovery for Real-World Robotics Applications
M Engelcke
University of Oxford, 2020
12020
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Artikkelit 1–19