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Iterative Amortized Inference

Iterative Amortized Inference

by Martina Haefeli | Jul 9, 2018 | Machine Learning, Visual Computing

Iterative Amortized Inference   We demonstrate the inference optimization capabilities of iterative inference models and show that they outperform standard inference models on several benchmark data sets of images and text. July 9, 2018International Conference on...
Computational Design of Robotic Devices from High-Level Motion Specifications

Computational Design of Robotic Devices from High-Level Motion Specifications

by Martina Haefeli | Jul 3, 2018 | Digital Fabrication, Robotics

Computational Design of Robotic Devices from High-Level Motion Specifications   We demonstrate the effectiveness of our computational design method by automatically creating a variety of robotic manipulators and legged robots. July 3, 2018IEEE Transactions on Robotics...
Automated Deep Reinforcement Learning Environment for Hardware of a Modular Legged Robot

Automated Deep Reinforcement Learning Environment for Hardware of a Modular Legged Robot

by Martina Haefeli | Jun 27, 2018 | Machine Learning, Robotics

Automated Deep Reinforcement Learning Environment for Hardware of a Modular Legged Robot   We present an automated learning environment for developing control policies directly on the hardware of a modular legged robot. June 27, 2018International Conference on...
Normalized Cut Loss for Weakly-supervised CNN Segmentation

Normalized Cut Loss for Weakly-supervised CNN Segmentation

by Martina Haefeli | Jun 18, 2018 | Video Processing, Visual Computing

Normalized Cut Loss for Weakly-supervised CNN Segmentation   Our normalized cut loss approach to segmentation brings the quality of weakly-supervised training significantly closer to fully supervised methods. June 18, 2018IEEE Conference on Computer Vision Pattern...
PhaseNet for Video Frame Interpolation

PhaseNet for Video Frame Interpolation

by Martina Haefeli | Jun 18, 2018 | Video Processing, Visual Computing

PhaseNet for Video Frame Interpolation   We propose a new approach, PhaseNet, that is designed to robustly handle challenging scenarios while also coping with larger motion. June 18, 2018IEEE Conference on Computer Vision Pattern Recognition (CVPR) 2018   Authors...
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