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In many robotic applications, it is crucial to maintain a belief about the state of a system, which serves as input for planning and decision making and provides feedback during task execution. Bayesian Filtering algorithms address this state estimation problem, but they require models of process dynamics and sensory observations and the respective noise characteristics of these models. Recently, multiple works have demonstrated that these models can be learned by end-to-end training through differentiab
Energy-Based Models (EBMs), also known as non-normalized probabilistic models, specify probability density or mass functions up to an unknown normalizing constant. Unlike most other probabilistic models, EBMs do not place a restriction on the tractab
Model-agnostic meta-learning (MAML) is arguably the most popular meta-learning algorithm nowadays, given its flexibility to incorporate various model architectures and to be applied to different problems. Nevertheless, its performance on few-shot cla
Reading and writing research papers is one of the most privileged abilities that a qualified researcher should master. However, it is difficult for new researchers (eg{students}) to fully {grasp} this ability. It would be fascinating if we could trai
Deep Neural Nets have hit quite a crest, But physical networks are where they must rest, And here we put them all to the test, To see which network optimization is best.
Real-time semantic segmentation on high-resolution videos is challenging due to the strict requirements of speed. Recent approaches have utilized the inter-frame continuity to reduce redundant computation by warping the feature maps across adjacent f