KalmanNet: Neural network aided Kalman filtering for partially known dynamics
State estimation of dynamical systems in real-time is a fundamental task in signal
processing. For systems that are well-represented by a fully known linear Gaussian state …
processing. For systems that are well-represented by a fully known linear Gaussian state …
Differentiable convex optimization layers
Recent work has shown how to embed differentiable optimization problems (that is,
problems whose solutions can be backpropagated through) as layers within deep learning …
problems whose solutions can be backpropagated through) as layers within deep learning …
Model-based deep learning: On the intersection of deep learning and optimization
Decision making algorithms are used in a multitude of different applications. Conventional
approaches for designing decision algorithms employ principled and simplified modelling …
approaches for designing decision algorithms employ principled and simplified modelling …
Learning convex optimization control policies
Many control policies used in applications compute the input or action by solving a convex
optimization problem that depends on the current state and some parameters. Common …
optimization problem that depends on the current state and some parameters. Common …
Optimization or architecture: How to hack kalman filtering
I Greenberg, N Yannay… - Advances in Neural …, 2024 - proceedings.neurips.cc
In non-linear filtering, it is traditional to compare non-linear architectures such as neural
networks to the standard linear Kalman Filter (KF). We observe that this mixes the evaluation …
networks to the standard linear Kalman Filter (KF). We observe that this mixes the evaluation …
Learning convex optimization models
A convex optimization model predicts an output from an input by solving a convex
optimization problem. The class of convex optimization models is large, and includes as …
optimization problem. The class of convex optimization models is large, and includes as …
Differentiable factor graph optimization for learning smoothers
A recent line of work has shown that end-to-end optimization of Bayesian filters can be used
to learn state estimators for systems whose underlying models are difficult to hand-design or …
to learn state estimators for systems whose underlying models are difficult to hand-design or …
EKFNet: Learning system noise statistics from measurement data
In this paper, to reduce the time and manpower spent on fine-tuning an extended Kalman
filter (EKF), we propose a new learning framework, EKFNet, for automatically estimating the …
filter (EKF), we propose a new learning framework, EKFNet, for automatically estimating the …
Least squares auto-tuning
ST Barratt, SP Boyd - Engineering Optimization, 2021 - Taylor & Francis
Least squares auto-tuning automatically finds hyper-parameters in least squares problems
that minimize another (true) objective. The least squares tuning optimization problem is non …
that minimize another (true) objective. The least squares tuning optimization problem is non …
Karnet: Kalman filter augmented recurrent neural network for learning world models in autonomous driving tasks
Autonomous driving has received a great deal of attention in the automotive industry and is
often seen as the future of transportation. The development of autonomous driving …
often seen as the future of transportation. The development of autonomous driving …