Abstract: A computer-implemented method for estimating a pose of an object includes receiving, at a pose estimation model, image data comprising a plurality of two-dimensional (2D) images of an object. Each 2D image of the plurality of 2D images has a different pose. The pose estimation model aligns a first 2D image of the plurality of 2D images with a second 2D image of the plurality of 2D images based on geometric properties related to the first 2D image and the second 2D image. The pose estimation model estimates a pose of the first 2D image and the second 2D image based on the plurality of 2D images and a loss associated with a common line between the first 2D image and the second 2D image.

Abstract: Methods, systems, and devices for wireless communications are described. A wireless device may receive an assignment of a set of resources associated with a channel where the set of resources includes a first subset of resources allocated for data transmission and a second subset of resources allocated for a reference signal. The wireless device may generate multiple channel estimations per layer of the channel and perform a refinement operation utilizing the estimations to generate a channel estimation associated with multiple layers. Each iteration of the refinement operation may include generating respective gradients associated with each per layer channel estimation; generating a current set of values of a latent variable; and modifying the channel estimations.

Abstract: Certain aspects of the present disclosure provide techniques and apparatus for training and using machine learning models to estimate a layout of a spatial area. An example method generally includes estimating a representation of a channel using a machine learning model trained to generate the estimated representation of the channel based on a location of a transmitter in a spatial environment, a location of a receiver in the spatial environment, and a three-dimensional representation of the spatial environment. One or more actions are taken based on the estimated representation of the channel.

Abstract: Methods, systems, and devices for wireless communications are described. A wireless device may receive an assignment of a set of resources associated with a channel, where the set of resources includes a first subset of resources allocated for data transmission and a second subset of resources allocated for a reference signal. The wireless device may generate, from the reference signal in accordance with a minimum mean square estimation (MMSE) operation, a first set of multiple channel estimations per layer of the channel. The wireless device may generate, in accordance with a nonlinear two-dimensional interpolation of the channel, a second set of multiple channel estimations per layer of the channel and may perform a refinement operation utilizing the estimations to generate a channel estimation associated with multiple layers.

Abstract: A processor-implemented method for estimating a channel by a deep generative model includes receiving, at a device, an observation of the channel and mapping, at the device, the observation to a mean value associated with the channel and a covariance matrix associated with the channel. The processor-implemented method also includes reconstructing, at the device, the channel based on the mean value and the covariance matrix.

Abstract: A processor-implemented method is presented. The method includes receiving an input sequence comprising a group of channel dynamics observations for a wireless communication channel. Each channel dynamics observation may correspond to a timing of a group of timings. The method also includes determining, via a recurrent neural network (RNN), a residual at each of the group of timings based on the group of channel dynamics observations. The method further includes updating Kalman filter (KF) parameters based on the residual and estimating, via the KF, a channel state based on the updated KF parameters.

Abstract: A method performed by a communication device includes generating an initial channel estimate of a channel for a current time step with a Kalman filter based on a first signal received at the communication device. The method also includes inferring, with a neural network, a residual of the initial channel estimate of the current time step. The method further includes updating the initial channel estimate of the current time step based on the residual.

Abstract: A processor-implemented method is presented. The method includes receiving an input sequence comprising a group of channel dynamics observations for a wireless communication channel. Each channel dynamics observation may correspond to a timing of a group of timings. The method also includes determining, via a recurrent neural network (RNN), a residual at each of the group of timings based on the group of channel dynamics observations. The method further includes updating Kalman filter (KF) parameters based on the residual and estimating, via the KF, a channel state based on the updated KF parameters.

Abstract: Techniques for self-service orchestration are disclosed. A system deploys instances of a self-service orchestration agent to tenant-specific software-as-a-service (SaaS) environments operating in a multi-tenant SaaS environment, without reconfiguring existing software in the tenant-specific SaaS environments. Each self-service orchestration agent includes functionality to configure one or more components. Each tenant-specific SaaS environment includes a dedicated set of software operating on a dedicated logical partition of hardware infrastructure. The system receives, via a self-service orchestration interface, a request to configure a component across the tenant-specific SaaS environments.

Abstract: A method performed by a communication device includes generating an initial channel estimate of a channel for a current time step with a Kalman filter based on a first signal received at the communication device. The method also includes inferring, with a neural network, a residual of the initial channel estimate of the current time step. The method further includes updating the initial channel estimate of the current time step based on the residual.

Abstract: Techniques for self-service orchestration are disclosed. A system deploys instances of a self-service orchestration agent to tenant-specific software-as-a-service (SaaS) environments operating in a multi-tenant SaaS environment, without reconfiguring existing software in the tenant-specific SaaS environments. Each self-service orchestration agent includes functionality to configure one or more components. Each tenant-specific SaaS environment includes a dedicated set of software operating on a dedicated logical partition of hardware infrastructure. The system receives, via a self-service orchestration interface, a request to configure a component across the tenant-specific SaaS environments.