Abstract: Methods and systems for user-selected viewpoint rendering of a virtual meeting are provided herein. First image data generated by a first client device during a virtual meeting and second image data generated by a second client device during a virtual meeting is obtained. The first image data depicts object(s) captured from a first vantage point and the second image data depicts the object(s) captured from a second vantage point. A request is received from a third client device for third image data depicting the object(s) captured from a third vantage point. The third image data depicting the object(s) corresponding to the third vantage point is generated based on the first image data and the second image data. A rendering of the third image data is provided for presentation via a graphical user interface (GUI) of the third client device during the virtual meeting in accordance with the request.

Abstract: Certain aspects of the present disclosure provide techniques for improved machine learning using gradient pruning, comprising computing, using a first batch of training data, a first gradient tensor comprising a gradient for each parameter of a parameter tensor for a machine learning model; identifying a first subset of gradients in the first gradient tensor based on a first gradient criteria; and updating a first subset of parameters in the parameter tensor based on the first subset of gradients in the first gradient tensor.

Abstract: Systems and techniques are described herein for performing optical flow estimation between one or more frames. For example, a process can include determining a subset of pixels of at least one of a first frame and a second frame, and generating a mask indicating the subset of pixels. The process can include determining, based on the mask, one or more features associated with the subset of pixels of at least the first frame and the second frame. The process can include determining optical flow vectors between the subset of pixels of the first frame and corresponding pixels of a second frame. The process can include generating an optical flow map for the second frame using the optical flow vectors.

Abstract: Certain aspects of the present disclosure provide techniques for performing tabular convolution, including performing a tabularization operation on input data to generate a tabularized representation of the input data and performing a convolution operation using the tabularized representation of the input data to generate a convolution output.

Abstract: A method includes receiving, as input, reference audio data representing a reference audio signal captured by an audio input device. The method also includes receiving, as input, from a beamformer, spatially-filtered audio data representing an output of the beamformer, the beamformer configured to spatially filter, based on additional audio data captured by one or more additional audio input devices, the reference audio data to attenuate one or more interfering signals in the spatially-filtered audio data. The method processes, using a trained guided speech-enhancement network, the reference audio data and the spatially-filtered audio data to generate, as output, enhanced audio data, the guided speech-enhancement network processing the reference audio data and the spatially-filtered audio data to further attenuate, in the enhanced audio data, the one or more interfering signals attenuated by the beamformer.

Abstract: A computer-implemented method includes receiving a first input. The first input is interpolated based on a first shift along a first dimension and a second shift along a second dimension. A first output is generated based on the interpolated first input. The first output corresponds to a vectorized bilinear shift of the first input for use in place of grid sampling algorithms.

Abstract: Systems and techniques are described for performing supervised learning (e.g., semi-supervised learning, self-supervised learning, and/or mixed supervision learning) for optical flow estimation. For example, a method can include obtaining an image associated with a sequence of images and generating an occluded image. The occluded image can include at least one of the image with an occlusion applied to the image and a different image of the sequence of images with the occlusion applied. The method can include determining a matching map based at least on matching areas of the image and the occluded image and, based on the matching map, determining a loss term associated with an optical flow loss prediction associated with the image and the occluded image. The loss term may include a matched loss and/or other loss. Based on the loss term, the method can include training a network configured to determine an optical flow between images.

Abstract: Certain aspects of the present disclosure provide techniques and apparatus for messaging in a wireless communications system using neural networks. An example method generally includes receiving a first message and a second message, wherein the second message comprises a secret message to be hidden in the first message. The first message and second message are combined into a combined message. An emulation message is generated through an encoder neural network based on the combined message. The emulation message generally comprises a message decodable by a receiving device into the first message. The emulation message is output emulation message for transmission to the receiving device.

Abstract: A method including processing a stream of data in a sequence of tasks. The processing including receiving a first block of data of the stream of data, determining features associated with the first block of data, selecting, based on the features, one of a first a task to process the first block of data or a second task to process the first block of data and if the second task is selected, shift an output of the second task in time to align the output of the second task with a predicted output of the first task processing a second block of data of the stream of data.

Abstract: Certain aspects of the present disclosure provide techniques and apparatus for training a machine learning model. An example method generally includes partitioning a machine learning model into a plurality of partitions. A request to update a respective partition of the plurality of partitions in the machine learning model is transmitted to each respective participating device of a plurality of participating devices in a federated learning scheme, and the request may specify that the respective partition is to be updated based on unique data at the respective participating device. Updates to one or more partitions in the machine learning model are received from the plurality of participating devices, and the machine learning model is updated based on the received updates.

Abstract: Certain aspects of the present disclosure provide techniques for training and using machine learning models to predict locations of stationary and non-stationary objects in a spatial environment. An example method generally includes measuring, by a device, a plurality of signals within a spatial environment. Timing information is extracted from the measured plurality of signals. Based on a machine learning model, the measured plurality of signals within the spatial environment, and the extracted timing information, locations of stationary reflection points and locations of non-stationary reflection points in the spatial environment are predicted. One or more actions are taken by the device based on predicting the locations of stationary reflection points and non-stationary reflection points in the spatial environment.

Abstract: Systems and techniques are described herein for performing optical flow estimation for one or more frames. For example, a process can include determining an optical flow prediction associated with a plurality of frames. The process can include determining a position of at least one feature associated with a first frame and determining, based on the position of the at least one feature in the first frame and the optical flow prediction, a position estimate of a search area for searching for the at least one feature in a second frame. The process can include determining, from within the search area, a position of the at least one feature in the second frame.

Abstract: Certain aspects of the present disclosure provide techniques and apparatus for surrogated federated learning. A set of intermediate activations is received at a trusted server from a node device, where the node device generated the set of intermediate activations using a first set of layers of a neural network. One or more weights associated with a second set of layers of the neural network are refined using the set of intermediate activations, and one or more weight updates corresponding to the refined one or more weights are transmitted to a federated learning system.

Abstract: Certain aspects of the present disclosure provide techniques and apparatus for feature masking. A feature tensor is accessed in a neural network, and a feature mask is generated by processing the feature tensor using a masking subnetwork, where the masking subnetwork was trained based at least in part on a polarization constraint and an activation constraint to generate feature masks. A masked feature tensor is generated based on the feature tensor and the feature mask, and an output inference is generated using the neural network based at least in part on the masked feature tensor.

Abstract: Certain aspects of the present disclosure provide techniques for desparsified convolution. A weight tensor having unstructured sparsity is accessed, and a densified weight tensor is generated based on the weight tensor by directionally squeezing the weight tensor to remove sparse values, and generating a sparsity map based on the directional squeezing. The densified weight tensor and sparsity map are output for use in a convolutional neural network.

Abstract: Certain aspects of the present disclosure provide techniques for desparsified convolution. An activation tensor is received, and a convolution output is generated for the activation tensor, comprising: selecting a subset of weight elements, corresponding to a set of non-zero elements in the activation tensor, from a weight tensor, and multiplying the set of non-zero elements and the set of weight elements.

Abstract: Methods, systems, and devices for wireless communications are described. A transmitter may transmit control signaling indicating a common resource commonly allocated to a set of receivers, a dedicated resource allocated to a first receiver, and a ramification coding structure that indicates a common code segment and a dedicated code segment of a ramification codeword. The transmitter may determine the ramification coding structure based on common data for multiple receivers, a quantity of hierarchical levels, a machine learning model, or any combination thereof. The transmitter may encode common and dedicated data into at least one ramification codeword in a dynamic signaling format using the ramification coding structure, and transmit the at least one ramification codeword to the first receiver via the common and dedicated resources. Being aware of the ramification coding structure, the first receiver may decode the ramification codeword transmitted by the transmitter in the dynamic signaling format.

Abstract: Certain aspects of the present disclosure provide techniques for machine learning-based deblurring. An input image is received, and a deblurred image is generated based on the input image using a neural network, comprising: generating a feature tensor by processing the input image using a first portion of the neural network, generating a motion mask by processing the feature tensor using a motion portion of the neural network, and generating the deblurred image by processing the feature tensor and the motion mask using a deblur portion of the neural network.

Abstract: Certain aspects of the present disclosure provide techniques and apparatus for messaging in a wireless communications system using neural networks. An example method generally includes receiving a first message and a second message, wherein the second message comprises a secret message to be hidden in the first message. The first message and second message are combined into a combined message. An emulation message is generated through an encoder neural network based on the combined message. The emulation message generally comprises a message decodable by a receiving device into the first message. The emulation message is output emulation message for transmission to the receiving device.

Abstract: Disclosed are systems, methods, and non-transitory media for performing radio frequency sensing detection operations. For instance, radio frequency data can be received that is associated with at least one wireless device. The radio frequency data can be based on radio frequency signals reflected from a first object and received by the at least one wireless device. Training label data can also be obtained (e.g., from a labeling device, from the at least one wireless device, etc.). The training label data can be based at least in part on the first object and input data (e.g., received by the labeling device, the at least one wireless device, etc.). A sensing model can be generated based on the radio frequency data and the training label data.