Qualcomm Incorporated

Google Inc.

Jamie Menjay Lin

Jing Jiang

Yang Yang

Tingfang Ji

Joseph Binamira Soriaga

Gabi Sarkis

Fatih Murat Porikli

Tao Luo

Peter Pui Lok Ang

John Edward Smee

Krishna Kiran Mukkavilli

Edwin Chongwoo Park

Kambiz Azarian Yazdi

Rotem Cooper

Hao Xu

Siddhartha Mallik

Jilei Hou

Chong Li

Saurabha Rangrao Tavildar

Jin Won Lee

Debasmit Das

Nojun Kwak

Wanshi Chen

Xiaoxia Zhang

Jing X Sun

Naga Bhushan

Tamer Adel Kadous

Taesang Yoo

Simone Merlin

Bilal Sadiq

Ashok Mantravadi

Bibhu P Mohanty

Chih-Ping Li

Chirag Sureshbhai Patel

Michael Alexander Howard

Petru Cristian Budianu

Brian Michael Buesker

Chih Ping Li

Kai Wang

Daniel Hendricus Franciscus Dijkman

Chuo-Ling Chang

Farhad Ghazvinian Zanjani

Harshit Joshi

Vamsi Vegunta

Ilia Karmanov

Raamkumar Balamurthi

Parham Noorzad

Daniel Hendricus Franciscus Fontijne

Francesco Negro

Yash Sanjay Bhalgat

Hong Cai

Risheek Garrepalli

Rajeswaran Chockalingapuramravindran

Rui Liang

Shichong Peng

Janarbek Matai

Kristopher Urquhart

Shizhong Steve Han

Jisoo Jeong

Chao Wei

Changlong Xu

Jian Li

Gaojin Wu

Certain aspects of the present disclosure provide techniques for domain adaptation. An input tensor comprising channel state information (CSI) for a wireless signal is determined, where each channel in the input tensor corresponds to a respective degree of freedom (DoF) in the wireless signal. A domain-adapted tensor is generated by processing the input tensor using a domain-adaptation network comprising, for each respective DoF in the wireless signal, a respective convolution path. The domain-adapted tensor is provided to a neural network trained for position estimation.

Domain adaptation for wireless sensing

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.

Dynamic heterogeneous task processing

Aspects described herein provide a method of performing phase selective convolution, including: receiving multi-phase pre-activation activation data; partitioning the multi-phase pre-activation data; applying a first activation function to the set of first phase pre-activation data to form a set of first phase activation output; convolving the set of first phase activation output with a first convolution kernel to form a first phase output feature map; negating the set of second phase activation data; applying a second activation function to the negated set of second phase pre-activation data to form a set of second phase activation output; convolving the set of second phase activation output with a second convolution kernel to form a second phase output feature map; negating the second phase output feature map; and training the neural network based on the first phase output feature map and the second phase output feature map.

Phase selective convolution with dynamic weight selection

Dimensional decomposition convolution systems and techniques are described. A system receives a tensor including a first number of dimensions. The system processes a variant of the tensor using a convolution function including a second number of dimensions to generate a processed tensor. The first number of dimensions is greater than the second number of dimensions. A plurality of dimensions from the first number of dimensions of the tensor are grouped into a dimension of the variant of the tensor to reduce dimensionality of the variant of the tensor to the second number of dimensions.

Systems and methods for recursive dimensional decomposition convolution

Certain aspects of the present disclosure provide techniques and apparatus for efficient processing of visual content using machine learning models. An example method generally includes generating, from an input, an embedding tensor for the input. The embedding tensor for the input is projected into a reduced-dimensional space projection of the embedding tensor based on a projection matrix. An attention value for the input is derived based on the reduced-dimensional space projection of the embedding tensor and a non-linear attention function. A match, in the reduced-dimensional space, is identified between a portion of the input and a corresponding portion of a target against which the input is evaluated based on the attention value for the input. One or more actions are taken based on identifying the match.

Efficient neural-network-based processing of visual content

Certain aspects of the present disclosure provide techniques for generating fine depth maps for images of a scene based on semantic segmentation and segment-based refinement neural networks. An example method generally includes generating, through a segmentation neural network, a segmentation map based on an image of a scene. The segmentation map generally comprises a map segmenting the scene into a plurality of regions, and each region of the plurality of regions is generally associated with one of a plurality of categories. A first depth map of the scene is generated through a first depth neural network based on a depth measurement of the scene. A second depth map of the scene is generated through a depth refinement neural network based on the segmentation map and the first depth map. One or more actions are taken based on the second depth map of the scene.

Depth map completion in visual content using semantic and three-dimensional information

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.

Learning-based dynamic signal formatting with ramification coding

Certain aspects of the present disclosure provide techniques for machine learning using basis decomposition, comprising receiving a first runtime record, where the first runtime record includes RF signal data collected in a physical space; processing the first runtime record using a plurality of basis machine learning (ML) models to generate a plurality of inferences; aggregating the plurality of inferences to generate a prediction comprising a plurality of coordinates; and outputting the prediction, where the plurality of coordinates indicate a location of a physical element in a physical space.

Data efficient learning and rapid domain adaptation for wireless positioning and tracking

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.

Gradient pruning for efficient training of machine learning models

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.

Sparse optical flow estimation

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.

Sparse-aware convolution and techniques for acceleration thereof

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.

Supervised learning and occlusion masking for optical flow estimation

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.

Vectorized bilinear shift for replacing grid sampling in optical flow estimation

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.

Message embedding and emulation in entropy encoder-decoder networks

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.

Volumetric sampling with correlative characterization for dense estimation

Growing community of inventors

San Diego, CA, United States of America

Jamie Menjay Lin