Microsoft Technology Licensing, LLC

Texas Instruments Corporation

National Semiconductor Corporation

Nokia Siemens Networks Oy

Black Sesame Technologies Inc.

Nokia Solutions and Networks Oy

University of Colorado

Zheng Li

Jan Erik Johan Berglund

Wenwu Zhu

Tawen Mei

Yang Yang

Wai Cheong Chan

Zheng Qi

Tie Wang

Roberto R Molinari

Boyi Chen

Hongzhi Li

Yanbin Jiang

Brett E Smith

Deepak Agarwal

Shaunak Chatterjee

Bo Long

Md Abidur Rahman

Bee-Chung Chen

Qun Gu

Mohamed Gamal Mohamed Mahmoud

Parul Jain

Zuoguan Wang

Ravi Kiran Holur Vijay

Chao Zhang

Romer E Rosales

Ying Xuan

Yi Wang

Xiaofeng Wang

Huijuan Li

Kevin Tang

Parinkumar D Shah

Shubham Gupta

Shilpa Gupta

Xi Chen

Xie Lu

Tian Zhou

Pujita Mathur

Kexin Nie

Divye Kapoor

Fenglin Li

Chenghao Zhang

Mark Jerome U Cruz

Marco Varela Alvarado

Matthew R Guevara

Brian Hung-Chung Leung

Tao Zheng

Manasi R Deshpande

Shidong Yan

Zhentao Xu

Yen-Jung Chang

Yunsong Meng

Moustafa Mohamed

Alan Mickelson

Kwei-you Tao

Brett Konold

Heidi Jaywing Wang

Jan Berglund

Embodiments of the disclosed technologies include parsing a query into a first query portion and at least one second query portion, matching an embedding of the at least one second query portion with an embedding that corresponds to a portion of a document of a document set, mapping the portion of the document to a first node of a graph; by a generative artificial intelligence model, constructing a graph query based on at least the first node, executing the graph query on the graph to identify a second node of the graph, extracting a path from the graph, and configuring the path for output at a device.

Generative graph-enhanced information retrieval

An example fused convolutional layer, comprising, a comparator capable of reception of a first zero point and a multiply-accumulation result, a first multiplexer coupled to the comparator, wherein the first multiplexer receives a plurality of power-of-two exponent values, a shift normalizer, coupled to the first multiplexer, wherein the shift normalizer is capable of receiving the multiply-accumulation result and the plurality of power-of-two exponent values, wherein the shift normalizer limits a quantization of the multiply-accumulation result to a power-of-two scale and a second multiplexer coupled to an output of the shift normalizer, the first multiplexer and receives a second zero point and outputs an activation.

Integer-based fused convolutional layer in a convolutional neural network

Methods, systems, and computer programs are presented for providing a unified search tool for searching data in multiple domains. One method includes an operation for causing presentation of a user interface (UI) for entering one or more search parameters to search items in two or more domains. Each domain is owned by a different entity and data for each domain is acquired by the corresponding entity. Further, the method includes operations for receiving one or more search parameters values, searching, in response to the received search parameter values, the two or more domains based on the search parameters values, and ranking separately search results for each domain. Further, the method includes causing presentation in the UI of the search results for the two or more domains based on the ranking, the search results for each domain being presented in a separate results list.

Unified search for dual domains

The disclosed embodiments provide a system for streamlining machine learning. During operation, the system determines a resource overhead for a baseline version of a machine learning model that uses a set of features to produce entity rankings and a number of features to be removed to lower the resource overhead to a target resource overhead. Next, the system calculates importance scores for the features, wherein each importance score represents an impact of a corresponding feature on the entity rankings. The system then identifies a first subset of the features to be removed as the number of features with lowest importance scores and trains a simplified version of the machine learning model using a second subset of the features that excludes the first subset of the features. Finally, the system executes the simplified version to produce new entity rankings.

Feature removal framework to streamline machine learning

An extensible multi-precision data pipeline system, comprising, a local buffer that stores an input local data set in a local storage format, an input tensor shaper coupled to the local buffer that reads the input local data set and converts the input local data set into an input tensor data set having a tensor format of vector width N by tensor length L, a cascaded pipeline coupled to the input tensor shaper that routes the input tensor data set through at least one function stage resulting in an output tensor data set, an output tensor shaper coupled to the cascaded pipeline that converts the output tensor data set into an output local data set having the local storage format and wherein the output tensor shaper writes the output local data set to the local buffer.

Extensible multi-precision data pipeline for computing non-linear and arithmetic functions in artificial neural networks

Technologies for monitoring performance of a machine learning model include receiving, by an unsupervised anomaly detection function, digital time series data for a feature metric; where the feature metric is computed for a feature that is extracted from an online system over a time interval; where the machine learning model is to produce model output that relates to one or more users' use of the online system; using the unsupervised anomaly detection function, detecting anomalies in the digital time series data; labeling a subset of the detected anomalies in response to a deviation of a time-series prediction model from a predicted baseline model exceeding a predicted deviation criterion; creating digital output that identifies the feature as associated with the labeled subset of the detected anomalies; causing, in response to the digital output, a modification of the machine learning model.

Machine learning model monitoring

Techniques for enhancing usability and electronic resource efficiency using comment relevance are disclosed herein. In some embodiments, a comment relevance system receives member feature data, including professional identity data such as professional experience or professional skill, and at least one of comment feature data and commenter feature data for comments for post on a social networking service, generates a score for each comment based on the member feature data and the comment or commenter feature data, ranks the comments based on the scores, and causes one or more of the comments to be displayed in a particular position based on the ranking. In some embodiments, the member feature data comprises professional identity data of a member of the social networking service, the comment feature data indicates at least one attribute of the comment, and the commenter feature data indicates at least one attribute of the commenter of the comment.

Usability and resource efficiency using comment relevance

A system and method for intermediate landing page rerouting are provided. In example embodiments, determine whether a webpage associated with a hyperlink has corresponding social network activities. Extract content from the webpage determined to have corresponding social network activities. In response to a selection of the hyperlink, reroute a web browser to an intermediate landing page. Cause presentation, at a user interface, of the extracted content and the corresponding social network activities.

Rerouting to an intermediate landing page

A variation-tolerant receiver includes a plurality of receiver resonators configured to demultiplex a multiplexed modulated signal received from at least one wavelength division multiplexing (WDM) transmitter having a plurality of transmitter resonators and wherein operationally each of the receiver resonators has a receiver resonance linewidth not exceeding a minimum spacing between two adjacent transmitter resonances of the transmitter resonators, the receiver resonances collectively spanning a free spectral range of one of the receiver resonators such that the variation-tolerant receiver achieves gapless spectral response. A method passively compensates transmitter-receiver channel mismatch in a transceiver by: separating a broadband carrier into a plurality of narrowband transmitter carriers; modulating the narrowband transmitter carriers to form a plurality of modulated signals; multiplexing each of the modulated signals to form a multiplexed modulated signal; and demultiplexing the multiplexed modulated signal onto a plurality of receiver channels.

Variation-tolerant wavelength division multiplexing receiver and transceiver, and associated methods

A circuit for sensing gate voltage of a power FET. A switching circuit includes a switching FET having a high voltage rating, its drain coupled to the gate of the power FET, and its source coupled to an output node. A first feedback loop is coupled to the gate of the switching FET to facilitate sensing rising gate voltage. A second feedback loop is coupled to the gate of the switching FET to facilitate sensing falling gate voltage.

Fast high-side power FET gate sense circuit for high voltage applications

Predictive, multi-layer caching architectures may be used to predict which elements a user is most likely to navigate to within a collection of elements associated with a predefined layout and, in response, to increase the accessibility of these elements to a client device of the user. For instance, the techniques may utilize a predictive, multi-layer caching architecture for storing these predicted elements to decrease the latency to render these images if the user navigates within the collection of elements in the predicted manner. The collection of elements may comprise images (e.g., a 3D model, a map, etc.), video files, audio files, text files, or any other type of file that is consumable on a client device.

Predictive, multi-layer caching architectures

A wireless access node receives a first frame from a station through a first communication network such as an IEEE 802.11 network. The first frame comprises a source address identifying the station. The wireless access node applies a function to the source address to derive a destination address identifying an access controller coupled to the wireless access node through a second communication network such as an IEEE 802.3 network. The first frame is encapsulated in a second frame that comprises the destination address identifying the access controller and a source address identifying the wireless access node, respectively. The wireless access node sends the second frame to the access controller having the destination address through the second communication network. The access controller owns association and authentication state of the station, and shares the association and authentication state of the station with the wireless access node when needed to support desired service.

Providing station context and mobility in a wireless local area network having a split MAC architecture

An error amplifier includes a difference amplifier providing an error signal representing a difference in voltage between a feedback signal and a reference signal. The error amplifier further includes a compensation circuit limiting the rate of change of the error signal. The compensation circuit includes a switch that when activated effectively removes a circuit portion from the compensation circuit. A switch signal indicates for the switch to be activated when the feedback signal exceeds the reference signal by a predefined amount. The compensation circuit may further include a second switch that when activated effectively removes a second circuit portion from the compensation circuit. A second switch signal indicates for the second switch to be activated when the feedback signal exceeds the reference signal by a second predefined amount.

Dynamic control of frequency compensation for improved over-voltage protection in a switching regulator

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San Jose, CA, United States of America

Zheng Li