Samsung Electronics Co., Ltd.

The Royal Institution for the Advancement of Learning / Mcgill University

Huawei Technologies Co., Limited

Sportlogiq Inc.

Gregory Lewis Dudek

Xue Liu

Xi Chen

Di Wu

Ju Wang

Yi Tian Xu

Hang Li

Michael Jenkin

Jikun Kang

Intaik Park

Taeseop Lee

Chengming Hu

Seowoo Jang

David Meger

Chenyi Zhou

Dmitriy Rivkin

Amal Feriani

Tianyu Li

Jun Luo

Elmira Amirloo Abolfathi

Mehrsan Javan Roshtkhari

Francois Robert Hogan

Juan Camilo Gamboa Higuera

Sahand Rezaei-Shoshtari

Trevor Ablett

Charlotte Morissette

Affan Jilani

Jimmy Li

Kaleem Siddiqi

Yogesh Girdhar

Yasasa Abeysirigoonawardena

Florian Shkurti

A method performed by an electronic device, includes: obtaining an image of a set of markers; based on the image, detecting an arrangement of the set of markers; based on the arrangement of the set of markers, performing a measurement about a deformation of the set of markers; based on the measurement about the deformation of the set of markers, generating a plurality of signals; transforming the plurality of signals to a plurality of input commands. The plurality of input commands are used to control the electronic device.

Continuous tactile feedback for electronic devices

An electronic device for controlling a photographic system may obtain a video stream and a user query for a target event, obtain a set of photos from the video stream, obtain at least one photoshoot suggestion based on the user query via a language model, obtain a snapped photo for the target event based on the at least one photoshoot suggestion, in response to a given video frame included in the video stream satisfying a target content criterion, and output one or more photos selected from the set of photos and the snapped photo as event photos.

Apparatus and method for controlling a robot photographer with semantic intelligence

A server may receive a first traffic data and a second traffic data from a first base station and a second base station; obtain a first augmented traffic data for the first base station, based on the first traffic data and a subset data of the second traffic data; obtain a second augmented traffic data for the second base station, based on the second traffic data and a subset data of the first traffic data; obtain a first artificial intelligence (AI) model via imitation learning based on the first augmented traffic data; obtain a second AI model imitation learning based on the second augmented traffic data; obtain a generalized AI model via knowledge distillation from the first AI model and the second AI model; and predict a future traffic load of each of the first base station and the second base station based on the generalized AI model.

Multi-batch reinforcement learning via multi-imitation learning

The present disclosure provides methods, apparatuses, and computer-readable mediums for performing ultra-wideband (UWB) remote control. In some embodiments, the method includes broadcasting an initial control request. The method further includes receiving, from one or more remote devices, at least one reply message comprising identification information and power spectrum information. The method further includes estimating, for each of the one or more remote devices, an angle indicating a pointing direction to that remote device relative to the remote control device. The method further includes determining a selected remote device that is being pointed at by the remote control device. The method further includes sending, to the one or more remote devices, a control signal comprising the identification information of the selected remote device and a control message indicating an action to be performed by the selected remote device.

Ultra-wideband enabled one-for-all smart remote

A method performed by an optical tactile sensor comprising a camera, includes: measuring a plurality of relations between the optical tactile sensor and an object; detecting a contact of a membrane on the object; detecting a plurality of markers on the membrane; performing a plurality of first operations by using a tactile perception module; and performing a second operation by using a three-dimensional (3D) perception module, based on a result of the performing of the plurality of first operations.

Optical tactile sensor and method for estimating shape from touch

The present disclosure provides for methods, apparatuses, and non-transitory computer-readable storage media for load balancing traffic scenarios by a network device. In an embodiment, a method includes training a plurality of learning agents to load balance a respective plurality of traffic scenarios to obtain a plurality of control policies. The method further includes performing at least one clustering iteration. Each clustering iteration includes selecting a pair of control policies and merging the pair of control policies into a clustered control policy that replaces the pair of control policies. The method further includes determining to stop the performing of the at least one clustering iteration when a quantity of control policies remaining in the plurality of control policies meets a predetermined value. The method further includes deploying to each base station of a plurality of base stations a corresponding control policy from the plurality of control policies.

Traffic scenario clustering and load balancing with distilled reinforcement learning policies

A server may obtain teacher artificial intelligence (AI) models from source base stations; obtain target traffic data from a target base station; obtain an integrated teacher prediction based on the target traffic data by integrating teacher prediction results of the teacher AI models based on teacher importance weights; obtain a student AI model that is trained to converge a student loss on the target traffic data; update the teacher importance weights to converge a teacher loss between a student prediction of the student AI model on the target traffic data, and the integrated teacher prediction of the teacher AI models on the target traffic data; update the student AI model based on the updated teacher importance weights being applied to the teacher prediction results of the teacher AI models; and predict a communication traffic load of the target base station using the updated student AI model.

Method of load forecasting via knowledge distillation, and an apparatus for the same

A method of forecasting a future load may include: obtaining source data sets and a target data set that have been collected from a plurality of source base stations and a target base station, respectively; among a plurality of source machine learning models, selecting at least one machine learn source model that has a traffic load prediction performance higher than that of a target machine learning model through a negative transfer analysis; obtaining model weights to be applied to the target machine learning model and the selected at least one source machine learning model via an attention neural network that is jointly trained with the target machine learning model and the selected source machine learning models; obtaining a load forecasting model for the target base station by combining the target machine learning model and the selected at least one source machine learning model according to the model weights; and predicting a future communication traffic load of the target base station based on the load forecasting model.

Method of load forecasting via attentive knowledge transfer, and an apparatus for the same

A server may be provided to obtain a load balancing artificial intelligence (AI) model for a plurality of base stations in a communication system. The server may obtain teacher models based on traffic data sets collected from the base stations, respectively; perform a policy rehearsal process including obtaining student models based on knowledge distillation from the teacher models, obtaining an ensemble student model by ensembling the student models, and obtaining a policy model by interacting with the ensemble student mode; provide the policy model to each of the base stations for a policy evaluation of the policy model; and based on a training continue signal being received from at least one of the base stations as a result of the policy evaluation, update the ensemble student model and the policy model by performing the policy rehearsal process on the student models.

Method of performing communication load balancing with multi-teacher reinforcement learning, and an apparatus for the same

A server for predicting a future traffic load of a base station is provided. The server may obtain a first prediction model based on traffic data collected from the base station for a first period of time, obtain a second prediction model based on traffic data collected from the same base station for a second period time, and also based on knowledge transferred from the first prediction model. Each of the first prediction model and the second prediction model may include an encoder module, a reconstruction module, and a prediction module which are connected to form two paths, an encoder-reconstruction path and an encoder-prediction path, to preserve more information of historic traffic data.

Method of communication traffic prediction via continual learning with knowledge distillation, and an apparatus for the same

Rapid and data-efficient training of an artificial intelligence (AI) algorithm are disclosed. Ground truth data are not available and a policy must be learned based on limited interactions with a system. A policy bank is used to explore different policies on a target system with shallow probing. A target policy is chosen by comparing a good policy from the shallow probing with a base target policy which has evolved over other learning experiences. The target policy then interacts with the target system and a replay buffer is built up. The base target policy is then updated using gradients found with respect to the transition experience stored in the replay buffer. The base target policy is quickly learned and is robust for application to new, unseen, systems.

Load balancing using data-efficient learning

Transfer learning based on prediction determines a similarity between a source base station and a target base station. Importance of parameters is determined and training is adjusted to respect the importance of parameters. A lack of historical data is compensated by selecting a base station as source base station which has a larger amount of historical data.

Transfer learning of network traffic prediction model among cellular base stations

A method is provided. The method includes receiving a first dimension set, extracting a first latent feature set from the first dimension set, training a first base predictor based on the first feature set, generating a second dimension set based on the first dimension set, the second dimension set having fewer dimensions than the first dimension set, extracting a second latent feature set from the second dimension set, training a second base predictor based on the second feature set, and generating a traffic prediction based on the first base predictor and the second base predictor.

Communication load forecasting accuracy with adaptive feature boosting

A location-aware electronic device is provided. The electronic device trains feature extraction layers, reconstruction layers, and classification layers. The training may be based on a reconstruction loss and/or a clustering loss. The electronic device processes a fingerprint to obtain an augmented fingerprint using randomization based on statistics of the fingerprint. The feature extraction layers provide feature data to both the reconstruction layers and the classification layers. The classification layers operate on the codes to obtain an estimated location label. An application processor operates on the estimated location label to provide a location-aware application result to a person.

System and method for WiFi-based indoor localization via unsupervised domain adaptation

A method, computer program, and computer system are provided for load forecasting. Datasets corresponding to source machine learning models and a target domain base model are identified. A set of forecasting models corresponding to the identified datasets are learned. An ensemble model is determined from the learned set of forecasting models based on gradient boosting. An available resource is allocated based on the ensemble model.

Growing community of inventors

Montreal, Canada

Gregory Lewis Dudek