Samsung Electronics Co., Ltd.

The Royal Institution for the Advancement of Learning / Mcgill University

Huawei Technologies Canada Co., Ltd.

David Meger

Michael Jenkin

Xue Liu

Gregory Lewis Dudek

Elmira Amirloo Abolfathi

Tianyu Li

Juan Camilo Gamboa Higuera

Melissa Mozifian

Yogesh Girdhar

Sahand Rezaei-Shoshtari

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

A method of sensing an interaction with an object using a semitransparent tactile surface (STS) sensor having an image sensor and a semitransparent membrane includes capturing, by the image sensor, a stream of images; separating the stream of images into a visual stream including light traveling through the semitransparent membrane and a tactile stream including light reflecting off of the semitransparent membrane; and processing the visual stream and tactile stream through a multimodal deep neural network.

Semitransparent tactile surface sensor and a method of sensing an interaction with an object using the semitransparent tactile surface sensor

Method or system for reinforcement learning that simultaneously learns a DR distribution &#x3d5; while optimizing an agent policy &#x3a0; to maximize performance over the learned DR distribution; method or system for training a learning agent using data synthesized by a simulator based on both a performance of the learning agent and a range of parameters present in the synthesized data.

Learning domain randomization distributions for transfer learning

Growing community of inventors

Montreal, Canada

David Meger