Royal Bank of Canada

Sportlogiq Inc.

Gregory Mori

Thibaut Durand

Andreas Steffen Michael Lehrmann

Megha Nawhal

Seyed Hossein Hajimirsadeghi

Leonid Sigal

Yanshuai Cao

Marcus Anthony Brubaker

Lei Chen

Luke Carey Bornn

Bo Chang

Ruizhi Deng

Jiawei He

Mehrsan Roshtkhari Javan

Jianhui Chen

Mengyao Zhai

Nazanin Mehrasa

Yu Gong

Yatao Zhong

Frederick Tung

Systems and methods for machine learning architecture for time series data prediction. The system may include a processor and a memory storing processor-executable instructions. The processor-executable instructions, when executed, may configure the processor to: obtain time series data associated with a data query; generate a predicted value based on a sampled realization of the time series data and a continuous time generative model, the continuous time generative model trained to define an invertible mapping to maximize a log-likelihood of a set of predicted values for a time range associated with the time series data; and generate a signal providing an indication of the predicted value associated with the data query.

Systems and methods for modeling continuous stochastic processes with dynamic normalizing flows

Systems and methods of generating interpretive data associated with data sets. Embodiments of systems may be for adapting Grad-CAM methods for embedding networks. The system includes a processor and a memory. The memory stores processor-executable instructions that, when executed, configure the processor to: obtain a subject data set; generate a feature embedding based on the subject data set; determine an embedding gradient weight based on a prior-trained embedding network and the feature embedding associated with the subject data set, the prior-trained embedding network defined based on a plurality of embedding gradient weights respectively corresponding to a feature map generated based on a plurality of training samples, and wherein the embedding gradient weight is determined based on querying a feature space for the feature embedding associated with the subject data set; and generate signals for communicating interpretive data associated with the embedding gradient weight.

System and method of machine learning using embedding networks

Variational Autoencoders (VAEs) have been shown to be effective in modeling complex data distributions. Conventional VAEs operate with fully-observed data during training. However, learning a VAE model from partially-observed data is still a problem. A modified VAE framework is proposed that can learn from partially-observed data conditioned on the fully-observed mask. A model described in various embodiments is capable of learning a proper proposal distribution based on the missing data. The framework is evaluated for both high-dimensional multimodal data and low dimensional tabular data.

System and method for machine learning architecture for partially-observed multimodal data

Effectively training machine learning systems with incomplete/partial labels is a practical, technical problem that solutions described herein attempt to overcome. In particular, an approach to modify loss functions on a proportionality basis is noted in some embodiments. In other embodiments, a graph neural network is provided to help identify correlations/causations as between categories. In another set of embodiments, a prediction approach is described to, based on originally provided labels, predict labels for unlabelled training samples such that the proportion of labelled labels relative to all labels is increased.

System and method for a convolutional neural network for multi-label classification with partial annotations

Systems and methods for knowledge distillation provide supervised training of a student network with a teacher network, including inputting a batch to the teacher network, inputting the batch to the student network, generating a teacher activation map at a layer of the teacher network, generating a student activation map at a layer of the student network corresponding to the layer of the teacher network, generating a pairwise teacher similarity matrix based on the teacher activation map, generating a pairwise student similarity matrix based on the student activation map, and minimizing a knowledge distillation loss defined as a difference between the pairwise teacher similarity matrix and the pairwise student similarity matrix.

System and method for knowledge distillation between neural networks

Systems and methods adapted for training a machine learning model to predict data labels are described. The approach includes receiving a first data set comprising first data objects and associated first data labels, and processing, with a user representation model, respective first data objects and associated data labels associated with a unique user representation by fusing the respective first data object and the associated first data labels. First data object representations of the respective first data objects are generated, and the first data object representations and the user representation model outputs are fused to create a user conditional object representation. The machine learning model updates corresponding parameters based on an error value based on a maximum similarity of the projections of the respective user conditional object representation and first data labels in a joint embedding space.

Systems and methods for learning user representations for open vocabulary data sets

A computer implemented system for generating one or more data structures is described, the one or more data structures representing an unseen composition based on a first category and a second category observed individually in a training data set. During training of a generator, a proposed framework utilizes at least one of the following discriminators&#x2014;three pixel-centric discriminators, namely, frame discriminator, gradient discriminator, video discriminator; and one object-centric relational discriminator. The three pixel-centric discriminators ensure spatial and temporal consistency across the frames, and the relational discriminator leverages spatio-temporal scene graphs to reason over the object layouts in videos ensuring the right interactions among objects.

System and method for generation of unseen composite data objects

Methods are provided for automatically analyzing and understanding activities and interactions. One method comprises receiving at least location information for one or more individual objects in a scene at a given time; applying at least one machine learning or artificial intelligence technique to automatically learn an informative representation of location trajectory data for each object; and identifying and analyzing individual and group activities in the scene based on the trajectory data. Another method comprises receiving at least location information for one or more individual objects in a scene at a given time; receiving visual information about the scene and at least one individual object in the scene; applying at least one machine learning or artificial intelligence technique to automatically learn an informative representation of location trajectory data, and to automatically learn an informative representation of visual appearance data; and combining the location trajectory and visual appearance data.

Systems and methods for behavior understanding from trajectories

A system and method are provided for object tracking in a sequence of images of a scene acquired by an imaging device. The method includes generating a representation of an object and its surroundings; generating a tracking score based on the representation of the object and its surroundings, and a classification scheme; detecting a peak in the tracking score corresponding to a location of the object in the image; and adjusting the representation of the object and its surroundings and the classification scheme according to one or more new appearances of the object.

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Vancouver, Canada

Gregory Mori