Machine learning research with positive real-world impact.

We develop industry leading machine learning systems. Our initiatives have the power to uplift large populations, while advancing the field of artificial intelligence.

Ambitious applied research, positive outcomes

Recent highlights

Our research is supported by access to massive datasets, close collaboration with world renowned academic faculty, and a uniquely scalable machine learning platform.

CVPR 2021 | Weakly Supervised Action Selection Learning in Video

CVPR 2021 | Weakly Supervised Action Selection Learning in Video

Weakly Supervised Action Selection Learning in Video

Abstract

Localizing actions in video is a core task in computer vision. The weakly supervised temporal localization problem investigates whether this task can be adequately solved with only video-level labels, significantly reducing the amount of expensive and error-prone annotation that is required. A common approach is to train a frame-level classifier where frames with the highest class probability are selected to make a video-level prediction. Frame-level activations are then used for localization. However, the absence of frame-level annotations cause the classifier to impart class bias on every frame. To address this, we propose the Action Selection Learning (ASL) approach to capture the general concept of action, a property we refer to as “actionness”.  Under ASL, the model is trained with a novel class-agnostic task to predict which frames will be selected by the classifier. Empirically, we show that ASL outperforms leading baselines on two popular benchmarks THUMOS-14 and ActivityNet-1.2, with 10.3% and 5.7% relative improvement respectively. We further analyze the properties of ASL and demonstrate the importance of actionness.

ICLR 2021 | C-Learning: Horizon-Aware Cumulative Accessibility Estimation

ICLR 2021 | C-Learning: Horizon-Aware Cumulative Accessibility Estimation

C-Learning: Horizon-Aware Cumulative Accessibility Estimation

Abstract

Multi-goal reaching is an important problem in reinforcement learning needed to achieve algorithmic generalization. Despite recent advances in this field, current algorithms suffer from three major challenges: high sample complexity, learning only a single way of reaching the goals, and difficulties in solving complex motion planning tasks. In order to address these limitations, we introduce the concept of cumulative accessibility functions, which measure the reachability of a goal from a given state within a specified horizon. We show that these functions obey a recurrence relation, which enables learning from offline interactions. We also prove that optimal cumulative accessibility functions are monotonic in the planning horizon. Additionally, our method can trade off speed and reliability in goal-reaching by suggesting multiple paths to a single goal depending on the provided horizon. We evaluate our approach on a set of multi-goal discrete and continuous control tasks. We show that our method outperforms state-of-the-art goal-reaching algorithms in success rate, sample complexity, and path optimality. Our code is available at this https URL, and additional visualizations can be found at this https URL.

The Web Conference 2021 | HGCF: Hyperbolic Graph Convolution Networks for Collaborative Filtering

The Web Conference 2021 | HGCF: Hyperbolic Graph Convolution Networks for Collaborative Filtering

HGCF: Hyperbolic Graph Convolution Networks for Collaborative Filtering

Abstract

Hyperbolic spaces offer a rich setup to learn embeddings with superior properties that have been leveraged in areas such as computer vision, natural language processing and computational biology. Recently, several hyperbolic approaches have been proposed to learn robust representations for users and items in the recommendation setting. However, these approaches don’t capture the higher order relationships that typically exist in the recommendation domain. Graph convolutional neural networks (GCNs) on the other hand excel atcapturing higher order information by applying multiple levels of aggregation to local representations. In this paper we combine these frameworks in a novel way, by proposing a hyperbolic GCN model for collaborative filtering. We demonstrate that our model can be effectively learned with a margin ranking loss, and show that hyperbolic space has desirable properties under the rank margin setting. At test time, inference in our model is done using the hyperbolic distance which preserves the structure of the learned space. We conduct extensive empirical analysis on three public benchmarks and compare against a large set of baselines. Our approach achieves highly competitive results and outperforms leading baselines including the Euclidean GCN counterpart. We further study the properties of the learned hyperbolic embeddings and show that they offer meaningful insights into the data.

npj | digital medicine: Predicting adverse outcomes due to diabetes complications

npj | digital medicine: Predicting adverse outcomes due to diabetes complications

Predicting adverse outcomes due to diabetes complications with machine learning using administrative health data

Abstract

Across jurisdictions, government and health insurance providers hold a large amount of data from patient interactions with the healthcare system. We aimed to develop a machine learning-based model for predicting adverse outcomes due to diabetes complications using administrative health data from the single-payer health system in Ontario, Canada. A Gradient Boosting Decision Tree model was trained on data from 1,029,366 patients, validated on 272,864 patients, and tested on 265,406 patients. Discrimination was assessed using the AUC statistic and calibration was assessed visually using calibration plots overall and across population subgroups. Our model predicting three-year risk of adverse outcomes due to diabetes complications (hyper/hypoglycemia, tissue infection, retinopathy, cardiovascular events, amputation) included 700 features from multiple diverse data sources and had strong discrimination (average test AUC = 77.7, range 77.7–77.9). Through the design and validation of a high-performance model to predict diabetes complications adverse outcomes at the population level, we demonstrate the potential of machine learning and administrative health data to inform health planning and healthcare resource allocation for diabetes management.

Characterizing early Canadian federal, provincial, territorial and municipal nonpharmaceutical interventions in response to COVID-19: a descriptive analysis

Characterizing early Canadian federal, provincial, territorial and municipal nonpharmaceutical interventions in response to COVID-19: a descriptive analysis

Characterizing early Canadian federal, provincial, territorial and municipal nonpharmaceutical interventions in response to COVID-19: a descriptive analysis

Abstract

Nonpharmaceutical interventions (NPIs) are the primary tools to mitigate early spread of the coronavirus disease 2019 (COVID-19) pandemic; however, such policies are implemented variably at the federal, provincial or territorial, and municipal levels without centralized documentation. We describe the development of the comprehensive open Canadian Non-Pharmaceutical Intervention (CAN-NPI) data set, which identifies and classifies all NPIs implemented in regions across Canada in response to COVID-19, and provides an accompanying description of geographic and temporal heterogeneity.

Our research areas include recommendation systems, computer vision, time series forecasting, and natural language processing.

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Big vision, deep roots

The co-founders of Layer 6, Jordan Jacobs and Tomi Poutanen, are also founders of the Vector Institute for Artificial Intelligence, and we maintain multiple research initiatives with Vector faculty. Current and former scientific advisors include professors Raquel Urtasun, Sanja Fidler, Rich Zemel, David Duvenaud, Laura Rosella and Scott Sanner.

Meaningful partnerships

Originally founded in 2011, Layer 6 now forms the AI research lab of TD Bank Group. Layer 6 impacts the lives of 25 million customers, helping more people achieve their financial goals. Partnerships with TD Securities provides Layer 6 with market data for training algo trading systems.

Layer 6 embraces opportunities to collaborate with Toronto’s world-leading medical research community, offering deep learning solutions to transform healthcare delivery and improve health outcomes. We are the first to deploy deep learning models on health data covering large population.

Passion to learn, driven to succeed

Our team represents 18 different countries of birth and we care deeply about fostering an inclusive culture where we learn from each other and win together. We are united by our passion for deep learning and a desire to apply our skills to have an outsized and positive impact on the future.

Meet some of our team

  • Tomi Poutanen

    Co-founder, Layer 6
    Chief AI Officer, TD Bank Group

    Tomi is a co-founder of Layer 6 which was acquired by TD Bank Group in January 2018 where he serves as the Chief AI Officer. Prior to Layer 6 Tomi founded three technology companies with exits to Microsoft and Yahoo!. Tomi is a co-founder of the Vector Institute, a world leading academic research institute for deep learning. He is a founding fellow at the Creative Destruction Lab, the world’s largest AI venture accelerator. Tomi holds an MSc in Computer Engineering and an MBA from University of Toronto. Most importantly, Tomi is a husband and a father to two daughters.

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  • Maksims Volkovs

    Co-founder & Head of Machine Learning, Layer 6
    Chief AI Scientist, TD Bank Group

    Maks is a co-founder of Layer 6 and the Chief AI Scientist at TD Bank Group. Maks received his PhD from the University of Toronto where he was part of the Machine Learning Group led by Geoffrey Hinton. He previously worked at Microsoft Research and at Credit Suisse in London, UK. Maks is the winner or medalist in multiple international machine learning competitions run by Kaggle (where his global peak ranking was #47 of over 75,000 data scientists), ACM RecSys (including winner of the 2017 and 2018 RecSys Challenge) and others. He has published over 15 papers and is co-inventor of 6 patents.

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  • Laura Doktorova

    Chief Architect

    Laura is the Chief Architect at Layer 6. She has a solid track record of building efficient technologies. Prior to Layer 6, Laura worked at Milq Inc. as the Chief Architect for 4 years and at Blackberry in various engineering roles for 10 years. Laura is passionate about entrepreneurship. She is a co-founder of Bebedo whose Inquiro™ technology provides high-speed spatio-temporal search on large volumes of time and location sensitive data. Laura received her degree from Belarusian State University of Informatics and Radioelectronics.

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  • Harry Braviner

    Machine Learning Scientist

    Harry is a Machine Learning Scientist at Layer 6. He is a mathematician and astrophysicist by training, and received his PhD, MMath and BA from Trinity College at the University of Cambridge. Before moving to Canada, Harry lived in London and worked for the research arm of a systematic hedge fund. His current research is focussed on reinforcement learning, and teaching machines to solve difficult control problems.

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  • Guangwei Yu

    Machine Learning Scientist

    Guang is leading the Computer Vision team at Layer 6. The Team achieved 2nd place at the Google Landmark Retrieval Challenge and 4th place at the RSNA Pneumonia Detection Challenge in 2018. Guang is also the medalist in multiple international machine learning competitions including the only repeat winner of the ACM RecSys Challenge and others. Guang received his MScAC in Computer Science and BASc in Engineering Science from University of Toronto.

    LinkedIn Github Google Research Website

  • Saba Zuberi

    Machine Learning Scientist

    Saba is a Machine Learning Scientist at Layer 6. She has years of data science experience and has previously worked at TD Bank Group in Toronto and at TaskRabbit in Silicon Valley. Saba is a Physicist with a PhD degree from University of Toronto. She completed her Post Doc research at the Lawrence Berkeley National Laboratory in California.

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  • Yichao Lu

    Machine Learning Scientist

    Yichao joined Layer 6 as a Machine Learning Scientist after a research internship at Layer 6 where he collaborated closely with the research fellows at the Vector Institute. Yichao received his MScAC degree in Computer Science at the University of Toronto. Before moving to Canada, Yichao lived in Shanghai and completed his bachelor’s degree from Fudan University.

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Develop your career at Layer 6

What’s in a name? Deep learning is inspired by the human brain. The cortex has six layers of neurons, where the sixth layer integrates the deeper processing to produce an action.

We’re growing our team exclusively with people driven to be at the top of the game in machine learning.

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Obtenez des renseignements en français

Layer 6 s’est engagé à servir la communauté francophone au meilleur de nos capacités. Pour obtenir des renseignements en français sur notre équipe ou des opportunités de carrière, veuillez écrire à l’adresse info@layer6.ai.