Doina Precup

Jesse Farebrother

Doctorat - McGill

Superviseur⋅e principal⋅e :

Marc Gendron-Bellemare

Doctorat - McGill

Superviseur⋅e principal⋅e :

Collaborateur·rice de recherche - Birla Institute of Technology

Jonathan Hu

Maîtrise recherche - McGill

Howard Huang

Doctorat - McGill

Haque Ishfaq

Collaborateur·rice alumni - McGill

Site web

Mohammad Sami Nur Islam Islam

Maîtrise recherche - McGill

Hangzhan Jin

Doctorat - Polytechnique

Doctorat - McGill

Postdoctorat - McGill

Jonathan Lebensold

Collaborateur·rice alumni - McGill

Collaborateur·rice alumni - McGill

Ray Luo

Doctorat - McGill

Superviseur⋅e principal⋅e :

G McCracken

Doctorat - McGill

Nazanin Mohammadi Sepahvand

Collaborateur·rice alumni - McGill

Shahrad Mohammadzadeh

Maîtrise recherche - McGill

Superviseur⋅e principal⋅e :

Gabriela Moisescu-Pareja

Collaborateur·rice de recherche - McGill

Co-superviseur⋅e :

Irina Rish

Padideh Nouri

Doctorat - UdeM

Co-superviseur⋅e :

Doctorat - McGill

Co-superviseur⋅e :

Stagiaire de recherche - McGill

Nate Rahn

Doctorat - McGill

Superviseur⋅e principal⋅e :

Marc Gendron-Bellemare

Manoosh Samiei

Doctorat - McGill

Co-superviseur⋅e :

Doctorat - McGill

Co-superviseur⋅e :

Doctorat - McGill

Nishanth Anand Vemgal

Doctorat - McGill

Doctorat - McGill

Co-superviseur⋅e :

Samira Ebrahimi Kahou

Stagiaire de recherche - McGill

Zihan Wang

Doctorat - McGill

Site web

Skipper : combiner l’abstraction spatiale et temporelle afin d’améliorer la généralisation

Steve Wen

Maîtrise recherche - McGill

Co-superviseur⋅e :

Gregory Dudek

Zijing Wu

Doctorat - McGill

Superviseur⋅e principal⋅e :

Doctorat - McGill

Harry Zhao

Collaborateur·rice alumni - McGill

Co-superviseur⋅e :

Billets de blogue

Generic thumbnail for Mila Blog articles.

22 février 2024

par

Mingde Harry Zhao

Safa Alver

Harm van Seijen

Romain Laroche

Doina Precup

Yoshua Bengio

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Publications

Learning-based interactive segmentation using the maximum mean cycle weight formalism

S. Nilufar

D. S. Wang

J. Girgis

C. G. Palii

D. Yang

A. Blais

M. Brand

T. J. Perkins

The maximum mean cycle weight (MMCW) segmentation framework is a graph-based alternative to approaches such as GraphCut or Markov Random Fie… (voir plus)lds. It offers time- and space-efficient computation and guaranteed optimality. However, unlike GraphCut or Markov Random Fields, MMCW does not seek to segment the entire image, but rather to find the single best object within the image, according to an objective function encoded by edge weights. Its focus on a single, best object makes MMCW attractive to interactive segmentation settings, where the user indicates which objects are to be segmented. However, a provably correct way of performing interactive segmentation using the MMCW framework has never been established. Further, the question of how to develop a good objective function based on user-provided information has never been addressed. Here, we propose a three-component objective function specifically designed for use with interactive MMCW segmentation. Two of those components, representing object boundary and object interior information, can be learned from a modest amount of user-labelled data, but in a way unique to the MMCW framework. The third component allows us to extend the MMCW framework to the situation of interactive segmentation. Specifically, we show that an appropriate weighted combination of the three components guarantees that the object produced by MMCW segmentation will enclose user-specified pixels that can be chosen interactively. The component weights can either be computed a priori based on image characteristics, or online via an adaptive reweighting scheme. We demonstrate the success of the approach on several microscope image segmentation problems.

2017-02-23

Medical Imaging (publié)

The Option-Critic Architecture

Pierre-Luc Bacon

Jean Harb

Temporal abstraction is key to scaling up learning and planning in reinforcement learning. While planning with temporally extended actions i… (voir plus)s well understood, creating such abstractions autonomously from data has remained challenging. We tackle this problem in the framework of options [Sutton, Precup & Singh, 1999; Precup, 2000]. We derive policy gradient theorems for options and propose a new option-critic architecture capable of learning both the internal policies and the termination conditions of options, in tandem with the policy over options, and without the need to provide any additional rewards or subgoals. Experimental results in both discrete and continuous environments showcase the flexibility and efficiency of the framework.

2017-02-12

Proceedings of the AAAI Conference on Artificial Intelligence (publié)

Real-Time Indoor Localization in Smart Homes Using Semi-Supervised Learning

Negar Ghourchian

Michel Allegue‐martínez

Long-term automated monitoring of residential or small in- dustrial properties is an important task within the broader scope of human activi… (voir plus)ty recognition. We present a device- free wifi-based localization system for smart indoor spaces, developed in a collaboration between McGill University and Aerˆıal Technologies. The system relies on existing wifi net- work signals and semi-supervised learning, in order to au- tomatically detect entrance into a residential unit, and track the location of a moving subject within the sensing area. The implemented real-time monitoring platform works by detect- ing changes in the characteristics of the wifi signals collected via existing off-the-shelf wifi-enabled devices in the environ- ment. This platform has been deployed in several apartments in the Montreal area, and the results obtained show the poten- tial of this technology to turn any regular home with an ex- isting wifi network into a smart home equipped with intruder alarm and room-level location detector. The machine learn- ing component has been devised so as to minimize the need for user annotation and overcome temporal instabilities in the input signals. We use a semi-supervised learning framework which works in two phases. First, we build a base learner for mapping wifi signals to different physical locations in the en- vironment from a small amount of labeled data; during its lifetime, the learner automatically re-trains when the uncer- tainty level rises significantly, without the need for further supervision. This paper describes the technical and practical issues arising in the design and implementation of such a sys- tem for real residential units, and illustrates its performance during on-going deployment.

2017-02-10

AAAI Conference on Artificial Intelligence (publié)

Independently Controllable Factors

Valentin Thomas

Jules Pondard

Emmanuel Bengio

Marc Sarfati

Philippe Beaudoin

Marie-Jean Meurs

Joelle Pineau

Yoshua Bengio

It has been postulated that a good representation is one that disentangles the underlying explanatory factors of variation. However, it rema… (voir plus)ins an open question what kind of training framework could potentially achieve that. Whereas most previous work focuses on the static setting (e.g., with images), we postulate that some of the causal factors could be discovered if the learner is allowed to interact with its environment. The agent can experiment with different actions and observe their effects. More specifically, we hypothesize that some of these factors correspond to aspects of the environment which are independently controllable, i.e., that there exists a policy and a learnable feature for each such aspect of the environment, such that this policy can yield changes in that feature with minimal changes to other features that explain the statistical variations in the observed data. We propose a specific objective function to find such factors and verify experimentally that it can indeed disentangle independently controllable aspects of the environment without any extrinsic reward signal.

2016-12-31

arXiv (prépublication)

Independently Controllable Features

Valentin Thomas

2016-12-31

(publié)

A Matrix Splitting Perspective on Planning with Options

Pierre-Luc Bacon

2016-12-02

ArXiv (prépublication)

Smart Classifier Selection for Activity Recognition on Wearable Devices

Negar Ghourchian

Activity recognition is a key component of human-machine interaction applications. Information obtained from sensors in smart wearable devic… (voir plus)es is especially valuable, because these devices have become ubiquitous, and they record large amounts of data. Machine learning algorithms can then be used to process this data. However, wearable devices impose restrictions in terms of computation and energy resources, which need to be taken into account by a learning algorithm. We propose to use a real-time learning approach, which interactively determines the most effective set of modalities (or features) for classification, given the task at hand. Our algorithm optimizes sensor selection, in order to consume less power, while still maintaining good accuracy in classifying sequences of activities. Performance on a large, noisy dataset including four different sensing modalities shows that this is a promising approach.

2016-11-22

International Conference on Pattern Recognition Applications and Methods (publié)

Verb Phrase Ellipsis Resolution Using Discriminative and Margin-Infused Algorithms

Kian Kenyon-Dean

Jackie CK Cheung

Verb Phrase Ellipsis (VPE) is an anaphoric construction in which a verb phrase has been elided. It occurs frequently in dialogue and informa… (voir plus)l conversational settings, but despite its evident impact on event coreference resolution and extraction, there has been relatively little work on computational methods for identifying and resolving VPE. Here, we present a novel approach to detecting and resolving VPE by using supervised discriminative machine learning techniques trained on features extracted from an automatically parsed, publicly available dataset. Our approach yields state-of-the-art results for VPE detection by improving F1 score by over 11%; additionally, we explore an approach to antecedent identiﬁ-cation that uses the Margin-Infused-Relaxed-Algorithm, which shows promising results.

2016-10-31

Conference on Empirical Methods in Natural Language Processing (publié)

Prediction of Cell Type Specific Transcription Factor Binding Site Occupancy

Faizy Ahsan

Mathieu Blanchette

2016-10-01

ACM International Conference on Bioinformatics, Computational Biology and Biomedicine (publié)