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检索条件"机构=Montreal Institute for Learning Algorithms"
119 条 记 录,以下是81-90 订阅
Entropic out-of-distribution detection
arXiv
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arXiv 2019年
作者: Macêdo, David Ren, Tsang Ing Zanchettin, Cleber Oliveira, Adriano L.I. Ludermir, Teresa Centro de Informática Universidade Federal de Pernambuco Recife Brazil Montreal Institute for Learning Algorithms University of Montreal QC Canada Department of Chemical and Biological Engineering Northwestern University Evanston United States
Out-of-distribution (OOD) detection approaches usually present special requirements (e.g., hyperparameter validation, collection of outlier data) and produce side effects (e.g., classification accuracy drop, slower en... 详细信息
来源: 评论
Shaping Rewards for Reinforcement learning with Imperfect Demonstrations using Generative Models
Shaping Rewards for Reinforcement Learning with Imperfect De...
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IEEE International Conference on Robotics and Automation (ICRA)
作者: Yuchen Wu Melissa Mozifian Florian Shkurti University of Toronto Robotics Institute University of Toronto Robotics Institute and Vector Institute Montreal Institute of Learning Algorithms (MILA) Mobile Robotics Lab (MRL) at the School of Computer Science McGill University Montréal Canada
The potential benefits of model-free reinforcement learning to real robotics systems are limited by its uninformed exploration that leads to slow convergence, lack of data-efficiency, and unnecessary interactions with... 详细信息
来源: 评论
Distantly-supervised neural relation extraction with side information using BERT
arXiv
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arXiv 2020年
作者: Moreira, Johny Oliveira, Chaina MacEdo, David Zanchettin, Cleber Barbosa, Luciano Centro de Inforḿatica Universidade Federal de Pernambuco Recife Brazil Montreal Institute for Learning Algorithms University of Montreal Quebec Canada Department of Chemical and Biological Engineering Northwestern University Evanston United States
Relation extraction (RE) consists in categorizing the relationship between entities in a sentence. A recent paradigm to develop relation extractors is Distant Supervision (DS), which allows the automatic creation of n... 详细信息
来源: 评论
AM-MobileNet1D: A Portable Model for Speaker Recognition
AM-MobileNet1D: A Portable Model for Speaker Recognition
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International Joint Conference on Neural Networks (IJCNN)
作者: João Antônio Chagas Nunes David Macêdo Cleber Zanchettin Centro de Informática Universidade Federal de Pernambuco Recife Brasil Montreal Institute for Learning Algorithms University of Montreal Canada Department of Chemical and Biological Engineering Northwestern University Evanston United States of America
Speaker Recognition and Speaker Identification are challenging tasks with essential applications such as automation, authentication, and security. Deep learning approaches like SincNet and AM-SincNet presented great r... 详细信息
来源: 评论
ReSeg: A Recurrent Neural Network-Based Model for Semantic Segmentation
ReSeg: A Recurrent Neural Network-Based Model for Semantic S...
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IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
作者: Francesco Visin Adriana Romero Kyunghyun Cho Matteo Matteucci Marco Ciccone Kyle Kastner Yoshua Bengio Aaron Courville Montreal Institute for Learning Algorithms (MILA) University of Montreal Montreal QC Canada Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milan Italy Courant Institute and Center for Data Science New York University New York NY United States CIFAR
We propose a structured prediction architecture, which exploits the local generic features extracted by Convolutional Neural Networks and the capacity of Recurrent Neural Networks (RNN) to retrieve distant dependencie... 详细信息
来源: 评论
learning What, Where and Which to Transfer
Learning What, Where and Which to Transfer
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International Joint Conference on Neural Networks (IJCNN)
作者: Lucas de Lima Nogueira David Macêdo Cleber Zanchettin Fernando M. P. Neto Adriano L. I. Oliveira Centro de Informática Universidade Federal de Pernambuco Recife Brasil Montreal Institute for Learning Algorithms University of Montreal Quebec Canada Department of Chemical and Biological Engineering Northwestern University Evanston United States of America
Deep learning models often require large datasets to perform well from scratch. Transfer learning methods solve this issue by using a pre-trained source network to improve a target network training. Recent approaches ...
来源: 评论
Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization
arXiv
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arXiv 2019年
作者: Sun, Fan-Yun Hoffmann, Jordan Verma, Vikas Tang, Jian National Taiwan University Mila-Quebec Institute for Learning Algorithms Canada Aalto University Finland Harvard University United States HEC Montreal Canada CIFAR AI Research Chair
This paper studies learning the representations of whole graphs in both unsupervised and semi-supervised scenarios. Graph-level representations are critical in a variety of real-world applications such as predicting t... 详细信息
来源: 评论
Distantly-Supervised Neural Relation Extraction with Side Information using BERT
Distantly-Supervised Neural Relation Extraction with Side In...
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International Joint Conference on Neural Networks (IJCNN)
作者: Johny Moreira Chaina Oliveira David Macêdo Cleber Zanchettin Luciano Barbosa Centro de Informática Universidade Federal de Pernambuco Recife Brasil Montreal Institute for Learning Algorithms University of Montreal Quebec Canada Department of Chemical and Biological Engineering Northwestern University Evanston United States of America
Relation extraction (RE) consists in categorizing the relationship between entities in a sentence. A recent paradigm to develop relation extractors is Distant Supervision (DS), which allows the automatic creation of n... 详细信息
来源: 评论
Count-ception: Counting by fully convolutional redundant counting
arXiv
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arXiv 2017年
作者: Cohen, Joseph Paul Boucher, Geneviève Glastonbury, Craig A. Lo, Henry Z. Bengio, Yoshua Montreal Institute for Learning Algorithms Université of Montréal Harvard University Herbaria Institute for Research in Immunology and Cancer Université of Montréal Big Data Institute University of Oxford Department of Computer Science University of Massachusetts Boston
Counting objects in digital images is a process that should be replaced by machines. This tedious task is time consuming and prone to errors due to fatigue of human annotators. The goal is to have a system that takes ... 详细信息
来源: 评论
Structure aware negative sampling in knowledge graphs
arXiv
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arXiv 2020年
作者: Ahrabian, Kian Feizi, Aarash Salehi, Yasmin Hamilton, William L. Bose, Avishek Joey School of Computer Science McGill University Canada Department of Electrical and Computer Engineering McGill University Canada Montreal Institute of Learning Algorithms Mila Canada Canada CIFAR AI Chair Canada
learning low-dimensional representations for entities and relations in knowledge graphs using contrastive estimation represents a scalable and effective method for inferring connectivity patterns. A crucial aspect of ... 详细信息
来源: 评论