咨询与建议

限定检索结果

文献类型

  • 80 篇 期刊文献
  • 36 篇 会议
  • 3 册 图书

馆藏范围

  • 119 篇 电子文献
  • 0 种 纸本馆藏

日期分布

学科分类号

  • 86 篇 工学
    • 55 篇 计算机科学与技术...
    • 52 篇 软件工程
    • 32 篇 生物工程
    • 16 篇 信息与通信工程
    • 13 篇 生物医学工程(可授...
    • 9 篇 光学工程
    • 7 篇 化学工程与技术
    • 3 篇 机械工程
    • 3 篇 电气工程
    • 3 篇 电子科学与技术(可...
    • 3 篇 控制科学与工程
    • 2 篇 材料科学与工程(可...
    • 2 篇 土木工程
    • 2 篇 环境科学与工程(可...
    • 2 篇 安全科学与工程
  • 58 篇 理学
    • 32 篇 生物学
    • 29 篇 数学
    • 13 篇 统计学(可授理学、...
    • 11 篇 物理学
    • 8 篇 化学
    • 2 篇 大气科学
    • 2 篇 地球物理学
  • 16 篇 管理学
    • 10 篇 图书情报与档案管...
    • 8 篇 管理科学与工程(可...
    • 4 篇 工商管理
  • 10 篇 法学
    • 10 篇 社会学
  • 7 篇 医学
    • 7 篇 基础医学(可授医学...
    • 7 篇 临床医学
    • 7 篇 药学(可授医学、理...
  • 1 篇 哲学
    • 1 篇 哲学
  • 1 篇 教育学
  • 1 篇 文学

主题

  • 8 篇 generative adver...
  • 8 篇 machine learning
  • 7 篇 convolution
  • 6 篇 deep learning
  • 5 篇 task analysis
  • 4 篇 deep neural netw...
  • 4 篇 computer archite...
  • 4 篇 image segmentati...
  • 4 篇 training
  • 3 篇 visualization
  • 3 篇 semantics
  • 3 篇 forecasting
  • 2 篇 knowledge based ...
  • 2 篇 reinforcement le...
  • 2 篇 magnetic resonan...
  • 2 篇 signal encoding
  • 2 篇 markov processes
  • 2 篇 graph neural net...
  • 2 篇 diagnosis
  • 2 篇 computational mo...

机构

  • 28 篇 montreal institu...
  • 9 篇 montreal institu...
  • 8 篇 montreal institu...
  • 8 篇 hec montreal
  • 7 篇 montreal institu...
  • 7 篇 montreal institu...
  • 7 篇 montreal institu...
  • 6 篇 cifar
  • 6 篇 montreal institu...
  • 5 篇 school of comput...
  • 5 篇 department of ch...
  • 5 篇 centro de inform...
  • 5 篇 department of ch...
  • 4 篇 centro de inform...
  • 4 篇 montreal institu...
  • 3 篇 harvard universi...
  • 3 篇 facebook ai rese...
  • 3 篇 microsoft resear...
  • 3 篇 department of co...
  • 3 篇 mila-quebec inst...

作者

  • 28 篇 bengio yoshua
  • 7 篇 pineau joelle
  • 7 篇 zanchettin clebe...
  • 7 篇 tang jian
  • 6 篇 macêdo david
  • 6 篇 david macêdo
  • 6 篇 cleber zanchetti...
  • 6 篇 cohen joseph pau...
  • 5 篇 zhang saizheng
  • 4 篇 pal chris
  • 4 篇 subramanian sand...
  • 4 篇 sankar chinnadhu...
  • 4 篇 luck margaux
  • 4 篇 sordoni alessand...
  • 4 篇 romero adriana
  • 4 篇 yoshua bengio
  • 4 篇 serban iulian v.
  • 3 篇 gulcehre caglar
  • 3 篇 ke nan rosemary
  • 3 篇 adriano l. i. ol...

语言

  • 119 篇 英文
检索条件"机构=Montreal Institute for Learning Algorithms"
119 条 记 录,以下是41-50 订阅
Multi-Region bilinear convolutional neural networks for person re-identification
Multi-Region bilinear convolutional neural networks for pers...
收藏 引用
IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS)
作者: Evgeniya Ustinova Yaroslav Ganin Victor Lempitsky Skolkovo Institute of Science and Technology Moscow Skolkovo Institute of Science and Technology Moscow Montreal Institute for Learning Algorithms Montreal Quebec
In this work we propose a new architecture for person re-identification. As the task of re-identification is inherently associated with embedding learning and non-rigid appearance description, our architecture is base... 详细信息
来源: 评论
A deep reinforcement learning chatbot
arXiv
收藏 引用
arXiv 2017年
作者: Serban, Iulian V. Sankar, Chinnadhurai Germain, Mathieu Zhang, Saizheng Lin, Zhouhan Subramanian, Sandeep Kim, Taesup Pieper, Michael Chandar, Sarath Ke, Nan Rosemary Rajeshwar, Sai de Brebisson, Alexandre Sotelo, Jose M.R. Suhubdy, Dendi Michalski, Vincent Nguyen, Alexandre Pineau, Joelle Bengio, Yoshua Montreal Institute for Learning Algorithms MontrealQC Canada School of Computer Science McGill University CIFAR
We present MILABOT: a deep reinforcement learning chatbot developed by the montreal institute for learning algorithms (MILA) for the Amazon Alexa Prize competition. MILABOT is capable of conversing with humans on popu... 详细信息
来源: 评论
Synthesizing programs for images using reinforced adversarial learning
arXiv
收藏 引用
arXiv 2018年
作者: Ganin, Yaroslav Kulkarni, Tejas Babuschkin, Igor Ali Eslami, S.M. Vinyals, Oriol Montreal Institute for Learning Algorithms Montréal Canada DeepMind London United Kingdom
Advances in deep generative networks have led to impressive results in recent years. Nevertheless, such models can often waste their capacity on the minutiae of datasets, presumably due to weak inductive biases in the... 详细信息
来源: 评论
Phishing URL Detection with Oversampling based on Text Generative Adversarial Networks
Phishing URL Detection with Oversampling based on Text Gener...
收藏 引用
2018 IEEE International Conference on Big Data, Big Data 2018
作者: Anand, Ankesh Gorde, Kshitij Antony Moniz, Joel Ruben Park, Noseong Chakraborty, Tanmoy Chu, Bei-Tseng Montreal Institute for Learning Algorithms Montreal Canada University of North Carolina CharlotteNC United States Carnegie Mellon University PittsburghPA United States George Mason University FairfaxVA United States IIIT-Delhi New Delhi India
The problem of imbalanced classes arises frequently in binary classification tasks. If one class outnumbers another, trained classifiers become heavily biased towards the majority class. For phishing URL detection, it... 详细信息
来源: 评论
VFunc: A deep generative model for functions
arXiv
收藏 引用
arXiv 2018年
作者: Bachman, Philip Islam, Riashat Sordoni, Alessandro Ahmed, Zafarali Microsoft Research School of Computer Science McGill University Montreal Institute of Learning Algorithms
We introduce a deep generative model for functions. Our model provides a joint distribution p(f, z) over functions f and latent variables z which lets us efficiently sample from the marginal p(f) and maximize a variat... 详细信息
来源: 评论
On the impressive performance of randomly weighted encoders in summarization tasks
arXiv
收藏 引用
arXiv 2020年
作者: Pilault, Jonathan Park, Jaehong Pal, Christopher Element AI Montreal Institute for Learning Algorithms Ecole Polytechnique de Montreal Canada CIFAR AI Chair
In this work, we investigate the performance of untrained randomly initialized encoders in a general class of sequence to sequence models and compare their performance with that of fully-trained encoders on the task o... 详细信息
来源: 评论
INFOGRAPH: UNSUPERVISED AND SEMI-SUPERVISED GRAPH-LEVEL REPRESENTATION learning VIA MUTUAL INFORMATION MAXIMIZATION
INFOGRAPH: UNSUPERVISED AND SEMI-SUPERVISED GRAPH-LEVEL REPR...
收藏 引用
8th International Conference on learning Representations, ICLR 2020
作者: Sun, Fan-Yun Hoffmann, Jordan Verma, Vikas Tang, Jian National Taiwan University Taiwan Mila-Quebec Institute for Learning Algorithms Canada Aalto University Finland Harvard University United States HEC Montreal Canada CIFAR AI Research
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
The Bottleneck Simulator: A model-based deep reinforcement learning approach
arXiv
收藏 引用
arXiv 2018年
作者: Serban, Iulian Vlad Sankar, Chinnadhurai Pieper, Michael Pineau, Joelle Bengio, Yoshua Montreal Institute for Learning Algorithms Montreal Canada School of Computer Science McGill University Montreal and Facebook Artificial Intelligence Research
Deep reinforcement learning has recently shown many impressive successes. However, one major obstacle towards applying such methods to realworld problems is their lack of data-efficiency. To this end, we propose the B... 详细信息
来源: 评论
Tell, Draw, and Repeat: Generating and Modifying Images Based on Continual Linguistic Instruction
arXiv
收藏 引用
arXiv 2018年
作者: El-Nouby, Alaaeldin Sharma, Shikhar Schulz, Hannes Hjelm, Devon Asri, Layla El Kahou, Samira Ebrahimi Bengio, Yoshua Taylor, Graham W. University of Guelph Microsoft Research Montreal Institute for Learning Algorithms Vector Institute for Artificial Intelligence University of Montreal Canadian Institute for Advanced Research
Conditional text-to-image generation is an active area of research, with many possible applications. Existing research has primarily focused on generating a single image from available conditioning information in one ... 详细信息
来源: 评论
Deep neural network or dermatologist?
arXiv
收藏 引用
arXiv 2019年
作者: Young, Kyle Booth, Gareth Simpson, Becks Dutton, Reuben Shrapnel, Sally School of Mathematics and Physics University of Queensland Brisbane Australia Montreal Institute for Learning Algorithms Canada
Deep learning techniques have proven high accuracy for identifying melanoma in digitised dermoscopic images. A strength is that these methods are not constrained by features that are pre-defined by human semantics. A ... 详细信息
来源: 评论