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首页认知推理:从图表示学习和图神经网络的最新理论看AI的未来
认知推理:从图表示学习和图神经网络的最新理论看AI的未来
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更新于2023-05-21
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为了推进国内在该领域的发展,由中国中文信息学会社会媒体处理专委会和北京智源人工智能研究院联合主办的“图神经网络在线研讨会 2020”于 3 月 29 日下午召开,邀请了宋国杰、沈华伟、唐杰、石川四位国内著名学者介绍图表示学习和图神经网络的最新理论进展和应用探索。
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Artificial Intelligence
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AI
• From perceptron to cognition
Computing Perception Cognition
Storage &
Computing
Recognize
text, images,
objects,
voices
Organize and
generate
knowledge,
reasoning

5
DDPG(2015) A3C(2016)
Perceptron(1958)
Frank Rosenblatt
Cornell University
psychologist
BPNN/MLP(1986)
Hopfield Network(1982)
[recurrent & feedback]
Geoffery Hinton
University of Toronto
deep learning
Neocognitron(1980)
[convolution & pooling]
LeNet/CNN(1998)
Yann Lecun
New York University
deep learning
AlexNet(2012)
Relu, dropout & bigger
VGG(2014)
GoogLeNet(2015)
ResNet(2016)
Kaiming He
MSRA => FAIR
computer vision
DenseNet(2017)
RBM(1986/2006)
Deep Belief
Nets(2006)
stack
AutoEncoder(1989/2006)
Denosing Autoencoder(2008)
VAE(2013)
Variational
Inference
Max Welling
University of Amsterdam
statistical learning
GAN(2014)
DCGAN(2014)
WGAN(2017)
PGGAN(2017)
Ian Goodfellow
Google Brain
deep adversarial learning
RNN/LSTM(1997)
Jürgen Schmidhuber
IDSIA
Universal AI
Seq2Seq(2014)
RNN in Speech
Recognition(2013)
Yoshua Bengio
University of Montreal
Deep learning
Neural Probabilistic
Language Model(2003)
word2Vec(2013)
SeqGAN(2017)
LeakGAN(2018)
Character CNN(2015)
self-attention(2017)
Deep Q-
learning(2013)
AlphaGo(2016)
Double DQN(2015)
Dueling Net(2016)
David Silver
DeepMind
Reinforcement learning
Alpha Zero(2017)
Capsule Nets(2017)
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