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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
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for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
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twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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learning (RL). We discuss six core elements, six important mechanisms, and
twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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learning (RL). We discuss six core elements, six important mechanisms, and
twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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learning (RL). We discuss six core elements, six important mechanisms, and
twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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learning (RL). We discuss six core elements, six important mechanisms, and
twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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learning (RL). We discuss six core elements, six important mechanisms, and
twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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learning (RL). We discuss six core elements, six important mechanisms, and
twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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learning (RL). We discuss six core elements, six important mechanisms, and
twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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learning (RL). We discuss six core elements, six important mechanisms, and
twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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learning (RL). We discuss six core elements, six important mechanisms, and
twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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learning (RL). We discuss six core elements, six important mechanisms, and
twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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learning (RL). We discuss six core elements, six important mechanisms, and
twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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learning (RL). We discuss six core elements, six important mechanisms, and
twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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learning (RL). We discuss six core elements, six important mechanisms, and
twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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learning (RL). We discuss six core elements, six important mechanisms, and
twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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learning (RL). We discuss six core elements, six important mechanisms, and
twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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learning (RL). We discuss six core elements, six important mechanisms, and
twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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learning (RL). We discuss six core elements, six important mechanisms, and
twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
1701.07274 | 279 | 70
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twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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learning (RL). We discuss six core elements, six important mechanisms, and
twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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learning (RL). We discuss six core elements, six important mechanisms, and
twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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learning (RL). We discuss six core elements, six important mechanisms, and
twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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learning (RL). We discuss six core elements, six important mechanisms, and
twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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learning (RL). We discuss six core elements, six important mechanisms, and
twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
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twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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learning (RL). We discuss six core elements, six important mechanisms, and
twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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learning (RL). We discuss six core elements, six important mechanisms, and
twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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learning (RL). We discuss six core elements, six important mechanisms, and
twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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learning (RL). We discuss six core elements, six important mechanisms, and
twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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learning (RL). We discuss six core elements, six important mechanisms, and
twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
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learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
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learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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learning (RL). We discuss six core elements, six important mechanisms, and
twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
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learning (RL). We discuss six core elements, six important mechanisms, and
twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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learning (RL). We discuss six core elements, six important mechanisms, and
twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
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discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
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Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
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model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
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Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
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model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
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Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
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learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
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robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
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Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
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model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
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model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
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Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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for RL, including attention and memory, unsupervised learning, transfer
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discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
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yet, and list a collection of RL resources. After presenting a brief summary,
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Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
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model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
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Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
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learning and reinforcement learning. Next we discuss core RL elements,
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model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
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learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
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learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
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we close with discussions.
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Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
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translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
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Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
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model, planning, and exploration. After that, we discuss important mechanisms
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discuss various applications of RL, including games, in particular, AlphaGo,
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business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
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Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
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including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
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twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update. | http://arxiv.org/pdf/1701.07274 | Yuxi Li | cs.LG | Please see Deep Reinforcement Learning, arXiv:1810.06339, for a
significant update | null | cs.LG | 20170125 | 20181126 | [] |
1701.06538 | 0 | 7 1 0 2
n a J 3 2 ] G L . s c [
1 v 8 3 5 6 0 . 1 0 7 1 : v i X r a
Under review as a conference paper at ICLR 2017
# OUTRAGEOUSLY LARGE NEURAL NETWORKS: THE SPARSELY-GATED MIXTURE-OF-EXPERTS LAYER
Noam Shazeer1, Azalia Mirhoseiniââ 1, Krzysztof Maziarzâ2, Andy Davis1, Quoc Le1, Geoffrey Hinton1 and Jeff Dean1
1Google Brain, {noam,azalia,andydavis,qvl,geoffhinton,jeff}@google.com 2Jagiellonian University, Cracow, [email protected]
# ABSTRACT | 1701.06538#0 | Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer | The capacity of a neural network to absorb information is limited by its
number of parameters. Conditional computation, where parts of the network are
active on a per-example basis, has been proposed in theory as a way of
dramatically increasing model capacity without a proportional increase in
computation. In practice, however, there are significant algorithmic and
performance challenges. In this work, we address these challenges and finally
realize the promise of conditional computation, achieving greater than 1000x
improvements in model capacity with only minor losses in computational
efficiency on modern GPU clusters. We introduce a Sparsely-Gated
Mixture-of-Experts layer (MoE), consisting of up to thousands of feed-forward
sub-networks. A trainable gating network determines a sparse combination of
these experts to use for each example. We apply the MoE to the tasks of
language modeling and machine translation, where model capacity is critical for
absorbing the vast quantities of knowledge available in the training corpora.
We present model architectures in which a MoE with up to 137 billion parameters
is applied convolutionally between stacked LSTM layers. On large language
modeling and machine translation benchmarks, these models achieve significantly
better results than state-of-the-art at lower computational cost. | http://arxiv.org/pdf/1701.06538 | Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, Jeff Dean | cs.LG, cs.CL, cs.NE, stat.ML | null | null | cs.LG | 20170123 | 20170123 | [
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"id": "1609.08144"
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"id": "1511.06297"
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"id": "1512.02595"
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] |
1701.06538 | 1 | # ABSTRACT
The capacity of a neural network to absorb information is limited by its number of parameters. Conditional computation, where parts of the network are active on a per-example basis, has been proposed in theory as a way of dramatically increas- ing model capacity without a proportional increase in computation. In practice, however, there are signiï¬cant algorithmic and performance challenges. In this work, we address these challenges and ï¬nally realize the promise of conditional computation, achieving greater than 1000x improvements in model capacity with only minor losses in computational efï¬ciency on modern GPU clusters. We in- troduce a Sparsely-Gated Mixture-of-Experts layer (MoE), consisting of up to thousands of feed-forward sub-networks. A trainable gating network determines a sparse combination of these experts to use for each example. We apply the MoE to the tasks of language modeling and machine translation, where model capacity is critical for absorbing the vast quantities of knowledge available in the training corpora. We present model architectures in which a MoE with up to 137 billion parameters is applied convolutionally between stacked LSTM layers. On large language modeling and machine translation benchmarks, these models achieve signiï¬cantly better results than state-of-the-art at lower computational cost.
1
# INTRODUCTION AND RELATED WORK | 1701.06538#1 | Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer | The capacity of a neural network to absorb information is limited by its
number of parameters. Conditional computation, where parts of the network are
active on a per-example basis, has been proposed in theory as a way of
dramatically increasing model capacity without a proportional increase in
computation. In practice, however, there are significant algorithmic and
performance challenges. In this work, we address these challenges and finally
realize the promise of conditional computation, achieving greater than 1000x
improvements in model capacity with only minor losses in computational
efficiency on modern GPU clusters. We introduce a Sparsely-Gated
Mixture-of-Experts layer (MoE), consisting of up to thousands of feed-forward
sub-networks. A trainable gating network determines a sparse combination of
these experts to use for each example. We apply the MoE to the tasks of
language modeling and machine translation, where model capacity is critical for
absorbing the vast quantities of knowledge available in the training corpora.
We present model architectures in which a MoE with up to 137 billion parameters
is applied convolutionally between stacked LSTM layers. On large language
modeling and machine translation benchmarks, these models achieve significantly
better results than state-of-the-art at lower computational cost. | http://arxiv.org/pdf/1701.06538 | Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, Jeff Dean | cs.LG, cs.CL, cs.NE, stat.ML | null | null | cs.LG | 20170123 | 20170123 | [
{
"id": "1502.03167"
},
{
"id": "1606.04199"
},
{
"id": "1602.02410"
},
{
"id": "1609.08144"
},
{
"id": "1511.06297"
},
{
"id": "1512.02595"
}
] |
1701.06538 | 2 | 1
# INTRODUCTION AND RELATED WORK
1.1 CONDITIONAL COMPUTATION
Exploiting scale in both training data and model size has been central to the success of deep learn- ing. When datasets are sufï¬ciently large, increasing the capacity (number of parameters) of neural networks can give much better prediction accuracy. This has been shown in domains such as text (Sutskever et al., 2014; Bahdanau et al., 2014; Jozefowicz et al., 2016; Wu et al., 2016), images (Krizhevsky et al., 2012; Le et al., 2012), and audio (Hinton et al., 2012; Amodei et al., 2015). For typical deep learning models, where the entire model is activated for every example, this leads to a roughly quadratic blow-up in training costs, as both the model size and the number of training examples increase. Unfortunately, the advances in computing power and distributed computation fall short of meeting such demand. | 1701.06538#2 | Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer | The capacity of a neural network to absorb information is limited by its
number of parameters. Conditional computation, where parts of the network are
active on a per-example basis, has been proposed in theory as a way of
dramatically increasing model capacity without a proportional increase in
computation. In practice, however, there are significant algorithmic and
performance challenges. In this work, we address these challenges and finally
realize the promise of conditional computation, achieving greater than 1000x
improvements in model capacity with only minor losses in computational
efficiency on modern GPU clusters. We introduce a Sparsely-Gated
Mixture-of-Experts layer (MoE), consisting of up to thousands of feed-forward
sub-networks. A trainable gating network determines a sparse combination of
these experts to use for each example. We apply the MoE to the tasks of
language modeling and machine translation, where model capacity is critical for
absorbing the vast quantities of knowledge available in the training corpora.
We present model architectures in which a MoE with up to 137 billion parameters
is applied convolutionally between stacked LSTM layers. On large language
modeling and machine translation benchmarks, these models achieve significantly
better results than state-of-the-art at lower computational cost. | http://arxiv.org/pdf/1701.06538 | Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, Jeff Dean | cs.LG, cs.CL, cs.NE, stat.ML | null | null | cs.LG | 20170123 | 20170123 | [
{
"id": "1502.03167"
},
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"id": "1606.04199"
},
{
"id": "1602.02410"
},
{
"id": "1609.08144"
},
{
"id": "1511.06297"
},
{
"id": "1512.02595"
}
] |
1701.06538 | 3 | Various forms of conditional computation have been proposed as a way to increase model capacity without a proportional increase in computational costs (Davis & Arel, 2013; Bengio et al., 2013; Eigen et al., 2013; Ludovic Denoyer, 2014; Cho & Bengio, 2014; Bengio et al., 2015; Almahairi et al., 2015). In these schemes, large parts of a network are active or inactive on a per-example basis. The gating decisions may be binary or sparse and continuous, stochastic or deterministic. Various forms of reinforcement learning and back-propagation are proposed for trarining the gating decisions.
âEqually major contributors â Work done as a member of the Google Brain Residency program (g.co/brainresidency)
1
# Under review as a conference paper at ICLR 2017
MoE layer Ge, MoE layer
Figure 1: A Mixture of Experts (MoE) layer embedded within a recurrent language model. In this case, the sparse gating function selects two experts to perform computations. Their outputs are modulated by the outputs of the gating network.
While these ideas are promising in theory, no work to date has yet demonstrated massive improve- ments in model capacity, training time, or model quality. We blame this on a combination of the following challenges: | 1701.06538#3 | Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer | The capacity of a neural network to absorb information is limited by its
number of parameters. Conditional computation, where parts of the network are
active on a per-example basis, has been proposed in theory as a way of
dramatically increasing model capacity without a proportional increase in
computation. In practice, however, there are significant algorithmic and
performance challenges. In this work, we address these challenges and finally
realize the promise of conditional computation, achieving greater than 1000x
improvements in model capacity with only minor losses in computational
efficiency on modern GPU clusters. We introduce a Sparsely-Gated
Mixture-of-Experts layer (MoE), consisting of up to thousands of feed-forward
sub-networks. A trainable gating network determines a sparse combination of
these experts to use for each example. We apply the MoE to the tasks of
language modeling and machine translation, where model capacity is critical for
absorbing the vast quantities of knowledge available in the training corpora.
We present model architectures in which a MoE with up to 137 billion parameters
is applied convolutionally between stacked LSTM layers. On large language
modeling and machine translation benchmarks, these models achieve significantly
better results than state-of-the-art at lower computational cost. | http://arxiv.org/pdf/1701.06538 | Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, Jeff Dean | cs.LG, cs.CL, cs.NE, stat.ML | null | null | cs.LG | 20170123 | 20170123 | [
{
"id": "1502.03167"
},
{
"id": "1606.04199"
},
{
"id": "1602.02410"
},
{
"id": "1609.08144"
},
{
"id": "1511.06297"
},
{
"id": "1512.02595"
}
] |
1701.06538 | 4 | ⢠Modern computing devices, especially GPUs, are much faster at arithmetic than at branch- ing. Most of the works above recognize this and propose turning on/off large chunks of the network with each gating decision.
⢠Large batch sizes are critical for performance, as they amortize the costs of parameter trans- fers and updates. Conditional computation reduces the batch sizes for the conditionally active chunks of the network.
⢠Network bandwidth can be a bottleneck. A cluster of GPUs may have computational power thousands of times greater than the aggregate inter-device network bandwidth. To be com- putationally efï¬cient, the relative computational versus network demands of an algorithm must exceed this ratio. Embedding layers, which can be seen as a form of conditional com- putation, are handicapped by this very problem. Since the embeddings generally need to be sent across the network, the number of (example, parameter) interactions is limited by network bandwidth instead of computational capacity.
⢠Depending on the scheme, loss terms may be necessary to achieve the desired level of sparsity per-chunk and/or per example. Bengio et al. (2015) use three such terms. These issues can affect both model quality and load-balancing. | 1701.06538#4 | Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer | The capacity of a neural network to absorb information is limited by its
number of parameters. Conditional computation, where parts of the network are
active on a per-example basis, has been proposed in theory as a way of
dramatically increasing model capacity without a proportional increase in
computation. In practice, however, there are significant algorithmic and
performance challenges. In this work, we address these challenges and finally
realize the promise of conditional computation, achieving greater than 1000x
improvements in model capacity with only minor losses in computational
efficiency on modern GPU clusters. We introduce a Sparsely-Gated
Mixture-of-Experts layer (MoE), consisting of up to thousands of feed-forward
sub-networks. A trainable gating network determines a sparse combination of
these experts to use for each example. We apply the MoE to the tasks of
language modeling and machine translation, where model capacity is critical for
absorbing the vast quantities of knowledge available in the training corpora.
We present model architectures in which a MoE with up to 137 billion parameters
is applied convolutionally between stacked LSTM layers. On large language
modeling and machine translation benchmarks, these models achieve significantly
better results than state-of-the-art at lower computational cost. | http://arxiv.org/pdf/1701.06538 | Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, Jeff Dean | cs.LG, cs.CL, cs.NE, stat.ML | null | null | cs.LG | 20170123 | 20170123 | [
{
"id": "1502.03167"
},
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"id": "1606.04199"
},
{
"id": "1602.02410"
},
{
"id": "1609.08144"
},
{
"id": "1511.06297"
},
{
"id": "1512.02595"
}
] |
1701.06538 | 5 | ⢠Model capacity is most critical for very large data sets. The existing literature on condi- tional computation deals with relatively small image recognition data sets consisting of up to 600,000 images. It is hard to imagine that the labels of these images provide a sufï¬cient signal to adequately train a model with millions, let alone billions of parameters.
In this work, we for the ï¬rst time address all of the above challenges and ï¬nally realize the promise of conditional computation. We obtain greater than 1000x improvements in model capacity with only minor losses in computational efï¬ciency and signiï¬cantly advance the state-of-the-art results on public language modeling and translation data sets.
1.2 OUR APPROACH: THE SPARSELY-GATED MIXTURE-OF-EXPERTS LAYER
Our approach to conditional computation is to introduce a new type of general purpose neural net- work component: a Sparsely-Gated Mixture-of-Experts Layer (MoE). The MoE consists of a num- ber of experts, each a simple feed-forward neural network, and a trainable gating network which selects a sparse combination of the experts to process each input (see Figure 1). All parts of the network are trained jointly by back-propagation.
2
# Under review as a conference paper at ICLR 2017 | 1701.06538#5 | Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer | The capacity of a neural network to absorb information is limited by its
number of parameters. Conditional computation, where parts of the network are
active on a per-example basis, has been proposed in theory as a way of
dramatically increasing model capacity without a proportional increase in
computation. In practice, however, there are significant algorithmic and
performance challenges. In this work, we address these challenges and finally
realize the promise of conditional computation, achieving greater than 1000x
improvements in model capacity with only minor losses in computational
efficiency on modern GPU clusters. We introduce a Sparsely-Gated
Mixture-of-Experts layer (MoE), consisting of up to thousands of feed-forward
sub-networks. A trainable gating network determines a sparse combination of
these experts to use for each example. We apply the MoE to the tasks of
language modeling and machine translation, where model capacity is critical for
absorbing the vast quantities of knowledge available in the training corpora.
We present model architectures in which a MoE with up to 137 billion parameters
is applied convolutionally between stacked LSTM layers. On large language
modeling and machine translation benchmarks, these models achieve significantly
better results than state-of-the-art at lower computational cost. | http://arxiv.org/pdf/1701.06538 | Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, Jeff Dean | cs.LG, cs.CL, cs.NE, stat.ML | null | null | cs.LG | 20170123 | 20170123 | [
{
"id": "1502.03167"
},
{
"id": "1606.04199"
},
{
"id": "1602.02410"
},
{
"id": "1609.08144"
},
{
"id": "1511.06297"
},
{
"id": "1512.02595"
}
] |
1701.06538 | 6 | 2
# Under review as a conference paper at ICLR 2017
While the introduced technique is generic, in this paper we focus on language modeling and machine translation tasks, which are known to beneï¬t from very large models. In particular, we apply a MoE convolutionally between stacked LSTM layers (Hochreiter & Schmidhuber, 1997), as in Figure 1. The MoE is called once for each position in the text, selecting a potentially different combination of experts at each position. The different experts tend to become highly specialized based on syntax and semantics (see Appendix E Table 9). On both language modeling and machine translation benchmarks, we improve on best published results at a fraction of the computational cost.
1.3 RELATED WORK ON MIXTURES OF EXPERTS | 1701.06538#6 | Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer | The capacity of a neural network to absorb information is limited by its
number of parameters. Conditional computation, where parts of the network are
active on a per-example basis, has been proposed in theory as a way of
dramatically increasing model capacity without a proportional increase in
computation. In practice, however, there are significant algorithmic and
performance challenges. In this work, we address these challenges and finally
realize the promise of conditional computation, achieving greater than 1000x
improvements in model capacity with only minor losses in computational
efficiency on modern GPU clusters. We introduce a Sparsely-Gated
Mixture-of-Experts layer (MoE), consisting of up to thousands of feed-forward
sub-networks. A trainable gating network determines a sparse combination of
these experts to use for each example. We apply the MoE to the tasks of
language modeling and machine translation, where model capacity is critical for
absorbing the vast quantities of knowledge available in the training corpora.
We present model architectures in which a MoE with up to 137 billion parameters
is applied convolutionally between stacked LSTM layers. On large language
modeling and machine translation benchmarks, these models achieve significantly
better results than state-of-the-art at lower computational cost. | http://arxiv.org/pdf/1701.06538 | Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, Jeff Dean | cs.LG, cs.CL, cs.NE, stat.ML | null | null | cs.LG | 20170123 | 20170123 | [
{
"id": "1502.03167"
},
{
"id": "1606.04199"
},
{
"id": "1602.02410"
},
{
"id": "1609.08144"
},
{
"id": "1511.06297"
},
{
"id": "1512.02595"
}
] |
1701.06538 | 7 | 1.3 RELATED WORK ON MIXTURES OF EXPERTS
Since its introduction more than two decades ago (Jacobs et al., 1991; Jordan & Jacobs, 1994), the mixture-of-experts approach has been the subject of much research. Different types of expert architectures hae been proposed such as SVMs (Collobert et al., 2002), Gaussian Processes (Tresp, 2001; Theis & Bethge, 2015; Deisenroth & Ng, 2015), Dirichlet Processes (Shahbaba & Neal, 2009), and deep networks. Other work has focused on different expert conï¬gurations such as a hierarchical structure (Yao et al., 2009), inï¬nite numbers of experts (Rasmussen & Ghahramani, 2002), and adding experts sequentially (Aljundi et al., 2016). Garmash & Monz (2016) suggest an ensemble model in the format of mixture of experts for machine translation. The gating network is trained on a pre-trained ensemble NMT model. | 1701.06538#7 | Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer | The capacity of a neural network to absorb information is limited by its
number of parameters. Conditional computation, where parts of the network are
active on a per-example basis, has been proposed in theory as a way of
dramatically increasing model capacity without a proportional increase in
computation. In practice, however, there are significant algorithmic and
performance challenges. In this work, we address these challenges and finally
realize the promise of conditional computation, achieving greater than 1000x
improvements in model capacity with only minor losses in computational
efficiency on modern GPU clusters. We introduce a Sparsely-Gated
Mixture-of-Experts layer (MoE), consisting of up to thousands of feed-forward
sub-networks. A trainable gating network determines a sparse combination of
these experts to use for each example. We apply the MoE to the tasks of
language modeling and machine translation, where model capacity is critical for
absorbing the vast quantities of knowledge available in the training corpora.
We present model architectures in which a MoE with up to 137 billion parameters
is applied convolutionally between stacked LSTM layers. On large language
modeling and machine translation benchmarks, these models achieve significantly
better results than state-of-the-art at lower computational cost. | http://arxiv.org/pdf/1701.06538 | Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, Jeff Dean | cs.LG, cs.CL, cs.NE, stat.ML | null | null | cs.LG | 20170123 | 20170123 | [
{
"id": "1502.03167"
},
{
"id": "1606.04199"
},
{
"id": "1602.02410"
},
{
"id": "1609.08144"
},
{
"id": "1511.06297"
},
{
"id": "1512.02595"
}
] |
1701.06538 | 8 | The works above concern top-level mixtures of experts. The mixture of experts is the whole model. Eigen et al. (2013) introduce the idea of using multiple MoEs with their own gating networks as parts of a deep model. It is intuitive that the latter approach is more powerful, since complex prob- lems may contain many sub-problems each requiring different experts. They also allude in their conclusion to the potential to introduce sparsity, turning MoEs into a vehicle for computational computation.
Our work builds on this use of MoEs as a general purpose neural network component. While Eigen et al. (2013) uses two stacked MoEs allowing for two sets of gating decisions, our convolutional application of the MoE allows for different gating decisions at each position in the text. We also realize sparse gating and demonstrate its use as a practical way to massively increase model capacity.
# 2 THE STRUCTURE OF THE MIXTURE-OF-EXPERTS LAYER | 1701.06538#8 | Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer | The capacity of a neural network to absorb information is limited by its
number of parameters. Conditional computation, where parts of the network are
active on a per-example basis, has been proposed in theory as a way of
dramatically increasing model capacity without a proportional increase in
computation. In practice, however, there are significant algorithmic and
performance challenges. In this work, we address these challenges and finally
realize the promise of conditional computation, achieving greater than 1000x
improvements in model capacity with only minor losses in computational
efficiency on modern GPU clusters. We introduce a Sparsely-Gated
Mixture-of-Experts layer (MoE), consisting of up to thousands of feed-forward
sub-networks. A trainable gating network determines a sparse combination of
these experts to use for each example. We apply the MoE to the tasks of
language modeling and machine translation, where model capacity is critical for
absorbing the vast quantities of knowledge available in the training corpora.
We present model architectures in which a MoE with up to 137 billion parameters
is applied convolutionally between stacked LSTM layers. On large language
modeling and machine translation benchmarks, these models achieve significantly
better results than state-of-the-art at lower computational cost. | http://arxiv.org/pdf/1701.06538 | Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, Jeff Dean | cs.LG, cs.CL, cs.NE, stat.ML | null | null | cs.LG | 20170123 | 20170123 | [
{
"id": "1502.03167"
},
{
"id": "1606.04199"
},
{
"id": "1602.02410"
},
{
"id": "1609.08144"
},
{
"id": "1511.06297"
},
{
"id": "1512.02595"
}
] |
1701.06538 | 9 | # 2 THE STRUCTURE OF THE MIXTURE-OF-EXPERTS LAYER
The Mixture-of-Experts (MoE) layer consists of a set of n âexpert networks" E1, · · · , En, and a âgating network" G whose output is a sparse n-dimensional vector. Figure 1 shows an overview of the MoE module. The experts are themselves neural networks, each with their own parameters. Although in principle we only require that the experts accept the same sized inputs and produce the same-sized outputs, in our initial investigations in this paper, we restrict ourselves to the case where the models are feed-forward networks with identical architectures, but with separate parameters.
Let us denote by G(x) and Ei(x) the output of the gating network and the output of the i-th expert network for a given input x. The output y of the MoE module can be written as follows:
y= Ga) Bi(2) (1)
i=1 | 1701.06538#9 | Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer | The capacity of a neural network to absorb information is limited by its
number of parameters. Conditional computation, where parts of the network are
active on a per-example basis, has been proposed in theory as a way of
dramatically increasing model capacity without a proportional increase in
computation. In practice, however, there are significant algorithmic and
performance challenges. In this work, we address these challenges and finally
realize the promise of conditional computation, achieving greater than 1000x
improvements in model capacity with only minor losses in computational
efficiency on modern GPU clusters. We introduce a Sparsely-Gated
Mixture-of-Experts layer (MoE), consisting of up to thousands of feed-forward
sub-networks. A trainable gating network determines a sparse combination of
these experts to use for each example. We apply the MoE to the tasks of
language modeling and machine translation, where model capacity is critical for
absorbing the vast quantities of knowledge available in the training corpora.
We present model architectures in which a MoE with up to 137 billion parameters
is applied convolutionally between stacked LSTM layers. On large language
modeling and machine translation benchmarks, these models achieve significantly
better results than state-of-the-art at lower computational cost. | http://arxiv.org/pdf/1701.06538 | Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, Jeff Dean | cs.LG, cs.CL, cs.NE, stat.ML | null | null | cs.LG | 20170123 | 20170123 | [
{
"id": "1502.03167"
},
{
"id": "1606.04199"
},
{
"id": "1602.02410"
},
{
"id": "1609.08144"
},
{
"id": "1511.06297"
},
{
"id": "1512.02595"
}
] |
1701.06538 | 10 | y= Ga) Bi(2) (1)
i=1
We save computation based on the sparsity of the output of G(x). Wherever G(x)i = 0, we need not compute Ei(x). In our experiments, we have up to thousands of experts, but only need to evaluate a handful of them for every example. If the number of experts is very large, we can reduce the branching factor by using a two-level hierarchical MoE. In a hierarchical MoE, a primary gating network chooses a sparse weighted combination of âexperts", each of which is itself a secondary mixture-of-experts with its own gating network. In the following we focus on ordinary MoEs. We provide more details on hierarchical MoEs in Appendix B.
Our implementation is related to other models of conditional computation. A MoE whose experts are simple weight matrices is similar to the parameterized weight matrix proposed in (Cho & Bengio, 2014). A MoE whose experts have one hidden layer is similar to the block-wise dropout described in (Bengio et al., 2015), where the dropped-out layer is sandwiched between fully-activated layers.
3
# Under review as a conference paper at ICLR 2017
2.1 GATING NETWORK | 1701.06538#10 | Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer | The capacity of a neural network to absorb information is limited by its
number of parameters. Conditional computation, where parts of the network are
active on a per-example basis, has been proposed in theory as a way of
dramatically increasing model capacity without a proportional increase in
computation. In practice, however, there are significant algorithmic and
performance challenges. In this work, we address these challenges and finally
realize the promise of conditional computation, achieving greater than 1000x
improvements in model capacity with only minor losses in computational
efficiency on modern GPU clusters. We introduce a Sparsely-Gated
Mixture-of-Experts layer (MoE), consisting of up to thousands of feed-forward
sub-networks. A trainable gating network determines a sparse combination of
these experts to use for each example. We apply the MoE to the tasks of
language modeling and machine translation, where model capacity is critical for
absorbing the vast quantities of knowledge available in the training corpora.
We present model architectures in which a MoE with up to 137 billion parameters
is applied convolutionally between stacked LSTM layers. On large language
modeling and machine translation benchmarks, these models achieve significantly
better results than state-of-the-art at lower computational cost. | http://arxiv.org/pdf/1701.06538 | Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, Jeff Dean | cs.LG, cs.CL, cs.NE, stat.ML | null | null | cs.LG | 20170123 | 20170123 | [
{
"id": "1502.03167"
},
{
"id": "1606.04199"
},
{
"id": "1602.02410"
},
{
"id": "1609.08144"
},
{
"id": "1511.06297"
},
{
"id": "1512.02595"
}
] |
1701.06538 | 11 | 3
# Under review as a conference paper at ICLR 2017
2.1 GATING NETWORK
Softmax Gating: A simple choice of non-sparse gating function (Jordan & Jacobs, 1994) is to multiply the input by a trainable weight matrix Wg and then apply the Sof tmax function.
GÏ(x) = Sof tmax(x · Wg) (2)
Noisy Top-K Gating: We add two components to the Softmax gating network: sparsity and noise. Before taking the softmax function, we add tunable Gaussian noise, then keep only the top k values, setting the rest to ââ (which causes the corresponding gate values to equal 0). The sparsity serves to save computation, as described above. While this form of sparsity creates some theoretically scary discontinuities in the output of gating function, we have not yet observed this to be a problem in practice. The noise term helps with load balancing, as will be discussed in Appendix A. The amount of noise per component is controlled by a second trainable weight matrix Wnoise.
G(x) = Sof tmax(KeepT opK(H(x), k)) (3)
H(x)i = (x · Wg)i + StandardN ormal() · Sof tplus((x · Wnoise)i) (4)
# Ui | 1701.06538#11 | Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer | The capacity of a neural network to absorb information is limited by its
number of parameters. Conditional computation, where parts of the network are
active on a per-example basis, has been proposed in theory as a way of
dramatically increasing model capacity without a proportional increase in
computation. In practice, however, there are significant algorithmic and
performance challenges. In this work, we address these challenges and finally
realize the promise of conditional computation, achieving greater than 1000x
improvements in model capacity with only minor losses in computational
efficiency on modern GPU clusters. We introduce a Sparsely-Gated
Mixture-of-Experts layer (MoE), consisting of up to thousands of feed-forward
sub-networks. A trainable gating network determines a sparse combination of
these experts to use for each example. We apply the MoE to the tasks of
language modeling and machine translation, where model capacity is critical for
absorbing the vast quantities of knowledge available in the training corpora.
We present model architectures in which a MoE with up to 137 billion parameters
is applied convolutionally between stacked LSTM layers. On large language
modeling and machine translation benchmarks, these models achieve significantly
better results than state-of-the-art at lower computational cost. | http://arxiv.org/pdf/1701.06538 | Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, Jeff Dean | cs.LG, cs.CL, cs.NE, stat.ML | null | null | cs.LG | 20170123 | 20170123 | [
{
"id": "1502.03167"
},
{
"id": "1606.04199"
},
{
"id": "1602.02410"
},
{
"id": "1609.08144"
},
{
"id": "1511.06297"
},
{
"id": "1512.02595"
}
] |
1701.06538 | 12 | H(x)i = (x · Wg)i + StandardN ormal() · Sof tplus((x · Wnoise)i) (4)
# Ui
KeepT opK(v, k)i = if vi is in the top k elements of v. ââ otherwise. (5)
Training the Gating Network We train the gating network by simple back-propagation, along with the rest of the model. If we choose k > 1, the gate values for the top k experts have nonzero derivatives with respect to the weights of the gating network. This type of occasionally-sensitive behavior is described in (Bengio et al., 2013) with respect to noisy rectiï¬ers. Gradients also back- propagate through the gating network to its inputs. Our method differs here from (Bengio et al., 2015) who use boolean gates and a REINFORCE-style approach to train the gating network.
3 ADDRESSING PERFORMANCE CHALLENGES
3.1 THE SHRINKING BATCH PROBLEM | 1701.06538#12 | Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer | The capacity of a neural network to absorb information is limited by its
number of parameters. Conditional computation, where parts of the network are
active on a per-example basis, has been proposed in theory as a way of
dramatically increasing model capacity without a proportional increase in
computation. In practice, however, there are significant algorithmic and
performance challenges. In this work, we address these challenges and finally
realize the promise of conditional computation, achieving greater than 1000x
improvements in model capacity with only minor losses in computational
efficiency on modern GPU clusters. We introduce a Sparsely-Gated
Mixture-of-Experts layer (MoE), consisting of up to thousands of feed-forward
sub-networks. A trainable gating network determines a sparse combination of
these experts to use for each example. We apply the MoE to the tasks of
language modeling and machine translation, where model capacity is critical for
absorbing the vast quantities of knowledge available in the training corpora.
We present model architectures in which a MoE with up to 137 billion parameters
is applied convolutionally between stacked LSTM layers. On large language
modeling and machine translation benchmarks, these models achieve significantly
better results than state-of-the-art at lower computational cost. | http://arxiv.org/pdf/1701.06538 | Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, Jeff Dean | cs.LG, cs.CL, cs.NE, stat.ML | null | null | cs.LG | 20170123 | 20170123 | [
{
"id": "1502.03167"
},
{
"id": "1606.04199"
},
{
"id": "1602.02410"
},
{
"id": "1609.08144"
},
{
"id": "1511.06297"
},
{
"id": "1512.02595"
}
] |
1701.06538 | 13 | 3 ADDRESSING PERFORMANCE CHALLENGES
3.1 THE SHRINKING BATCH PROBLEM
On modern CPUs and GPUs, large batch sizes are necessary for computational efficiency, so as to amortize the overhead of parameter loads and updates. If the gating network chooses k out of n experts for each example, then for a batch of b examples, each expert receives a much smaller batch of approximately ab < b examples. This causes a naive MoE implementation to become very inefficient as the number of experts increases. The solution to this shrinking batch problem is to make the original batch size as large as possible. However, batch size tends to be limited by the memory necessary to store activations between the forwards and backwards passes. We propose the following techniques for increasing the batch size: | 1701.06538#13 | Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer | The capacity of a neural network to absorb information is limited by its
number of parameters. Conditional computation, where parts of the network are
active on a per-example basis, has been proposed in theory as a way of
dramatically increasing model capacity without a proportional increase in
computation. In practice, however, there are significant algorithmic and
performance challenges. In this work, we address these challenges and finally
realize the promise of conditional computation, achieving greater than 1000x
improvements in model capacity with only minor losses in computational
efficiency on modern GPU clusters. We introduce a Sparsely-Gated
Mixture-of-Experts layer (MoE), consisting of up to thousands of feed-forward
sub-networks. A trainable gating network determines a sparse combination of
these experts to use for each example. We apply the MoE to the tasks of
language modeling and machine translation, where model capacity is critical for
absorbing the vast quantities of knowledge available in the training corpora.
We present model architectures in which a MoE with up to 137 billion parameters
is applied convolutionally between stacked LSTM layers. On large language
modeling and machine translation benchmarks, these models achieve significantly
better results than state-of-the-art at lower computational cost. | http://arxiv.org/pdf/1701.06538 | Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, Jeff Dean | cs.LG, cs.CL, cs.NE, stat.ML | null | null | cs.LG | 20170123 | 20170123 | [
{
"id": "1502.03167"
},
{
"id": "1606.04199"
},
{
"id": "1602.02410"
},
{
"id": "1609.08144"
},
{
"id": "1511.06297"
},
{
"id": "1512.02595"
}
] |
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