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論文まとめ

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論文まとめ

この記事について

自分の研究に関連する論文のまとめ.あるいは学生とのシェア用.気まぐれに随時更新.

映像から言語

  • Nguyen et al., (2017). "Translating Videos to Commands for Robotic Manipulation with Deep Recurrent Neural Networks." [arXiv]

言語とロボット動作の変換

言語から動作
  • Yamada et al., (2016/07). "Dynamical Integration of Language and Behavior in a Recurrent Neural Network for Human–Robot Interaction." [Frontiers]
動作から言語
  • Heinrich and Wermter., (2014). "Interactive Language Understanding with Multiple Timescale Recurrent Neural Networks," ICANN2017, [Springer]
双方向変換
  • Ogata et al., (2007). "Two-way translation of compound sentences and arm motions by recurrent neural networks," IROS2007. [IEEEXplore]
  • Sugita and Tani., (2005). "Learning Semantic Combinatoriality from the Interaction between Linguistic and Behavioral Processes." [SAGE]

Semantic Navigation

Vision-and-Language Navigation (VLN.画像と文章から)
  • Das et al., (2017/12). "Embodied Question Answering." [arXiv]
  • Anderson et al., (2017/11). "Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments." [arXiv]
  • Hermann et al., (2017/06). "Grounded Language Learning in a Simulated 3D World." [arXiv]
  • Chaplot et al., (2017/06). "Gated-Attention Architectures for Task-Oriented Language Grounding." [arXiv]
LRFによるSemantic Navigation
  • Luo and Chen., (2017). "Recursive Neural Network Based Semantic Navigation of an Autonomous Mobile Robot through Understanding Human Verbal Instructions," IROS2017, No open-access file.

翻訳,主にneural machine translation (NMT)

  • Lample et al., FAIR, (2017/11). "Unsupervised Machine Translation Using Monolingual Corpora Only." [arXiv]
  • Johnson et al., Google, (2016/11). "Google's Multilingual Neural Machine Translation System : Enabling Zero-Shot Translation." [arXiv]
  • Luong et al., Stanford Univ., (2015/08). "Effective Approaches to Attention-based Neural Machine Translation." [arXiv]
  • Bahdanau et al., w/Bengio, (2014/09)."Neural Machine Translation by Jointly Learning to Align and Translate." [arXiv]
Attention関連の技術
  • See et al., Stanford Univ. and Google, (2017/04). "Get To The Point: Summarization with Pointer-Generator Networks." [arXiv]
  • Gu et al., (2016/03). "Incorporating Copying Mechanism in Sequence-to-Sequence Learning." [arXiv]
  • Vinyals et al., Google Brain, (2015/06). "Pointer networks." [arXiv]

表現学習(Representation Learning)

  • Tran et al., Open AI, (2017/12). "Feature-Matching Auto-Encoders." [pdf]

コミュニケーションによる記号創発

  • Havrylov and Titov, (2017/05). "Emergence of Language with Multi-agent Games: Learning to Communicate with Sequences of Symbols." [arXiv]
  • Mordatch and Abbeel, (2017/03). "Emergence of Grounded Compositional Language in Multi-Agent Populations." [arXiv]

ニューラルネット,ディープラーニング基本テクニックやモデルあれこれ

  • CapsNet初出.Sabour et al., (2017/11). "Dynamic Routing Between Capsules." [arXiv]
  • Adam初出.Kingma and Ba, (2014/12). "Adam: A Method for Stochastic Optimization." [arXiv]
  • VAE初出.Kingma and Welling, (2013/12). "Auto-Encoding Variational Bayes." [arXiv]
  • Elman型RNN. Elman, (1990). "Finding Structure in Time." [ScienceDirect]
  • BPTT初出.Rumelhart et al., (1986). "“Learning internal representations by error propagation." [pdf]

レビュー論文

  • DLによる自然言語処理.Young et al., (2017/08). "Recent Trends in Deep Learning Based Natural Language Processing." [arXiv]
  • DLによる音楽生成.Briot et al., (2017/09). "Deep Learning Techniques for Music Generation - A Survey." [arXiv]
  • 記号創発ロボティクス.Taniguchi et al. (2015/09). "Symbol Emergence in Robotics: A Survey." [arXiv]

古典

  • シンボルグラウンディング, Harnad, (1990/02). "The Symbol Grounding Problem." [ScienceDirect]
  • SHRDLU, Winograd, (1972/01). "Understanding natural language." [ScienceDirect]
yamadat25
ニューラルネット(特にリカレント)を用いた言語とロボットの動作の関係性の学習に取り組んでいます。その他に、リカレントネットによるソプラノへのハーモニー付けなど。趣味はギター。最近好きなのはゆるめるモ!
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