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Jianfeng Gao | Jurgen Schmidhuber | Paul Smolensky | Roland Fernandez | Nebojsa Jojic | Imanol Schlag | J. Schmidhuber | P. Smolensky | Jianfeng Gao | N. Jojic | Imanol Schlag | Roland Fernandez
[1] Jürgen Schmidhuber,et al. Learning to Reason with Third-Order Tensor Products , 2018, NeurIPS.
[2] Paul Smolensky,et al. Tensor Product Variable Binding and the Representation of Symbolic Structures in Connectionist Systems , 1990, Artif. Intell..
[3] Yoshua Bengio,et al. Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.
[4] J. Schmidhuber. Reducing the Ratio Between Learning Complexity and Number of Time Varying Variables in Fully Recurrent Nets , 1993 .
[5] Jürgen Schmidhuber,et al. Learning to Control Fast-Weight Memories: An Alternative to Dynamic Recurrent Networks , 1992, Neural Computation.
[6] Geoffrey E. Hinton,et al. Distributed Representations , 1986, The Philosophy of Artificial Intelligence.
[7] A. G. Ivakhnenko,et al. Polynomial Theory of Complex Systems , 1971, IEEE Trans. Syst. Man Cybern..
[8] Klaus Greff,et al. A Perspective on Objects and Systematic Generalization in Model-Based RL , 2019, ArXiv.
[9] Pushmeet Kohli,et al. Analysing Mathematical Reasoning Abilities of Neural Models , 2019, ICLR.
[10] Christopher D. Manning,et al. A Structural Probe for Finding Syntax in Word Representations , 2019, NAACL.
[11] Christoph Goller,et al. Learning task-dependent distributed representations by backpropagation through structure , 1996, Proceedings of International Conference on Neural Networks (ICNN'96).
[12] Li Deng,et al. Deep Learning of Grammatically-Interpretable Representations Through Question-Answering , 2017, ArXiv.
[13] Virginia R. de Sa,et al. Learning Distributed Representations of Symbolic Structure Using Binding and Unbinding Operations , 2018, ArXiv.
[14] Guillaume Lample,et al. Augmenting Self-attention with Persistent Memory , 2019, ArXiv.
[15] Aaron C. Courville,et al. Systematic Generalization: What Is Required and Can It Be Learned? , 2018, ICLR.
[16] Martin Wattenberg,et al. Visualizing and Measuring the Geometry of BERT , 2019, NeurIPS.
[17] F. Scarselli,et al. A new model for learning in graph domains , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..
[18] Ming-Wei Chang,et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.
[19] Razvan Pascanu,et al. Relational recurrent neural networks , 2018, NeurIPS.
[20] Li Deng,et al. Question-Answering with Grammatically-Interpretable Representations , 2017, AAAI.
[21] Jürgen Schmidhuber,et al. Training Very Deep Networks , 2015, NIPS.
[22] Marco Baroni,et al. Generalization without Systematicity: On the Compositional Skills of Sequence-to-Sequence Recurrent Networks , 2017, ICML.
[23] Robert Frank,et al. Open Sesame: Getting inside BERT’s Linguistic Knowledge , 2019, BlackboxNLP@ACL.
[24] Christoph von der Malsburg,et al. The Correlation Theory of Brain Function , 1994 .
[25] Razvan Pascanu,et al. Stabilizing Transformers for Reinforcement Learning , 2019, ICML.
[26] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[27] Dipanjan Das,et al. BERT Rediscovers the Classical NLP Pipeline , 2019, ACL.