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Recurrent Neural Networks and Related Machines That Learn Algorithms

Publications

Unsupervised Object Keypoint Learning using Local Spatial Predictability

Author(s): A. Gopalakrishnan, S. van Steenkiste, J. Schmidhuber
Published in: ICML 2020 workshop on Object-Oriented Learning: Perception, Representation and Reasoning, 2020

Policy Optimization via Importance Sampling

Author(s): A. M. Metelli, M. Papini, F. Faccio, M. Restelli
Published in: NeurIPS 2018, 2018

Improving Differentiable Neural Computers Through Memory Masking, De-allocation, and Link Distribution Sharpness Control

Author(s): R. Csordas, J. Schmidhuber
Published in: ICLR 2019, 2019

Modular Networks: Learning to Decompose Neural Computation

Author(s): L. Kirsch, J. Kunze, D. Barber
Published in: NeurIPS 2018, 2018

Learning to Reason with Third Order Tensor Products

Author(s): I. Schlag, J. Schmidhuber
Published in: NeurIPS 2018, 2018

Recurrent World Models Facilitate Policy Evolution

Author(s): D. Ha, J. Schmidhuber
Published in: NeurIPS 2018, 2018

Enhancing the Transformer with Explicit Relational Encoding for Math Problem Solving

Author(s): I. Schlag, P. Smolensky, R. Fernandez, N. Jojic, J. Schmidhuber, J. Gao
Published in: Under review, 2020

Improving Generalization in Meta Reinforcement Learning using Neural Objectives

Author(s): L. Kirsch, S. van Steenkiste, J. Schmidhuber
Published in: ICLR 2020, 2020

Generative Adversarial Networks are Special Cases of Artificial Curiosity (1990) and also Closely Related to Predictability Minimization (1991)

Author(s): J. Schmidhuber
Published in: Neural Networks, 2020, ISSN 0893-6080