Tegg Sung Deep learning researcher

Currently reading lists

11 Jan 2018

Everyday I read papers related to deep reinforcement learning and gaussian processes which are my interested research topics. Those gave me research inspirations and thoughts. Also I check reddit and twitter for the state-of-the-art research papers. For the reinforcement learning, such topics including better exploration methods, continuous controls, and hierarchical learning are my tastes. I started to get interested in gaussian process by researching better exploration. Uncertainty is the key of the deep learning and largely concerning to reinforcement learning too. The below lists are the collection of currently reading papers.

Deep Reinforcement Learning

Conventional RL

  • H. van Seijen, Effective multi-step temporal-difference learning for non-linear function approximation (2016)
  • K. De Asis et al., Multi-step reinforcement learning: a unifying algorithm (2017)
  • A. Mahmood, Incremental off-policy reinforcement learning algorithms (2017, Ph.D. thesis)
  • R. Sutton and A. Barto, Reinforcement learning: an introduction (2nd ed.) (2017, textbook)

MDP

  • A. Ng, Shaping and policy search in reinforcement learning Ch.1 & 2 (2003, Ph.D. thesis)

Deep RL

  • L. Lin, Reinforcement learning for robots using neural networks (1993, Ph.D. thesis)
  • V. Mnih et al., Human-level control through deep reinforcement learning (2015)
  • V. Mnih et al., Playing atari with deep reinforcement learning (2013)

Inverse RL

  • C. Finn et al., A connection between generative adversarial networks, inverse reinforcement learning, and energy-based models (2016)
  • B. Ziebart et al., Maximum entropy inverse reinforcement learning (2010)

Imitation learning

  • S. Ross et al., A reduction of imitation learning and structured prediction to no-regret online learning (2011, DAGGER)

Meta learning

  • Y. Duan, Meta learning (2017, Ph.D. thesis)

Continuous control

  • Y. Wu, E. Mansimov et al., Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (2017, ACKTR)
  • D. Silver et al., Deterministic policy gradient algorithms (2014, DPG)
  • T. Lillicrap et al., Continuous control with deep reinforcement learning (2016, DDPG)
  • R. Islam et al., Reproducibility of benchmarked deep reinforcement learning tasks for continuous control (2017)
  • N. Heess et al., Learning continuous control policies by stochastic value gradients (2015, SVG)
  • V. Mnih et al., Asynchronous methods for deep reinforcement learning (2016, A3C)
  • T. Haarnoja et al., Reinforcement learning with deep energy-based policies (2017, Soft Q-learning)

Improving exploration

  • I. Osband et al., Deep exploration via bootstrapped DQN (2016)
  • M. Plappert et al., Parameter space noise for exploration (2017)

Model-based RL

  • M. Deisenroth and C. Rasmussen, PILCO: A model-based and data-efficient approach to policy search (2011, PILCO)

Policy gradient

  • R. Sutton et al., Policy gradient methods for reinforcement learning with function approximation (2000)
  • J. Peters and S. Schaal, Policy gradient methods for robotics (2006)
  • J. Peters and S. Schaal, Reinforcement learning of motor skills with policy gradients (2008)

Gaussian Process

Uncertainty

  • Y. Gal, Uncertainty in deep learning (2017, Ph.D. thesis)
  • Z. Ghahramani, Probabilistic machine learning and artificial intelligence (2015)
  • N. Srivastava et al., Dropout: A simple way to prevent neural networks from overfitting (2014)
  • Y. Gal and Z. Ghahramani, Dropout as a bayesian approximation: representing model uncertainty in deep learning (2016)
  • C. Guo et al., On calibration of modern neural networks (2017)

GP

  • C.E. Rasmussen and C.K.I. Williams, Gaussian processes for machine learning (2006, textbook)
  • C. Viroli and G.J. McLachlan, Deep gaussian mixture models (2017)

Kernel

  • D. Duvenaud et al., Structure discovery in nonparametric regression through compositional kernel search (2013)
  • A. Wilson and R. Adams, Gaussian process kernels for pattern discovery and exploration (2013)

Mathematics

  • G. Moore, The emergence of open sets, closed sets, and limit points in analysis and topology (2008)

Variational inference

  • A. Graves, Practical variational inference for neural networks (2011)

Other topics.

Generative models

  • C. Vondrick et al., Generating videos with scene dynamics (2016)
  • I. Goodfellow, NIPS 2016 tutorial: Generative adversarial networks (2017)
  • D. Kingma and M. Welling, Auto-encoding variational bayes (2014 VAE)
  • I. Goodfellow et al., Generative adversarial nets (2014, GAN)
  • A. Radford and L. Metz et al., Unsupervised representational learning with deep convolutional generative adversarial networks (2016, DCGAN)

Visual domains

  • M. Mathieu et al., Deep multi-scale video prediction beyond mean square error (2016)
  • H. Altwaijry et al., Learning to match aerial images with deep attentive architectures (2016)
  • M. Ranzato et al., Video (language) modeling: A baseline for generative models of natural videos (2016)
  • W. Lotter et al., Deep predictive coding networks for video prediction and unsupervised learning (2017)
  • G. Huang et al., Densely connected convolutional networks (2017, DenseNet)
  • K. Simonyan and A. Zisserman, Very deep convolutional networks for large-scale image recognition (2015, VGGnet)
  • M. Lin et al., Network in network (2014)
  • A. Krizhevsky et al., ImageNet classification with deep convolutional neural networks (2012)
  • K. He et al., Deep residual learning for image recognition (2015, ResNet)
  • C. Szegedy et al., Going deeper with convolutions (2015, GoogLeNet)
  • G. Masi et al., Pansharpening by convolutional neural networks (2016)
  • C. Dong et al., Image super-resolution using deep convolutional networks (2015)
  • A. Karpathy et al., Large-scale video classification with convolutional neural networks (2014)
  • M. D. Zeiler and R. Fergus, Visualizing and understanding convolutional networks (2013, ZFNet)

NLP

  • I. Sutskever et al., Sequence to sequence learning with neural networks (2014, Seq2seq)
  • J. Pennington et al., GloVe: Global vectors for word representation (2014)
  • Y. Kim, Convolutional neural networks for sentence classification (2014)

Related Posts