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2012

  • A Better Way to Pretrain Deep Boltzmann Machines.[[pdf](docs/2012/A Better Way to Pretrain Deep Boltzmann Machines.pdf)] [url]
  • A GeneratDeep neural networks for acoustic modeling in speech recognition: The shared views of four research groupsive Process for Sampling Contractive Auto-Encoders.[[pdf](docs/2012/A Generative Process for Sampling Contractive Auto-Encoders.pdf)] [url]
  • An Efficient Learning Procedure for Deep Boltzmann Machines.[[pdf](docs/2012/An Efficient Learning Procedure for Deep Boltzmann Machines.pdf)] [url]
  • Autoencoders, Unsupervised Learning, and Deep Architectures.[[pdf](docs/2012/Autoencoders, Unsupervised Learning, and Deep Architectures(2012).pdf)] [url]
  • Building High-level Features Using Large Scale Unsupervised Learning.[[pdf](docs/2012/Building High-level Features Using Large Scale Unsupervised Learning.pdf)] [url]
  • Deep Learning of Representations for Unsupervised and Transfer Learning.[[pdf](docs/2012/Deep Learning of Representations for Unsupervised and Transfer Learning.pdf)] [url]
  • Deep Learning via Semi-Supervised Embedding.[[pdf](docs/2012/Deep Learning via Semi-Supervised Embedding.pdf)] [url]
  • Deep Learning with Hierarchical Convolutional Factor Analysis.[[pdf](docs/2012/Deep Learning with Hierarchical Convolutional Factor Analysis.pdf)] [url]
  • Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups.[url] ⭐
  • RDiscriminative Learning of Sum-Product Networks.[[pdf](docs/2012/Discriminative Learning of Sum-Product Networks.pdf)] [url]
  • [AlexNet] ImageNet Classification with Deep Convolutional Neural Networks. [[pdf](docs/2012/ImageNet Classification with Deep Convolutional Neural Networks.pdf)] [url] [code] ⭐
  • [Dropout] Improving neural networks by preventing co-adaptation of feature detectors.[[pdf](docs/2012/Improving neural networks by preventing co-adaptation of feature detectors.pdf)] [arxiv] ⭐
  • Invariant Scattering Convolution Networks.[[pdf](docs/2012/Invariant Scattering Convolution Networks.pdf)] [url]
  • Learning with Hierarchical-Deep Models.[[pdf](docs/2012/Learning with Hierarchical-Deep Models.pdf)] [url]
  • Practical Bayesian Optimization of Machine Learning Algorithms.[[pdf](docs/2012/Practical Bayesian Optimization of Machine Learning Algorithms.pdf)] [url] ⭐
  • Practical Recommendations for Gradient-Based Training of Deep Architectures.[[pdf](docs/2012/Practical Recommendations for Gradient-Based Training of Deep Architectures.pdf)] [url]
  • Random Search for Hyper-Parameter Optimization.[[pdf](docs/2012/Random Search for Hyper-Parameter Optimization.pdf)] [url] ⭐

Transfer learning

  • Cross-domain co-extraction of sentiment and topic lexicons. [pdf] ⭐
  • Domain adaptation from multiple sources: a domain-dependent regularization approach. [pdf]
  • Domain Transfer Multiple Kernel Learning. [pdf]
  • Information-Theoretical Learning of Discriminative Clusters for Unsupervised Domain Adaptation. [pdf]
  • Learning with Augmented Features for Heterogeneous Domain Adaptation. [pdf]
  • Semi-Supervised Kernel Matching for Domain Adaptation. [pdf]
  • Supplementary Material Geodesic Flow Kernel for Unsupervised Domain Adaptation. [pdf]
  • TALMUD: transfer learning for multiple domains. [pdf]