Instructional Material
Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes
Humans can quickly learn new tasks from just a handful of examples by preserving rich representations of experience [1].
Supplementary: Training Generative Adversarial Networks by Solving Ordinary Differential Equations
Image samples are shown in Section I.
Deep Knowledge Tracing
Chris Piech, Jonathan Bassen, Jonathan Huang, Surya Ganguli, Mehran Sahami, Leonidas J. Guibas, Jascha Sohl-Dickstein
Neural Information Processing Systems http://nips.cc/
Deep Reinforcement and InfoMax Learning
We begin with the hypothesis that a model-free agent whose representations are predictive of properties of future states (beyond expected rewards) will be more capable of solving and adapting to new RL problems.
Lifelong Learning with Non-i.i.d. Tasks
Anastasia Pentina, Christoph H. Lampert
Neural Information Processing Systems http://nips.cc/
Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization Haoliang Li1Y uFei Wang 1 Renjie Wan 1 Shiqi Wang 2
Recently, we have witnessed great progress in the field of medical imaging classification by adopting deep neural networks. However, the recent advanced models still require accessing sufficiently large and representative datasets for training, which is often unfeasible in clinically realistic environments.
Large-Scale Bayesian Multi-Label Learning via Topic-Based Label Embeddings
Piyush Rai, Changwei Hu, Ricardo Henao, Lawrence Carin
Neural Information Processing Systems http://nips.cc/
A Latent Variational Framework for Stochastic Optimization
Neural Information Processing Systems http://nips.cc/