Deep Learning
Mastering Atari Games with Limited Data Weirui Y e
Reinforcement learning has achieved great success in many applications.
Batch Multi-Fidelity Bayesian Optimization with Deep Auto-Regressive Networks
Bayesian optimization (BO) is a powerful approach for optimizing black-box, expensive-to-evaluate functions. To enable a flexible trade-off between the cost and accuracy, many applications allow the function to be evaluated at different fidelities.
Collapsed Variational Bounds for Bayesian Neural Networks
In this paper, we also apply inference to the prior parameters of Gaussian BNNs (e.g.
BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs Kay Liu
Despite the importance of graph OD and many algorithms being developed for it in recent years, there is no comprehensive benchmark on graph outlier detection, which we believe has hindered the development and understanding of graph OD algorithms.
MORA: Improving Ensemble Robustness Evaluation with Model-Reweighing Attack Y unrui Y u
Adversarial attacks can deceive neural networks by adding tiny perturbations to their input data.
MORA: Improving Ensemble Robustness Evaluation with Model-Reweighing Attack Y unrui Y u
Adversarial attacks can deceive neural networks by adding tiny perturbations to their input data.