# Deep Learning - A First Look at the Crypto-Mining Malware Ecosystem: A Decade of Unrestricted Wealth. [`arxiv`](https://arxiv.org/abs/1901.00846) - A Gentle Introduction to Deep Learning for Graphs. [`arxiv`](https://arxiv.org/abs/1912.12693) - AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty. [`arxiv`](https://arxiv.org/pdf/1912.02781.pdf) [`code`](https://github.com/google-research/augmix) :star: - diffGrad: An Optimization Method for Convolutional Neural Networks. [`arxiv`](https://arxiv.org/abs/1909.11015) [`code`](https://github.com/lessw2020/Best-Deep-Learning-Optimizers) - LiSHT: Non-Parametric Linearly Scaled Hyperbolic Tangent Activation Function for Neural Networks. [`arxiv`](https://arxiv.org/abs/1901.05894) - One-Class Convolutional Neural Network. [`arxiv`](https://arxiv.org/abs/1901.08688) - On the effect of the activation function on the distribution of hidden nodes in a deep network. [`arxiv`](https://arxiv.org/abs/1901.02104) - TactileGCN: A Graph Convolutional Network for Predicting Grasp Stability with Tactile Sensors. [`arxiv`](https://arxiv.org/abs/1901.06181) ## Attention - Attentive Neural Processes. [`arxiv`](https://arxiv.org/abs/1901.05761) :star: - FAN: Focused Attention Networks. [`arxiv`](https://arxiv.org/abs/1905.11498) ## Auto ML - A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments. [`arxiv`](https://arxiv.org/abs/1911.00294) - Combinatorial Bayesian Optimization using the Graph Cartesian Product. [`arxiv`](https://arxiv.org/abs/1902.00448) [`code`](https://github.com/QUVA-Lab/COMBO) - EAT-NAS: Elastic Architecture Transfer for Accelerating Large-scale Neural Architecture Search. [`arxiv`](https://arxiv.org/abs/1901.05884v1) ## Transfer Learning - Time Series Anomaly Detection Using Convolutional Neural Networks and Transfer Learning. [`arxiv`](https://arxiv.org/abs/1905.13628) - Virtual-to-Real-World Transfer Learning for Robots on Wilderness Trails. [`arxiv`](https://arxiv.org/abs/1901.05599)