# ml
- Meta-Learning in Neural Networks: A Survey (Hospedales, 2020) -
- Deep Mean Function Meta Learning Gaussian Processes (Fortuin, 2019) -
- Model-agnostic meta-learning (MAML) -
- Bayesian meta-learning: a primer -
- EfficientNet -
- Neural Architecture Search -
- “Domain Adaptation Via Teacher Student Learning For Speech Recognition” 👩🏫➡👩🎓 -
- Inductive Bias -
- Gaussian Processes (GP) -
- Nonparametric Vs Parametric Methods -
- KL Divergence -
- Inductive Vs Deductive -
- Tensor Networks -
- Clever Hans Effect -
- Batch Norm For Flows -
- Activation Normalization (Act Norm) -
- Learning Rate Scheduling/Warm-up -
- L2 Regularization -
- L2 With Batch Norm -
- Batch Normalization -
- Empirical Bayes -
- Implicit Maximum Likelihood Estimation (IMLE) by Ke Li -
- On Statistical Thinking In Deep Learning -
- Linear Regression -
- Pseudoinverse -
- Nonparametric Methods -
- “Bayesian Optimization for Likelihood-Free Inference” (BOLFI) -
- “Neural Ordinary Differential Equations” 🐰🦊 -
- Neural Processes for image completion 🎨🖌 -
- “Advanced probabilistic methods in machine learning” notes -
- scikit-learn in a nutshell 🥜 -
- “Motivating the Rules of the Game for Adversarial Example Research” -
- Privacy-preserving ML: Paper summaries and discussion notes -
- “Membership Inference Attacks Against Machine Learning Models” -
- The duality of \(p(x|\theta)\) -
- Monstrous machine learning notes -
- PAISS: PRAIRIE AI Summer School with Inria and NAVER LABS Europe -