Відео

7.2 Learning Fast Optimizers For Contextual Stochastic Integer Programs

Full title: Constraint-Based Casual Discovery For Non-Linear Structural Causal Models With Cycles And Latent Confounders

7.3 Abstraction Sampling In Graphical Models

18.2 A Lagrangian Perspective On Latent Variable Generative Models

2. Bayesian Optimization

8.3 A Dual Approach To Scalable Verifiaction Of Deep Networks.mp4

3.2 End To End QA

15. Reproducibility, Reusability, And Robustness In Deep Reinforcement Learning

16.3 Non-Parametric Path Analysis In Structural Causal Models

16. Causal Learning For Partially Observed Stochastic Dynamical Systems

16.2 Identification Of Personalized Effects Associated With Causal Pathways

1.2 Addressing Data Security In Deep Learning.mp4

13.3 Discrete Sampling Using Semigradient-Based Product Mixtures

13.2 A Unified Particle-Optimization Framework For Scalable Bayesian Sampling

17. Towards Flatter Loss Surface Via Nonmonotonic Learning Rate Scheduling

17.3 Revisiting Differentially Private Linear Regression

12.3 Variational Zero-Inflated Gaussian Process With Sparse Kernels

13. Lifted Marginal MAP Inference

8. Adaptive Stratified Sampling For Precision-Recall Estimation

9. Bigger Data About Smaller People: Studying Children's Language Learning At Scale

6.2 Sylvester Normalizing Flow For Variational Inference

6.3 Hyperspherical Variational Auto-Encoders

17.2 Averaging Weights Leads To Wider Optima And Better Generalization

4. Recent Progress In The Theory Of Deep Learning

3. UAI Tutorial On Machine Reading

1. Tackling Data Scarcity In Deep Learning.mp4

6. The Variational Homoencoder

7. A Forest Mixture Bound For Block-Free Parallel Inference

14. Causal Discovery With Linear Non-Gaussian Models Under Measurement Error