Garbled highlights from NeurIPS 2020
September 17, 2020 — December 10, 2020
neural nets
statistics
Suspiciously similar content
1 workshops
- Machine Learning for Creativity and Design
- Workshop on Deep Learning and Inverse Problems
- Differentiable vision, graphics, and physics applied to machine learning
- Learning Meaningful Representations of Life
- Tackling Climate Change with Machine Learning
- AI for Earth Sciences
- Causal Discovery & Causality-Inspired Machine Learning
- Interpretable Inductive Biases and Physically Structured Learning
2 interesting papers by ad hoc theme
2.1 causality
- Causal Imitation Learning With Unobserved Confounders
- Causal Learning
- Domain Adaptation as a Problem of Inference on Graphical Models
- Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confounding
- Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs
- Differentiable Causal Discovery from Interventional Data
2.2 learning in continuous time/depth
- Almost Surely Stable Deep Dynamics
- Learning Differential Equations that are Easy to Solve
- Dissecting Neural ODEs
- STEER: Simple Temporal Regularization for Neural ODE
- Training Generative Adversarial Networks by Solving Ordinary Differential Equations
- Ode to an ODE
- Time-Reversal Symmetric ODE Network
- Hypersolvers: Toward Fast Continuous-Depth Models
- On Second Order Behaviour in Augmented Neural ODEs
- Neural Controlled Differential Equations for Irregular Time Series
3 Learning with weird losses
4 ML physical sciences
5 References
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Saha, and Balamurugan. 2020. “Learning with Operator-Valued Kernels in Reproducing Kernel Krein Spaces.” In Advances in Neural Information Processing Systems.
Salim, Korba, and Luise. 2020. “The Wasserstein Proximal Gradient Algorithm.” In Advances in Neural Information Processing Systems.
Shen, Wang, Ribeiro, et al. 2020. “Sinkhorn Natural Gradient for Generative Models.” In Advances in Neural Information Processing Systems.
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