| Dynamic Data Augmentation with Gating Networks for Time Series Recognition | Nov 5, 2021 | Data AugmentationTime Series | CodeCode Available | 1 | 5 |
| Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from Data | Sep 12, 2020 | DenoisingDiversity | CodeCode Available | 1 | 5 |
| Automatic Change-Point Detection in Time Series via Deep Learning | Nov 7, 2022 | Change Point DetectionDeep Learning | CodeCode Available | 1 | 5 |
| Discovering Predictable Latent Factors for Time Series Forecasting | Mar 18, 2023 | Time SeriesTime Series Forecasting | CodeCode Available | 1 | 5 |
| Discovering Mixtures of Structural Causal Models from Time Series Data | Oct 10, 2023 | Causal DiscoveryTime Series | CodeCode Available | 1 | 5 |
| Time series forecasting with Gaussian Processes needs priors | Sep 17, 2020 | Gaussian ProcessesTime Series | CodeCode Available | 1 | 5 |
| Discovering Nonlinear Relations with Minimum Predictive Information Regularization | Jan 7, 2020 | Time SeriesTime Series Analysis | CodeCode Available | 1 | 5 |
| Discrete Graph Structure Learning for Forecasting Multiple Time Series | Jan 18, 2021 | Graph structure learningTime Series | CodeCode Available | 1 | 5 |
| DIME: Fine-grained Interpretations of Multimodal Models via Disentangled Local Explanations | Mar 3, 2022 | Decision MakingDisentanglement | CodeCode Available | 1 | 5 |
| Automated Evolutionary Approach for the Design of Composite Machine Learning Pipelines | Jun 26, 2021 | BIG-bench Machine LearningManagement | CodeCode Available | 1 | 5 |
