variational inference
A technique in Bayesian inference that approximates complex posterior distributions using optimization, facilitating the use of probabilistic models in AI.
- Act to See, See to Act: Diffusion-Driven Perception-Action Interplay for Adaptive Policies
- Brain-like Variational Inference
- Deep Taxonomic Networks for Unsupervised Hierarchical Prototype Discovery
- Functional Complexity-adaptive Temporal Tensor Decomposition
- In Search of Adam’s Secret Sauce
- In Search of Adam’s Secret Sauce
- Least squares variational inference
- Model-Informed Flows for Bayesian Inference
- Rao-Blackwellised Reparameterisation Gradients
- SING: SDE Inference via Natural Gradients
- Towards Identifiability of Hierarchical Temporal Causal Representation Learning
- Training Robust Graph Neural Networks by Modeling Noise Dependencies
- Training-Free Bayesianization for Low-Rank Adapters of Large Language Models
- VERA: Variational Inference Framework for Jailbreaking Large Language Models
- VaMP: Variational Multi-Modal Prompt Learning for Vision-Language Models
- Variational Inference with Mixtures of Isotropic Gaussians
- Variational Supervised Contrastive Learning