Latent Space
Latent space refers to a lower-dimensional representation of high-dimensional data, aiming to capture essential features while reducing computational complexity and improving interpretability. Current research focuses on developing efficient algorithms and model architectures, such as variational autoencoders (VAEs), generative adversarial networks (GANs), and diffusion models, to learn and manipulate these latent spaces for tasks ranging from anomaly detection and image generation to controlling generative models and improving the efficiency of autonomous systems. This work has significant implications across diverse fields, enabling advancements in areas like drug discovery, autonomous driving, and cybersecurity through improved data analysis, model efficiency, and enhanced control over generative processes.
Papers
Latent Space Planning for Multi-Object Manipulation with Environment-Aware Relational Classifiers
Yixuan Huang, Nichols Crawford Taylor, Adam Conkey, Weiyu Liu, Tucker Hermans
Uncertainty Quantification in Deep Neural Networks through Statistical Inference on Latent Space
Luigi Sbailò, Luca M. Ghiringhelli