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
Implementing engrams from a machine learning perspective: the relevance of a latent space
J Marco de Lucas
On The Expressive Power of Knowledge Graph Embedding Methods
Jiexing Gao, Dmitry Rodin, Vasily Motolygin, Denis Zaytsev
EIANet: A Novel Domain Adaptation Approach to Maximize Class Distinction with Neural Collapse Principles
Zicheng Pan, Xiaohan Yu, Yongsheng Gao