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Disentangled Representations are representations in machine learning where different factors of variation in the data are separated into distinct components. This allows for better interpretability and control over the learned representations, making them useful for tasks like generative modeling and transfer learning.
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 Adaptive sampling using variational autoencoder and reinforcement learning Adil Rasheed Mikael Aleksander Jansen Shahly Muhammad Faisal Aftab | |||
 Learning Group Actions In Disentangled Latent Image Representations Farhana Hossain Swarnali Miaomiao Zhang Tonmoy Hossain | |||
 SimFlow: Simplified and End-to-End Training of Latent Normalizing Flows Qinyu Zhao Guangting Zheng Tao Yang Rui Zhu Xingjian Leng Stephen Gould Liang Zheng | |||
Learning Reduced Representations for Quantum Classifiers Patrick Odagiu Vasilis Belis Lennart Schulze Panagiotis Barkoutsos Michele Grossi Florentin Reiter Günther Dissertori Ivano Tavernelli Sofia Vallecorsa | |||
 Physically Interpretable Representation Learning with Gaussian Mixture Variational AutoEncoder (GM-VAE) Tiffany Fan Murray Cutforth Marta DÉlia Alexandre Cortiella Alireza Doostan Eric Darve | |||
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 Variational Autoencoder for Calibration: A New ApproachInternational Instrumentation and Measurement Technology Conference (I2MTC), 2025 | |||
 IB-GAN: Disentangled Representation Learning with Information Bottleneck Generative Adversarial NetworksAAAI Conference on Artificial Intelligence (AAAI), 2021 | |||
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