Representation Learning
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Benchmarks
Most implemented
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Neural Discrete Representation Learning
Momentum Contrast for Unsupervised Visual Representation Learning
Deep High-Resolution Representation Learning for Visual Recognition
High-Resolution Representations for Labeling Pixels and Regions
Deep High-Resolution Representation Learning for Human Pose Estimation
Papers
Learning Discriminative Geometry for Drifting Models
Recently proposed Drifting Models shift iterative distribution refinement from inference to training, enabling effective one-step generation. However, their performance on complex image datasets depends strongly on the r…
Representation LearningDensity EstimationDo Language Models Need a Trainable Input Embedding Table? Fixed Minimal Token Codes at 1.7B-Class Scale
A trainable input embedding table assigns each vocabulary item an independently adjustable vector. We investigate whether this token-specific parameterization is required for substantial language-modeling capability, or …
Representation LearningHigher-Order Molecular Grammars for Generative and Foundation Models in Chemistry
Molecular learning models are strongly shaped by their underlying representations. Yet standard sequential and graph formalisms struggle to explicitly encode higher-order topology, such as ring systems and recurring moti…
Representation LearningSurface-volume self-supervised representation learning of brain MRI for genetic discovery
Existing genome-wide association studies (GWAS) of brain imaging provide predefined or deep-learning-derived imaging phenotypes, yet these phenotypes come from either volumetric scans or cortical surface meshes, so each …
Self-Supervised LearningRepresentation LearningLatent-Foresight: End-to-End Learning Predictable Representations for Latent World Models
Predicting the future evolution of a scene is a fundamental capability for world modeling. Recent work has shown that operating in the feature space of Vision Foundation Models (VFMs) yields semantically rich representat…
Dimensionality ReductionRepresentation LearningScene UnderstandingPragmatic DML with AI-Learned Representations
Text, images, and other rich covariates are increasingly compressed into AI-learned representations and then used as controls in causal analysis. We study when this approach is valid and develop a practical framework for…
Representation LearningCausal Inference