{"total":69,"rows":[{"id":"2206.05075","title":"Diffeomorphic Counterfactuals with Generative Models","created":"2022-06-10","cited_by_count":29,"area":null},{"id":"2105.00026","title":"Data Augmentation in High Dimensional Low Sample Size Setting Using a Geometry-Based Variational Autoencoder","created":"2021-04-30","cited_by_count":27,"area":null},{"id":"2206.08309","title":"Pythae: Unifying Generative Autoencoders in Python -- A Benchmarking Use Case","created":"2022-06-16","cited_by_count":26,"area":null},{"id":"2209.07370","title":"A Geometric Perspective on Variational Autoencoders","created":"2022-09-15","cited_by_count":24,"area":null},{"id":"2106.04315","title":"Learning Riemannian Manifolds for Geodesic Motion Skills","created":"2021-06-08","cited_by_count":23,"area":null},{"id":"2106.05367","title":"Pulling back information geometry","created":"2021-06-09","cited_by_count":23,"area":null},{"id":"2310.01211","title":"From Bricks to Bridges: Product of Invariances to Enhance Latent Space Communication","created":"2023-10-02","cited_by_count":21,"area":null},{"id":"2405.09800","title":"Manifold Integrated Gradients: Riemannian Geometry for Feature Attribution","created":"2024-05-16","cited_by_count":21,"area":null},{"id":"2305.19101","title":"Which Models have Perceptually-Aligned Gradients? An Explanation via Off-Manifold Robustness","created":"2023-05-30","cited_by_count":20,"area":null},{"id":"2206.07387","title":"The Manifold Hypothesis for Gradient-Based Explanations","created":"2022-06-15","cited_by_count":16,"area":null},{"id":"2305.19730","title":"Data Representations' Study of Latent Image Manifolds","created":"2023-05-31","cited_by_count":11,"area":null},{"id":"2602.15293","title":"The Information Geometry of Softmax: Probing and Steering","created":"2026-02-17","cited_by_count":11,"area":null},{"id":"2304.00948","title":"VTAE: Variational Transformer Autoencoder with Manifolds Learning","created":"2023-04-03","cited_by_count":10,"area":null},{"id":"2309.08247","title":"A Geometric Perspective on Autoencoders","created":"2023-09-15","cited_by_count":8,"area":null},{"id":"2403.06612","title":"Pulling back symmetric Riemannian geometry for data analysis","created":"2024-03-11","cited_by_count":8,"area":null},{"id":"2103.16605","title":"Unsupervised Disentanglement of Linear-Encoded Facial Semantics","created":"2021-03-30","cited_by_count":7,"area":null},{"id":"2210.05810","title":"A unified model for continuous conditional video prediction","created":"2022-10-11","cited_by_count":7,"area":null},{"id":"2112.02450","title":"Adaptive Feature Interpolation for Low-Shot Image Generation","created":"2021-12-04","cited_by_count":6,"area":null},{"id":"2202.09258","title":"Autoencoding Low-Resolution MRI for Semantically Smooth Interpolation of Anisotropic MRI","created":"2022-02-18","cited_by_count":6,"area":null},{"id":"2203.07761","title":"Reactive Motion Generation on Learned Riemannian Manifolds","created":"2022-03-15","cited_by_count":6,"area":null},{"id":"2301.03345","title":"Latent Spectral Regularization for Continual Learning","created":"2023-01-09","cited_by_count":6,"area":null},{"id":"2102.00264","title":"Atlas Generative Models and Geodesic Interpolation","created":"2021-01-30","cited_by_count":5,"area":null},{"id":"2310.17072","title":"MMP++: Motion Manifold Primitives with Parametric Curve Models","created":"2023-10-26","cited_by_count":5,"area":null},{"id":"2410.13792","title":"Analyzing Deep Transformer Models for Time Series Forecasting via Manifold Learning","created":"2024-10-17","cited_by_count":5,"area":null},{"id":"2605.06258","title":"The Weight Gram Matrix Captures Sequential Feature Linearization in Deep Networks","created":"2026-05-07","cited_by_count":5,"area":null},{"id":"2106.12096","title":"Learning Identity-Preserving Transformations on Data Manifolds","created":"2021-06-22","cited_by_count":4,"area":null},{"id":"2106.15962","title":"On the Generative Utility of Cyclic Conditionals","created":"2021-06-30","cited_by_count":4,"area":null},{"id":"2208.03923","title":"Adversarial robustness of VAEs through the lens of local geometry","created":"2022-08-08","cited_by_count":4,"area":null},{"id":"2208.10193","title":"Convergent autoencoder approximation of low bending and low distortion manifold embeddings","created":"2022-08-22","cited_by_count":4,"area":null},{"id":"2403.06425","title":"A Differential Geometric View and Explainability of GNN on Evolving Graphs","created":"2024-03-11","cited_by_count":4,"area":null},{"id":"2506.01599","title":"Connecting Neural Models Latent Geometries with Relative Geodesic Representations","created":"2025-06-02","cited_by_count":4,"area":null},{"id":"2106.10777","title":"Manifold Matching via Deep Metric Learning for Generative Modeling","created":"2021-06-20","cited_by_count":3,"area":null},{"id":"2502.13757","title":"Identifying Metric Structures of Deep Latent Variable Models","created":"2025-02-19","cited_by_count":3,"area":null},{"id":"2502.19070","title":"A Sample-Level Evaluation and Generative Framework for Model Inversion Attacks","created":"2025-02-26","cited_by_count":3,"area":null},{"id":"2503.08240","title":"Tangentially Aligned Integrated Gradients for User-Friendly Explanations","created":"2025-03-11","cited_by_count":3,"area":null},{"id":"2603.28764","title":"Geometry-aware similarity metrics for neural representations on Riemannian and statistical manifolds","created":"2026-03-30","cited_by_count":3,"area":null},{"id":"2201.10423","title":"Rayleigh EigenDirections (REDs): GAN latent space traversals for multidimensional features","created":"2022-01-25","cited_by_count":2,"area":null},{"id":"2205.07477","title":"Manifold Characteristics That Predict Downstream Task Performance","created":"2022-05-16","cited_by_count":2,"area":null},{"id":"2302.00908","title":"GANalyzer: Analysis and Manipulation of GANs Latent Space for Controllable Face Synthesis","created":"2023-02-02","cited_by_count":2,"area":null},{"id":"2406.18588","title":"Varying Manifolds in Diffusion: From Time-varying Geometries to Visual Saliency","created":"2024-06-07","cited_by_count":2,"area":null},{"id":"2503.00892","title":"Riemannian Integrated Gradients: A Geometric View of Explainable AI","created":"2025-03-02","cited_by_count":2,"area":null},{"id":"2509.22697","title":"Learning Hyperspectral Images with Curated Text Prompts for Efficient Multimodal Alignment","created":"2025-09-20","cited_by_count":2,"area":null},{"id":"2510.09468","title":"Geodesic Calculus on Implicitly Defined Latent Manifolds","created":"2025-10-10","cited_by_count":2,"area":null},{"id":"2601.18678","title":"Counterfactual Explanations on Robust Perceptual Geodesics","created":"2026-01-26","cited_by_count":2,"area":null},{"id":"2107.03903","title":"Manifold Hypothesis in Data Analysis: Double Geometrically-Probabilistic Approach to Manifold Dimension Estimation","created":"2021-07-08","cited_by_count":1,"area":null},{"id":"2205.05764","title":"Deep Learning and Synthetic Media","created":"2022-05-11","cited_by_count":1,"area":null},{"id":"2211.07195","title":"Controllable GAN Synthesis Using Non-Rigid Structure-from-Motion","created":"2022-11-14","cited_by_count":1,"area":null},{"id":"2306.10055","title":"Smoothing the Rough Edges: Evaluating Automatically Generated Multi-Lattice Transitions","created":"2023-06-13","cited_by_count":1,"area":null},{"id":"2307.02637","title":"Surge Routing: Event-informed Multiagent Reinforcement Learning for Autonomous Rideshare","created":"2023-07-05","cited_by_count":1,"area":null},{"id":"2311.08850","title":"Controlling the Output of a Generative Model by Latent Feature Vector Shifting","created":"2023-11-15","cited_by_count":1,"area":null},{"id":"2407.11244","title":"(Deep) Generative Geodesics","created":"2024-07-15","cited_by_count":1,"area":null},{"id":"2602.02611","title":"Discovering Data Manifold Geometry through Geometric Properties","created":"2026-02-02","cited_by_count":1,"area":null},{"id":"2605.24942","title":"Riemannian-Manifold Steering: Geometry-Aware Generative Autoencoders for Label-Free Steering","created":"2026-05-24","cited_by_count":1,"area":null},{"id":"2104.13189","title":"Learning low bending and low distortion manifold embeddings","created":"2021-04-27","cited_by_count":0,"area":null},{"id":"2203.01035","title":"Discriminating Against Unrealistic Interpolations in Generative Adversarial Networks","created":"2022-03-02","cited_by_count":0,"area":null},{"id":"2210.00577","title":"Deep Invertible Approximation of Topologically Rich Maps between Manifolds","created":"2022-10-02","cited_by_count":0,"area":null},{"id":"2303.12711","title":"Modeling Barrett's Esophagus Progression using Geometric Variational Autoencoders","created":"2023-03-17","cited_by_count":0,"area":null},{"id":"2303.14220","title":"Variational Inference for Longitudinal Data Using Normalizing Flows","created":"2023-03-24","cited_by_count":0,"area":null},{"id":"2305.10513","title":"Learning Pose Image Manifolds Using Geometry-Preserving GANs and Elasticae","created":"2023-05-17","cited_by_count":0,"area":null},{"id":"2409.05459","title":"Beyond Flatland: A Geometric Take on Matching Methods for Treatment Effect Estimation","created":"2024-09-09","cited_by_count":0,"area":null},{"id":"2503.02773","title":"Prime Convolutional Model: Breaking the Ground for Theoretical Explainability","created":"2025-03-04","cited_by_count":0,"area":null},{"id":"2503.06773","title":"Investigating Image Manifolds of 3D Objects: Learning, Shape Analysis, and Comparisons","created":"2025-03-09","cited_by_count":0,"area":null},{"id":"2507.10201","title":"History Matching under Uncertainty of Geological Scenarios with Implicit Geological Realism Control with Generative Deep Learning and Graph Convolutions","created":"2025-07-14","cited_by_count":0,"area":null},{"id":"2509.26221","title":"Marginal Flow: a flexible and efficient framework for density estimation","created":"2025-09-30","cited_by_count":0,"area":null},{"id":"2510.01988","title":"PepCompass: Navigating peptide embedding spaces using Riemannian Geometry","created":"2025-10-02","cited_by_count":0,"area":null},{"id":"2510.26266","title":"A Likely Geometry of Generative Models","created":"2025-10-30","cited_by_count":0,"area":null},{"id":"2609.37677","title":"Learning Expressive and Compositional Motion Representation via Spectral Skills","created":"2026-09-29","cited_by_count":0,"area":null},{"id":"2609.39115","title":"Beyond Local Linearity: Scale-Resolved Geometry of Learned Image Encoders","created":"2026-09-30","cited_by_count":0,"area":null},{"id":"2610.00708","title":"Beyond Unimodal Bases: Pullback Geometry for Multimodal Data","created":"2026-09-30","cited_by_count":0,"area":null}],"next_cursor":null}