{"total":133,"rows":[{"id":"2106.05931","title":"Score-based Generative Modeling in Latent Space","created":"2021-06-10","cited_by_count":636,"area":"diffusion"},{"id":"2103.04922","title":"Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models","created":"2021-03-08","cited_by_count":252,"area":null},{"id":"2303.04226","title":"A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT","created":"2023-03-07","cited_by_count":224,"area":null},{"id":"2305.01140","title":"Geometric Latent Diffusion Models for 3D Molecule Generation","created":"2023-05-02","cited_by_count":186,"area":"diffusion"},{"id":"2101.08629","title":"Image-to-Image Translation: Methods and Applications","created":"2021-01-21","cited_by_count":116,"area":null},{"id":"2201.00308","title":"DiffuseVAE: Efficient, Controllable and High-Fidelity Generation from Low-Dimensional Latents","created":"2022-01-02","cited_by_count":104,"area":"diffusion"},{"id":"2205.15612","title":"GlanceNets: Interpretabile, Leak-proof Concept-based Models","created":"2022-05-31","cited_by_count":73,"area":null},{"id":"2205.07547","title":"SQ-VAE: Variational Bayes on Discrete Representation with Self-annealed Stochastic Quantization","created":"2022-05-16","cited_by_count":68,"area":null},{"id":"2209.14733","title":"Hyper-Representations as Generative Models: Sampling Unseen Neural Network Weights","created":"2022-09-29","cited_by_count":64,"area":"distill"},{"id":"2305.19951","title":"Not All Neuro-Symbolic Concepts Are Created Equal: Analysis and Mitigation of Reasoning Shortcuts","created":"2023-05-31","cited_by_count":63,"area":null},{"id":"2104.05168","title":"Soft then Hard: Rethinking the Quantization in Neural Image Compression","created":"2021-04-12","cited_by_count":51,"area":null},{"id":"2303.15919","title":"Fully Hyperbolic Convolutional Neural Networks for Computer Vision","created":"2023-03-28","cited_by_count":49,"area":null},{"id":"2305.06351","title":"Reconstructing Animatable Categories from Videos","created":"2023-05-10","cited_by_count":47,"area":null},{"id":"2404.02954","title":"Deep Generative Models through the Lens of the Manifold Hypothesis: A Survey and New Connections","created":"2024-04-03","cited_by_count":47,"area":"diffusion"},{"id":"2107.10189","title":"DRIVE: Deep Reinforced Accident Anticipation with Visual Explanation","created":"2021-07-21","cited_by_count":46,"area":null},{"id":"2205.06924","title":"A Tale of Two Flows: Cooperative Learning of Langevin Flow and Normalizing Flow Toward Energy-Based Model","created":"2022-05-13","cited_by_count":44,"area":null},{"id":"2302.02228","title":"Counterfactual Identifiability of Bijective Causal Models","created":"2023-02-04","cited_by_count":43,"area":null},{"id":"2106.09016","title":"Smoothing the Disentangled Latent Style Space for Unsupervised Image-to-Image Translation","created":"2021-06-16","cited_by_count":35,"area":null},{"id":"2206.04496","title":"Mitigating Modality Collapse in Multimodal VAEs via Impartial Optimization","created":"2022-06-09","cited_by_count":32,"area":null},{"id":"2105.09371","title":"VOILA: Visual-Observation-Only Imitation Learning for Autonomous Navigation","created":"2021-05-19","cited_by_count":31,"area":"offlinerl"},{"id":"2204.07172","title":"Diagnosing and Fixing Manifold Overfitting in Deep Generative Models","created":"2022-04-14","cited_by_count":28,"area":null},{"id":"2206.02416","title":"Embrace the Gap: VAEs Perform Independent Mechanism Analysis","created":"2022-06-06","cited_by_count":28,"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":"2105.05735","title":"Autoencoding Under Normalization Constraints","created":"2021-05-12","cited_by_count":26,"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":"2308.14177","title":"AI-Generated Content (AIGC) for Various Data Modalities: A Survey","created":"2023-08-27","cited_by_count":25,"area":null},{"id":"2206.05266","title":"Does Self-supervised Learning Really Improve Reinforcement Learning from Pixels?","created":"2022-06-10","cited_by_count":24,"area":null},{"id":"2207.06415","title":"The Free Energy Principle for Perception and Action: A Deep Learning Perspective","created":"2022-07-13","cited_by_count":24,"area":null},{"id":"2209.07370","title":"A Geometric Perspective on Variational Autoencoders","created":"2022-09-15","cited_by_count":24,"area":null},{"id":"2306.17638","title":"Geometric Autoencoders -- What You See is What You Decode","created":"2023-06-30","cited_by_count":23,"area":null},{"id":"2103.01071","title":"A survey on Variational Autoencoders from a GreenAI perspective","created":"2021-03-01","cited_by_count":22,"area":null},{"id":"2109.00055","title":"Sentence Bottleneck Autoencoders from Transformer Language Models","created":"2021-08-31","cited_by_count":19,"area":null},{"id":"2208.14226","title":"Unsupervised Representation Learning in Deep Reinforcement Learning: A Review","created":"2022-08-27","cited_by_count":18,"area":null},{"id":"2209.08739","title":"Adaptive Multi-stage Density Ratio Estimation for Learning Latent Space Energy-based Model","created":"2022-09-19","cited_by_count":18,"area":null},{"id":"2306.01843","title":"Lifting Architectural Constraints of Injective Flows","created":"2023-06-02","cited_by_count":18,"area":null},{"id":"2401.03321","title":"PIXAR: Auto-Regressive Language Modeling in Pixel Space","created":"2024-01-06","cited_by_count":16,"area":null},{"id":"2102.08663","title":"Preventing Oversmoothing in VAE via Generalized Variance Parameterization","created":"2021-02-17","cited_by_count":15,"area":null},{"id":"2111.13236","title":"Joint inference and input optimization in equilibrium networks","created":"2021-11-25","cited_by_count":15,"area":null},{"id":"2205.00608","title":"Physics-aware Reduced-order Modeling of Transonic Flow via $\\beta$-Variational Autoencoder","created":"2022-05-02","cited_by_count":15,"area":null},{"id":"2102.07559","title":"Certifiably Robust Variational Autoencoders","created":"2021-02-15","cited_by_count":14,"area":null},{"id":"2403.05300","title":"Unity by Diversity: Improved Representation Learning in Multimodal VAEs","created":"2024-03-08","cited_by_count":14,"area":null},{"id":"2101.02149","title":"Cauchy-Schwarz Regularized Autoencoder","created":"2021-01-06","cited_by_count":13,"area":null},{"id":"2308.02652","title":"A Review of Change of Variable Formulas for Generative Modeling","created":"2023-08-04","cited_by_count":13,"area":null},{"id":"2310.03218","title":"Learning Energy-Based Prior Model with Diffusion-Amortized MCMC","created":"2023-10-05","cited_by_count":13,"area":null},{"id":"2207.10951","title":"Hyper-Representations for Pre-Training and Transfer Learning","created":"2022-07-22","cited_by_count":12,"area":"distill"},{"id":"2407.08751","title":"Latent Diffusion for Neural Spiking Data","created":"2024-06-27","cited_by_count":12,"area":"diffusion"},{"id":"2102.11495","title":"Anytime Sampling for Autoregressive Models via Ordered Autoencoding","created":"2021-02-23","cited_by_count":11,"area":null},{"id":"2306.15711","title":"Semi-supervised Multimodal Representation Learning through a Global Workspace","created":"2023-06-27","cited_by_count":11,"area":null},{"id":"2309.10852","title":"Using AI Uncertainty Quantification to Improve Human Decision-Making","created":"2023-09-19","cited_by_count":11,"area":null},{"id":"2105.08204","title":"Synthesising Multi-Modal Minority Samples for Tabular Data","created":"2021-05-17","cited_by_count":10,"area":null},{"id":"2106.06245","title":"Model Selection for Bayesian Autoencoders","created":"2021-06-11","cited_by_count":10,"area":null},{"id":"2110.04935","title":"Learning Temporally-Consistent Representations for Data-Efficient Reinforcement Learning","created":"2021-10-11","cited_by_count":10,"area":null},{"id":"2304.00948","title":"VTAE: Variational Transformer Autoencoder with Manifolds Learning","created":"2023-04-03","cited_by_count":10,"area":null},{"id":"2401.17835","title":"Simplifying Latent Dynamics with Softly State-Invariant World Models","created":"2024-01-31","cited_by_count":10,"area":"wm"},{"id":"2103.16773","title":"PAUL: Procrustean Autoencoder for Unsupervised Lifting","created":"2021-03-31","cited_by_count":9,"area":null},{"id":"2211.07725","title":"Hierarchically Structured Task-Agnostic Continual Learning","created":"2022-11-14","cited_by_count":9,"area":null},{"id":"2307.06431","title":"Energy Discrepancies: A Score-Independent Loss for Energy-Based Models","created":"2023-07-12","cited_by_count":9,"area":null},{"id":"2310.07665","title":"Deep Backtracking Counterfactuals for Causally Compliant Explanations","created":"2023-10-11","cited_by_count":9,"area":null},{"id":"2306.06323","title":"Learning Joint Latent Space EBM Prior Model for Multi-layer Generator","created":"2023-06-10","cited_by_count":8,"area":null},{"id":"2112.04598","title":"InvGAN: Invertible GANs","created":"2021-12-08","cited_by_count":7,"area":null},{"id":"2310.19944","title":"Conditional Unscented Autoencoders for Trajectory Prediction","created":"2023-10-30","cited_by_count":7,"area":null},{"id":"2312.02469","title":"Learning Energy-based Model via Dual-MCMC Teaching","created":"2023-12-05","cited_by_count":7,"area":null},{"id":"2403.10988","title":"Boosting Flow-based Generative Super-Resolution Models via Learned Prior","created":"2024-03-16","cited_by_count":7,"area":null},{"id":"2505.18230","title":"Follow the Energy, Find the Path: Riemannian Metrics from Energy-Based Models","created":"2025-05-23","cited_by_count":7,"area":null},{"id":"2008.07284","title":"Forward and inverse reinforcement learning sharing network weights and hyperparameters","created":"2020-08-17","cited_by_count":6,"area":"offlinerl"},{"id":"2301.00011","title":"eVAE: Evolutionary Variational Autoencoder","created":"2023-01-01","cited_by_count":6,"area":null},{"id":"2310.09604","title":"Learning Hierarchical Features with Joint Latent Space Energy-Based Prior","created":"2023-10-14","cited_by_count":6,"area":null},{"id":"2311.07693","title":"Matching aggregate posteriors in the variational autoencoder","created":"2023-11-13","cited_by_count":6,"area":null},{"id":"2501.10901","title":"ARD-VAE: A Statistical Formulation to Find the Relevant Latent Dimensions of Variational Autoencoders","created":"2025-01-18","cited_by_count":6,"area":null},{"id":"2206.11267","title":"Neural Implicit Manifold Learning for Topology-Aware Density Estimation","created":"2022-06-22","cited_by_count":5,"area":null},{"id":"2211.04468","title":"An efficient graph generative model for navigating ultra-large combinatorial synthesis libraries","created":"2022-10-19","cited_by_count":5,"area":null},{"id":"2305.10406","title":"Variational Classification","created":"2023-05-17","cited_by_count":5,"area":null},{"id":"2407.04958","title":"Entropy-Informed Weighting Channel Normalizing Flow for Deep Generative Models","created":"2024-07-06","cited_by_count":5,"area":null},{"id":"2510.12311","title":"Learning Latent Energy-Based Models via Interacting Particle Langevin Dynamics","created":"2025-10-14","cited_by_count":5,"area":null},{"id":"2205.09546","title":"Deterministic training of generative autoencoders using invertible layers","created":"2022-05-19","cited_by_count":4,"area":null},{"id":"2403.02598","title":"Pooling Image Datasets With Multiple Covariate Shift and Imbalance","created":"2024-03-05","cited_by_count":4,"area":null},{"id":"2507.04385","title":"Tractable Representation Learning with Probabilistic Circuits","created":"2025-07-06","cited_by_count":4,"area":null},{"id":"2109.10777","title":"Deep Variational Clustering Framework for Self-labeling of Large-scale Medical Images","created":"2021-09-22","cited_by_count":3,"area":null},{"id":"2209.11350","title":"Oracle Analysis of Representations for Deep Open Set Detection","created":"2022-09-22","cited_by_count":3,"area":null},{"id":"2304.04664","title":"Inductive biases in deep learning models for weather prediction","created":"2023-04-06","cited_by_count":3,"area":null},{"id":"2406.06079","title":"Latent Representation Matters: Human-like Sketches in One-shot Drawing Tasks","created":"2024-06-10","cited_by_count":3,"area":"diffusion"},{"id":"2410.14038","title":"Sliding Puzzles Gym: A Scalable Benchmark for State Representation in Visual Reinforcement Learning","created":"2024-10-17","cited_by_count":3,"area":"deeprl"},{"id":"2507.18838","title":"Flow Stochastic Segmentation Networks","created":"2025-07-24","cited_by_count":3,"area":null},{"id":"2103.00142","title":"Disentangling Geometric Deformation Spaces in Generative Latent Shape Models","created":"2021-02-27","cited_by_count":2,"area":null},{"id":"2107.09286","title":"ByPE-VAE: Bayesian Pseudocoresets Exemplar VAE","created":"2021-07-20","cited_by_count":2,"area":null},{"id":"2109.05232","title":"Joint Debiased Representation Learning and Imbalanced Data Clustering","created":"2021-09-11","cited_by_count":2,"area":null},{"id":"2109.13596","title":"Exploratory State Representation Learning","created":"2021-09-28","cited_by_count":2,"area":null},{"id":"2110.00784","title":"Seeking Visual Discomfort: Curiosity-driven Representations for Reinforcement Learning","created":"2021-10-02","cited_by_count":2,"area":null},{"id":"2112.00305","title":"Forward Operator Estimation in Generative Models with Kernel Transfer Operators","created":"2021-12-01","cited_by_count":2,"area":null},{"id":"2209.00281","title":"Large-Scale Auto-Regressive Modeling Of Street Networks","created":"2022-09-01","cited_by_count":2,"area":null},{"id":"2306.05256","title":"Unscented Autoencoder","created":"2023-06-08","cited_by_count":2,"area":null},{"id":"2110.10303","title":"Momentum Contrastive Autoencoder: Using Contrastive Learning for Latent Space Distribution Matching in WAE","created":"2021-10-19","cited_by_count":1,"area":null},{"id":"2110.12185","title":"Group-disentangled Representation Learning with Weakly-Supervised Regularization","created":"2021-10-23","cited_by_count":1,"area":null},{"id":"2202.02738","title":"Enhancing variational generation through self-decomposition","created":"2022-02-06","cited_by_count":1,"area":null},{"id":"2202.03434","title":"Multi-modal data generation with a deep metric variational autoencoder","created":"2022-02-07","cited_by_count":1,"area":null},{"id":"2203.00645","title":"Variational Autoencoders Without the Variation","created":"2022-03-01","cited_by_count":1,"area":null},{"id":"2206.10101","title":"Model-Based Imitation Learning Using Entropy Regularization of Model and Policy","created":"2022-06-21","cited_by_count":1,"area":"offlinerl"},{"id":"2208.10940","title":"Evaluating Out-of-Distribution Detectors Through Adversarial Generation of Outliers","created":"2022-08-20","cited_by_count":1,"area":null},{"id":"2209.01555","title":"Latent Preserving Generative Adversarial Network for Imbalance classification","created":"2022-09-04","cited_by_count":1,"area":null},{"id":"2304.02473","title":"Fully Variational Noise-Contrastive Estimation","created":"2023-04-04","cited_by_count":1,"area":null}],"next_cursor":"100"}