{"total":50,"rows":[{"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":"2210.01241","title":"Is Reinforcement Learning (Not) for Natural Language Processing: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization","created":"2022-10-03","cited_by_count":246,"area":null},{"id":"2109.00486","title":"Survey of Low-Resource Machine Translation","created":"2021-09-01","cited_by_count":92,"area":null},{"id":"2106.11344","title":"f-Domain-Adversarial Learning: Theory and Algorithms","created":"2021-06-21","cited_by_count":47,"area":null},{"id":"2101.08176","title":"Introduction to Normalizing Flows for Lattice Field Theory","created":"2021-01-20","cited_by_count":35,"area":null},{"id":"2211.08714","title":"Reward Gaming in Conditional Text Generation","created":"2022-11-16","cited_by_count":30,"area":null},{"id":"2304.11431","title":"A Review of Deep Learning for Video Captioning","created":"2023-04-22","cited_by_count":28,"area":null},{"id":"2302.13344","title":"Tailoring Language Generation Models under Total Variation Distance","created":"2023-02-26","cited_by_count":27,"area":null},{"id":"2103.04130","title":"Learning to Generate 3D Shapes with Generative Cellular Automata","created":"2021-03-06","cited_by_count":22,"area":null},{"id":"2109.08705","title":"Relating Neural Text Degeneration to Exposure Bias","created":"2021-09-17","cited_by_count":19,"area":null},{"id":"2307.03749","title":"On the Efficacy of Sampling Adapters","created":"2023-07-07","cited_by_count":17,"area":null},{"id":"2305.16958","title":"MixCE: Training Autoregressive Language Models by Mixing Forward and Reverse Cross-Entropies","created":"2023-05-26","cited_by_count":16,"area":null},{"id":"2101.02046","title":"TextBox: A Unified, Modularized, and Extensible Framework for Text Generation","created":"2021-01-06","cited_by_count":15,"area":null},{"id":"2303.12132","title":"Fundamentals of Generative Large Language Models and Perspectives in Cyber-Defense","created":"2023-03-21","cited_by_count":13,"area":null},{"id":"2410.05165","title":"Efficient Inference for Large Language Model-based Generative Recommendation","created":"2024-10-07","cited_by_count":11,"area":null},{"id":"2106.05232","title":"Realizing GANs via a Tunable Loss Function","created":"2021-06-09","cited_by_count":10,"area":null},{"id":"2311.01388","title":"Time-series Generation by Contrastive Imitation","created":"2023-11-02","cited_by_count":9,"area":null},{"id":"2304.08968","title":"Stochastic Parrots Looking for Stochastic Parrots: LLMs are Easy to Fine-Tune and Hard to Detect with other LLMs","created":"2023-04-18","cited_by_count":8,"area":null},{"id":"2310.01041","title":"Language Model Decoding as Direct Metrics Optimization","created":"2023-10-02","cited_by_count":8,"area":null},{"id":"2201.06118","title":"DeepCreativity: Measuring Creativity with Deep Learning Techniques","created":"2022-01-16","cited_by_count":7,"area":null},{"id":"2602.22617","title":"Semantic Tube Prediction: Beating LLM Data Efficiency with JEPA","created":"2026-02-26","cited_by_count":7,"area":null},{"id":"2310.18291","title":"Addressing GAN Training Instabilities via Tunable Classification Losses","created":"2023-10-27","cited_by_count":5,"area":null},{"id":"2111.13282","title":"Generative Adversarial Networks and Adversarial Autoencoders: Tutorial and Survey","created":"2021-11-26","cited_by_count":4,"area":null},{"id":"2112.10139","title":"Denoised Labels for Financial Time-Series Data via Self-Supervised Learning","created":"2021-12-19","cited_by_count":4,"area":null},{"id":"2103.00265","title":"A Brief Introduction to Generative Models","created":"2021-02-27","cited_by_count":3,"area":null},{"id":"2203.07437","title":"Unsupervised Clustering of Roman Potsherds via Variational Autoencoders","created":"2022-03-14","cited_by_count":3,"area":null},{"id":"2108.03578","title":"Language Model Evaluation in Open-ended Text Generation","created":"2021-08-08","cited_by_count":2,"area":null},{"id":"2108.12275","title":"Can the Transformer Be Used as a Drop-in Replacement for RNNs in Text-Generating GANs?","created":"2021-08-26","cited_by_count":2,"area":null},{"id":"2109.07277","title":"Photon detection probability prediction using one-dimensional generative neural network","created":"2021-09-11","cited_by_count":2,"area":null},{"id":"2309.17157","title":"LatticeGen: A Cooperative Framework which Hides Generated Text in a Lattice for Privacy-Aware Generation on Cloud","created":"2023-09-29","cited_by_count":2,"area":null},{"id":"2109.06308","title":"Improving Scheduled Sampling with Elastic Weight Consolidation for Neural Machine Translation","created":"2021-09-13","cited_by_count":1,"area":null},{"id":"2210.10715","title":"Autoregressive Generative Modeling with Noise Conditional Maximum Likelihood Estimation","created":"2022-10-19","cited_by_count":1,"area":null},{"id":"2502.14075","title":"Towards Vector Optimization on Low-Dimensional Vector Symbolic Architecture","created":"2025-02-19","cited_by_count":1,"area":null},{"id":"2510.03074","title":"Neural Posterior Estimation with Autoregressive Tiling for Detecting Objects in Astronomical Images","created":"2025-10-03","cited_by_count":1,"area":null},{"id":"2512.09152","title":"Understanding temperature tuning in energy-based models","created":"2025-12-09","cited_by_count":1,"area":null},{"id":"2602.08660","title":"Equalized Generative Treatment: Matching f-divergences for Fairness in Generative Models","created":"2026-02-09","cited_by_count":1,"area":null},{"id":"2606.12990","title":"Exposure Bias as Epistemic Underidentification in Recursive Forecasting","created":"2026-06-11","cited_by_count":1,"area":null},{"id":"2101.09313","title":"$k$-Neighbor Based Curriculum Sampling for Sequence Prediction","created":"2021-01-22","cited_by_count":0,"area":null},{"id":"2102.03710","title":"HGAN: Hybrid Generative Adversarial Network","created":"2021-02-07","cited_by_count":0,"area":null},{"id":"2107.01273","title":"Visual Time Series Forecasting: An Image-driven Approach","created":"2021-07-02","cited_by_count":0,"area":null},{"id":"2204.13597","title":"PhysioGAN: Training High Fidelity Generative Model for Physiological Sensor Readings","created":"2022-04-25","cited_by_count":0,"area":null},{"id":"2211.07234","title":"Shared Loss between Generators of GANs","created":"2022-11-14","cited_by_count":0,"area":null},{"id":"2306.13576","title":"Penalty Gradient Normalization for Generative Adversarial Networks","created":"2023-06-23","cited_by_count":0,"area":null},{"id":"2312.07040","title":"Rethinking Model Inversion Attacks With Patch-Wise Reconstruction","created":"2023-12-12","cited_by_count":0,"area":null},{"id":"2603.03405","title":"Surprisal-R\\'enyi Free Energy","created":"2026-03-03","cited_by_count":0,"area":null},{"id":"2605.13225","title":"Mix, Don't Tune: Bilingual Pre-Training Outperforms Hyperparameter Search in Data-Constrained Settings","created":"2026-05-13","cited_by_count":0,"area":null},{"id":"2605.30981","title":"Cognitive Fatigue in Autoregressive Transformers: Formalization and Measurement","created":"2026-05-29","cited_by_count":0,"area":null},{"id":"2605.31438","title":"Flow map learning in nonlinear vector autoregressive models: influence of the feature-library structure on the training error","created":"2026-05-29","cited_by_count":0,"area":null},{"id":"2609.31635","title":"What Next-Event Accuracy Cannot See: Closed-Loop Evaluation of Emergency Department Trajectory Simulators","created":"2026-09-10","cited_by_count":0,"area":null},{"id":"2609.33405","title":"Decoupling Token Roles in Autoregressive Pretraining","created":"2026-09-27","cited_by_count":0,"area":null}],"next_cursor":null}