{"list":"other","total":53,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"1412.6980","title":"Adam: A Method for Stochastic Optimization","created":"2014-12-22","cited_by_count":37240},{"id":"2006.11239","title":"Denoising Diffusion Probabilistic Models","created":"2020-06-19","cited_by_count":21076},{"id":"2112.10752","title":"High-Resolution Image Synthesis with Latent Diffusion Models","created":"2021-12-20","cited_by_count":18863},{"id":"2010.02502","title":"Denoising Diffusion Implicit Models","created":"2020-10-06","cited_by_count":9781},{"id":"2011.13456","title":"Score-Based Generative Modeling through Stochastic Differential Equations","created":"2020-11-26","cited_by_count":9122},{"id":"1503.03585","title":"Deep Unsupervised Learning using Nonequilibrium Thermodynamics","created":"2015-03-12","cited_by_count":7058},{"id":"2210.02747","title":"Flow Matching for Generative Modeling","created":"2022-10-06","cited_by_count":5702},{"id":"2206.00927","title":"DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps","created":"2022-06-02","cited_by_count":1979},{"id":"2107.03006","title":"Structured Denoising Diffusion Models in Discrete State-Spaces","created":"2021-07-07","cited_by_count":1719},{"id":"2205.14217","title":"Diffusion-LM Improves Controllable Text Generation","created":"2022-05-27","cited_by_count":1027},{"id":"2502.09992","title":"Large Language Diffusion Models","created":"2025-02-14","cited_by_count":912},{"id":"2102.05379","title":"Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions","created":"2021-02-10","cited_by_count":574},{"id":"2209.12152","title":"All are Worth Words: A ViT Backbone for Diffusion Models","created":"2022-09-25","cited_by_count":502},{"id":"1908.01207","title":"Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks","created":"2019-08-03","cited_by_count":448},{"id":"2505.22618","title":"Fast-dLLM: Training-free Acceleration of Diffusion LLM by Enabling KV Cache and Parallel Decoding","created":"2025-05-28","cited_by_count":420},{"id":"2503.09573","title":"Block Diffusion: Interpolating Between Autoregressive and Diffusion Language Models","created":"2025-03-12","cited_by_count":409},{"id":"2208.11970","title":"Understanding Diffusion Models: A Unified Perspective","created":"2022-08-25","cited_by_count":340},{"id":"2002.09291","title":"Transformer Hawkes Process","created":"2020-02-21","cited_by_count":235},{"id":"2210.17432","title":"SSD-LM: Semi-autoregressive Simplex-based Diffusion Language Model for Text Generation and Modular Control","created":"2022-10-31","cited_by_count":194},{"id":"1909.12127","title":"Intensity-Free Learning of Temporal Point Processes","created":"2019-09-26","cited_by_count":140},{"id":"2509.26328","title":"Fast-dLLM v2: Efficient Block-Diffusion LLM","created":"2025-09-30","cited_by_count":139},{"id":"2508.09192","title":"Diffusion LLMs Can Do Faster-Than-AR Inference via Discrete Diffusion Forcing","created":"2025-08-08","cited_by_count":116},{"id":"1801.02294","title":"Learning Tree-based Deep Model for Recommender Systems","created":"2018-01-08","cited_by_count":99},{"id":"2509.24695","title":"SANA-Video: Efficient Video Generation with Block Linear Diffusion Transformer","created":"2025-09-29","cited_by_count":94},{"id":"1705.08982","title":"Modeling The Intensity Function Of Point Process Via Recurrent Neural Networks","created":"2017-05-24","cited_by_count":93},{"id":"2201.00044","title":"Transformer Embeddings of Irregularly Spaced Events and Their Participants","created":"2021-12-31","cited_by_count":93},{"id":"2307.08097","title":"EasyTPP: Towards Open Benchmarking Temporal Point Processes","created":"2023-07-16","cited_by_count":68},{"id":"2210.01753","title":"HYPRO: A Hybridly Normalized Probabilistic Model for Long-Horizon Prediction of Event Sequences","created":"2022-10-04","cited_by_count":57},{"id":"2305.12403","title":"Spatio-temporal Diffusion Point Processes","created":"2023-05-21","cited_by_count":52},{"id":"2305.16646","title":"Language Models Can Improve Event Prediction by Few-Shot Abductive Reasoning","created":"2023-05-26","cited_by_count":45},{"id":"1905.05570","title":"Imputing Missing Events in Continuous-Time Event Streams","created":"2019-05-14","cited_by_count":44},{"id":"1806.00221","title":"Lecture Notes: Temporal Point Processes and the Conditional Intensity Function","created":"2018-06-01","cited_by_count":40},{"id":"2007.13794","title":"Neural Temporal Point Processes For Modelling Electronic Health Records","created":"2020-07-27","cited_by_count":39},{"id":"1507.02293","title":"COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution","created":"2015-07-08","cited_by_count":37},{"id":"2208.01874","title":"Exploring Generative Neural Temporal Point Process","created":"2022-08-03","cited_by_count":30},{"id":"2502.07737","title":"Next Block Prediction: Video Generation via Semi-Autoregressive Modeling","created":"2025-02-11","cited_by_count":26},{"id":"2510.22852","title":"Encoder-Decoder Diffusion Language Models for Efficient Training and Inference","created":"2025-10-26","cited_by_count":24},{"id":"2311.01139","title":"Add and Thin: Diffusion for Temporal Point Processes","created":"2023-11-02","cited_by_count":23},{"id":"2101.02815","title":"Long Horizon Forecasting With Temporal Point Processes","created":"2021-01-08","cited_by_count":20},{"id":"2409.18158","title":"Decomposable Transformer Point Processes","created":"2024-09-26","cited_by_count":16},{"id":"2306.17066","title":"On the Predictive Accuracy of Neural Temporal Point Process Models for Continuous-time Event Data","created":"2023-06-29","cited_by_count":14},{"id":"2410.22493","title":"Unlocking Point Processes through Point Set Diffusion","created":"2024-10-29","cited_by_count":14},{"id":"2412.19634","title":"Deep Continuous-Time State-Space Models for Marked Event Sequences","created":"2024-12-27","cited_by_count":11},{"id":"2310.17800","title":"Interacting Diffusion Processes for Event Sequence Forecasting","created":"2023-10-26","cited_by_count":10},{"id":"2510.06050","title":"Edit-Based Flow Matching for Temporal Point Processes","created":"2025-10-07","cited_by_count":8},{"id":"2303.12341","title":"EasyDGL: Encode, Train and Interpret for Continuous-time Dynamic Graph Learning","created":"2023-03-22","cited_by_count":7},{"id":"2311.01033","title":"Non-Autoregressive Diffusion-based Temporal Point Processes for Continuous-Time Long-Term Event Prediction","created":"2023-11-02","cited_by_count":7},{"id":"2505.18411","title":"DanmakuTPPBench: A Multi-modal Benchmark for Temporal Point Process Modeling and Understanding","created":"2025-05-23","cited_by_count":7},{"id":"1912.09522","title":"Event Outlier Detection in Continuous Time","created":"2019-12-19","cited_by_count":6},{"id":"2106.04465","title":"Detecting Anomalous Event Sequences with Temporal Point Processes","created":"2021-06-08","cited_by_count":6},{"id":"2211.08499","title":"Probabilistic Querying of Continuous-Time Event Sequences","created":"2022-11-15","cited_by_count":5},{"id":"2501.14291","title":"Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches","created":"2025-01-24","cited_by_count":3}],"next_cursor":null}