{"list":"other","total":58,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"2005.14165","title":"Language Models are Few-Shot Learners","created":"2020-05-28","cited_by_count":32419},{"id":"2112.10752","title":"High-Resolution Image Synthesis with Latent Diffusion Models","created":"2021-12-20","cited_by_count":18863},{"id":"2302.13971","title":"LLaMA: Open and Efficient Foundation Language Models","created":"2023-02-27","cited_by_count":14132},{"id":"2307.09288","title":"Llama 2: Open Foundation and Fine-Tuned Chat Models","created":"2023-07-18","cited_by_count":13028},{"id":"2312.00752","title":"Mamba: Linear-Time Sequence Modeling with Selective State Spaces","created":"2023-12-01","cited_by_count":4718},{"id":"2104.09864","title":"RoFormer: Enhanced Transformer with Rotary Position Embedding","created":"2021-04-20","cited_by_count":4703},{"id":"1412.3555","title":"Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling","created":"2014-12-11","cited_by_count":2367},{"id":"2002.05202","title":"GLU Variants Improve Transformer","created":"2020-02-12","cited_by_count":1804},{"id":"1910.07467","title":"Root Mean Square Layer Normalization","created":"2019-10-16","cited_by_count":1480},{"id":"1611.07308","title":"Variational Graph Auto-Encoders","created":"2016-11-21","cited_by_count":1382},{"id":"1805.04833","title":"Hierarchical Neural Story Generation","created":"2018-05-13","cited_by_count":1193},{"id":"1703.00564","title":"MoleculeNet: A Benchmark for Molecular Machine Learning","created":"2017-03-02","cited_by_count":1051},{"id":"2404.02905","title":"Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction","created":"2024-04-03","cited_by_count":1023},{"id":"2203.17003","title":"Equivariant Diffusion for Molecule Generation in 3D","created":"2022-03-31","cited_by_count":677},{"id":"1802.04364","title":"Junction Tree Variational Autoencoder for Molecular Graph Generation","created":"2018-02-12","cited_by_count":652},{"id":"2005.00341","title":"Jukebox: A Generative Model for Music","created":"2020-04-30","cited_by_count":542},{"id":"2209.14734","title":"DiGress: Discrete Denoising diffusion for graph generation","created":"2022-09-29","cited_by_count":520},{"id":"1802.08773","title":"GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models","created":"2018-02-24","cited_by_count":413},{"id":"1802.03480","title":"GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders","created":"2018-02-09","cited_by_count":383},{"id":"2010.09885","title":"ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction","created":"2020-10-19","cited_by_count":337},{"id":"2001.09382","title":"GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation","created":"2020-01-26","cited_by_count":305},{"id":"1811.09621","title":"GuacaMol: Benchmarking Models for De Novo Molecular Design","created":"2018-11-22","cited_by_count":297},{"id":"2202.02514","title":"Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations","created":"2022-02-05","cited_by_count":283},{"id":"1811.12823","title":"Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models","created":"2018-11-29","cited_by_count":275},{"id":"2003.00638","title":"Permutation Invariant Graph Generation via Score-Based Generative Modeling","created":"2020-03-02","cited_by_count":270},{"id":"2304.12995","title":"AudioGPT: Understanding and Generating Speech, Music, Sound, and Talking Head","created":"2023-04-25","cited_by_count":265},{"id":"1910.00760","title":"Efficient Graph Generation with Graph Recurrent Attention Networks","created":"2019-10-02","cited_by_count":234},{"id":"1803.03324","title":"Learning Deep Generative Models of Graphs","created":"2018-03-08","cited_by_count":229},{"id":"2202.03036","title":"Structure-Aware Transformer for Graph Representation Learning","created":"2022-02-07","cited_by_count":194},{"id":"2101.05974","title":"Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks","created":"2021-01-15","cited_by_count":193},{"id":"2402.08170","title":"LLaGA: Large Language and Graph Assistant","created":"2024-02-13","cited_by_count":181},{"id":"2310.04560","title":"Talk like a Graph: Encoding Graphs for Large Language Models","created":"2023-10-06","cited_by_count":176},{"id":"2106.05667","title":"GraphiT: Encoding Graph Structure in Transformers","created":"2021-06-10","cited_by_count":147},{"id":"1803.00816","title":"NetGAN: Generating Graphs via Random Walks","created":"2018-03-02","cited_by_count":131},{"id":"2204.01613","title":"SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators","created":"2022-04-04","cited_by_count":99},{"id":"2307.08849","title":"Autoregressive Diffusion Model for Graph Generation","created":"2023-07-17","cited_by_count":83},{"id":"2305.04111","title":"Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modeling","created":"2023-05-06","cited_by_count":80},{"id":"2405.11416","title":"Discrete-state Continuous-time Diffusion for Graph Generation","created":"2024-05-19","cited_by_count":76},{"id":"2103.03864","title":"Learning to Extend Molecular Scaffolds with Structural Motifs","created":"2021-03-05","cited_by_count":71},{"id":"2001.08184","title":"GraphGen: A Scalable Approach to Domain-agnostic Labeled Graph Generation","created":"2020-01-22","cited_by_count":66},{"id":"2006.15502","title":"Scalable Deep Generative Modeling for Sparse Graphs","created":"2020-06-28","cited_by_count":65},{"id":"1805.08490","title":"Generative Code Modeling with Graphs","created":"2018-05-22","cited_by_count":60},{"id":"2302.03596","title":"Graph Generation with Diffusion Mixture","created":"2023-02-07","cited_by_count":55},{"id":"2102.08786","title":"Walking Out of the Weisfeiler Leman Hierarchy: Graph Learning Beyond Message Passing","created":"2021-02-17","cited_by_count":47},{"id":"2201.09871","title":"On Evaluation Metrics for Graph Generative Models","created":"2022-01-24","cited_by_count":46},{"id":"2106.01098","title":"Evaluation Metrics for Graph Generative Models: Problems, Pitfalls, and Practical Solutions","created":"2021-06-02","cited_by_count":39},{"id":"2003.05189","title":"Convolutional Kernel Networks for Graph-Structured Data","created":"2020-03-11","cited_by_count":38},{"id":"2312.11529","title":"Efficient and Scalable Graph Generation through Iterative Local Expansion","created":"2023-12-14","cited_by_count":36},{"id":"2106.06189","title":"Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation","created":"2021-06-11","cited_by_count":33},{"id":"2202.13538","title":"Algorithm and System Co-design for Efficient Subgraph-based Graph Representation Learning","created":"2022-02-28","cited_by_count":28},{"id":"2402.03687","title":"Pard: Permutation-Invariant Autoregressive Diffusion for Graph Generation","created":"2024-02-06","cited_by_count":25},{"id":"2406.03386","title":"Learning Long Range Dependencies on Graphs via Random Walks","created":"2024-06-05","cited_by_count":18},{"id":"2002.00102","title":"Edge-based sequential graph generation with recurrent neural networks","created":"2020-01-31","cited_by_count":16},{"id":"2301.10857","title":"Improving Graph Generation by Restricting Graph Bandwidth","created":"2023-01-25","cited_by_count":16},{"id":"2312.02230","title":"A Simple and Scalable Representation for Graph Generation","created":"2023-12-04","cited_by_count":16},{"id":"2407.01214","title":"Revisiting Random Walks for Learning on Graphs","created":"2024-07-01","cited_by_count":13},{"id":"2305.19337","title":"HiGen: Hierarchical Graph Generative Networks","created":"2023-05-30","cited_by_count":6}],"next_cursor":null}