{"list":"other","total":52,"rows":[{"id":"1810.04805","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","created":"2018-10-11","cited_by_count":40399},{"id":"2005.14165","title":"Language Models are Few-Shot Learners","created":"2020-05-28","cited_by_count":32419},{"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":"1910.10683","title":"Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer","created":"2019-10-23","cited_by_count":14599},{"id":"1907.11692","title":"RoBERTa: A Robustly Optimized BERT Pretraining Approach","created":"2019-07-26","cited_by_count":13372},{"id":"2011.13456","title":"Score-Based Generative Modeling through Stochastic Differential Equations","created":"2020-11-26","cited_by_count":9122},{"id":"2105.05233","title":"Diffusion Models Beat GANs on Image Synthesis","created":"2021-05-11","cited_by_count":8255},{"id":"1503.03585","title":"Deep Unsupervised Learning using Nonequilibrium Thermodynamics","created":"2015-03-12","cited_by_count":7058},{"id":"1706.02216","title":"Inductive Representation Learning on Large Graphs","created":"2017-06-07","cited_by_count":6320},{"id":"1810.00826","title":"How Powerful are Graph Neural Networks?","created":"2018-10-01","cited_by_count":4393},{"id":"2208.12242","title":"DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation","created":"2022-08-25","cited_by_count":3300},{"id":"2104.07636","title":"Image Super-Resolution via Iterative Refinement","created":"2021-04-15","cited_by_count":1511},{"id":"1903.10676","title":"SciBERT: A Pretrained Language Model for Scientific Text","created":"2019-03-26","cited_by_count":1414},{"id":"2009.09761","title":"DiffWave: A Versatile Diffusion Model for Audio Synthesis","created":"2020-09-21","cited_by_count":1354},{"id":"1802.08219","title":"Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds","created":"2018-02-22","cited_by_count":746},{"id":"1802.04364","title":"Junction Tree Variational Autoencoder for Molecular Graph Generation","created":"2018-02-12","cited_by_count":652},{"id":"2108.02938","title":"ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models","created":"2021-08-06","cited_by_count":635},{"id":"2009.00713","title":"WaveGrad: Estimating Gradients for Waveform Generation","created":"2020-09-02","cited_by_count":622},{"id":"2006.10503","title":"SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks","created":"2020-06-18","cited_by_count":607},{"id":"1802.03480","title":"GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders","created":"2018-02-09","cited_by_count":383},{"id":"2001.09382","title":"GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation","created":"2020-01-26","cited_by_count":305},{"id":"1703.01925","title":"Grammar Variational Autoencoder","created":"2017-03-06","cited_by_count":285},{"id":"2206.01729","title":"Torsional Diffusion for Molecular Conformer Generation","created":"2022-06-01","cited_by_count":263},{"id":"2006.02425","title":"Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities","created":"2020-06-03","cited_by_count":250},{"id":"2002.12880","title":"Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data","created":"2020-02-25","cited_by_count":248},{"id":"2110.07728","title":"Pre-training Molecular Graph Representation with 3D Geometry","created":"2021-10-07","cited_by_count":248},{"id":"1805.09076","title":"Constrained Graph Variational Autoencoders for Molecule Design","created":"2018-05-23","cited_by_count":185},{"id":"2006.10137","title":"MoFlow: An Invertible Flow Model for Generating Molecular Graphs","created":"2020-06-17","cited_by_count":185},{"id":"2102.01189","title":"GraphDF: A Discrete Flow Model for Molecular Graph Generation","created":"2021-02-01","cited_by_count":180},{"id":"1906.00957","title":"Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules","created":"2019-06-02","cited_by_count":172},{"id":"2105.09016","title":"E(n) Equivariant Normalizing Flows","created":"2021-05-19","cited_by_count":165},{"id":"2209.05481","title":"A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Language","created":"2022-09-12","cited_by_count":131},{"id":"2310.12798","title":"MolCA: Molecular Graph-Language Modeling with Cross-Modal Projector and Uni-Modal Adapter","created":"2023-10-19","cited_by_count":128},{"id":"1905.11600","title":"GraphNVP: An Invertible Flow Model for Generating Molecular Graphs","created":"2019-05-28","cited_by_count":116},{"id":"2209.00865","title":"Diffusion-based Molecule Generation with Informative Prior Bridges","created":"2022-09-02","cited_by_count":106},{"id":"1802.08786","title":"Syntax-Directed Variational Autoencoder for Structured Data","created":"2018-02-24","cited_by_count":103},{"id":"2109.04824","title":"Inverse design of 3d molecular structures with conditional generative neural networks","created":"2021-09-10","cited_by_count":93},{"id":"1904.00314","title":"Molecular geometry prediction using a deep generative graph neural network","created":"2019-03-31","cited_by_count":91},{"id":"1905.13372","title":"MolecularRNN: Generating realistic molecular graphs with optimized properties","created":"2019-05-31","cited_by_count":82},{"id":"2209.15408","title":"Equivariant Energy-Guided SDE for Inverse Molecular Design","created":"2022-09-30","cited_by_count":73},{"id":"2112.03041","title":"Keeping it Simple: Language Models can learn Complex Molecular Distributions","created":"2021-12-06","cited_by_count":68},{"id":"2401.13923","title":"Towards 3D Molecule-Text Interpretation in Language Models","created":"2024-01-25","cited_by_count":66},{"id":"2310.14753","title":"Rethinking Tokenizer and Decoder in Masked Graph Modeling for Molecules","created":"2023-10-23","cited_by_count":49},{"id":"1805.00108","title":"Conditional molecular design with deep generative models","created":"2018-04-30","cited_by_count":44},{"id":"2405.12564","title":"ProtT3: Protein-to-Text Generation for Text-based Protein Understanding","created":"2024-05-21","cited_by_count":25},{"id":"1811.09766","title":"DEFactor: Differentiable Edge Factorization-based Probabilistic Graph Generation","created":"2018-11-24","cited_by_count":24},{"id":"2402.03781","title":"MolTC: Towards Molecular Relational Modeling In Language Models","created":"2024-02-06","cited_by_count":22},{"id":"1910.03131","title":"Generating valid Euclidean distance matrices","created":"2019-10-07","cited_by_count":19},{"id":"2010.06477","title":"3DMolNet: A Generative Network for Molecular Structures","created":"2020-10-08","cited_by_count":19},{"id":"2405.14225","title":"ReactXT: Understanding Molecular \"Reaction-ship\" via Reaction-Contextualized Molecule-Text Pretraining","created":"2024-05-23","cited_by_count":19},{"id":"2202.07476","title":"MGCVAE: Multi-objective Inverse Design via Molecular Graph Conditional Variational Autoencoder","created":"2022-02-14","cited_by_count":16}],"next_cursor":null}