{"list":"other","total":68,"rows":[{"id":"2010.11929","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","created":"2020-10-22","cited_by_count":30186},{"id":"2006.11239","title":"Denoising Diffusion Probabilistic Models","created":"2020-06-19","cited_by_count":21076},{"id":"2303.08774","title":"GPT-4 Technical Report","created":"2023-03-15","cited_by_count":18396},{"id":"2407.21783","title":"The Llama 3 Herd of Models","created":"2024-07-31","cited_by_count":14715},{"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":"2210.02747","title":"Flow Matching for Generative Modeling","created":"2022-10-06","cited_by_count":5702},{"id":"1612.00593","title":"PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation","created":"2016-12-02","cited_by_count":5248},{"id":"1711.00937","title":"Neural Discrete Representation Learning","created":"2017-11-02","cited_by_count":4712},{"id":"1706.02413","title":"PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space","created":"2017-06-07","cited_by_count":4401},{"id":"1806.07366","title":"Neural Ordinary Differential Equations","created":"2018-06-19","cited_by_count":3779},{"id":"2209.03003","title":"Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow","created":"2022-09-07","cited_by_count":3700},{"id":"2012.09841","title":"Taming Transformers for High-Resolution Image Synthesis","created":"2020-12-17","cited_by_count":3253},{"id":"1409.3215","title":"Sequence to Sequence Learning with Neural Networks","created":"2014-09-10","cited_by_count":2986},{"id":"1505.05770","title":"Variational Inference with Normalizing Flows","created":"2015-05-21","cited_by_count":2170},{"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":"2011.00931","title":"Point Transformer","created":"2020-11-02","cited_by_count":1470},{"id":"1703.06114","title":"Deep Sets","created":"2017-03-10","cited_by_count":1421},{"id":"2202.04200","title":"MaskGIT: Masked Generative Image Transformer","created":"2022-02-08","cited_by_count":1099},{"id":"2404.02905","title":"Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction","created":"2024-04-03","cited_by_count":1023},{"id":"2405.09818","title":"Chameleon: Mixed-Modal Early-Fusion Foundation Models","created":"2024-05-16","cited_by_count":927},{"id":"2103.01458","title":"Diffusion Probabilistic Models for 3D Point Cloud Generation","created":"2021-03-02","cited_by_count":735},{"id":"2203.01941","title":"Autoregressive Image Generation using Residual Quantization","created":"2022-03-03","cited_by_count":722},{"id":"2406.11838","title":"Autoregressive Image Generation without Vector Quantization","created":"2024-06-17","cited_by_count":651},{"id":"1810.01367","title":"FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models","created":"2018-10-02","cited_by_count":594},{"id":"2312.10035","title":"Point Transformer V3: Simpler, Faster, Stronger","created":"2023-12-15","cited_by_count":589},{"id":"2210.06978","title":"LION: Latent Point Diffusion Models for 3D Shape Generation","created":"2022-10-12","cited_by_count":553},{"id":"2305.11000","title":"SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities","created":"2023-05-18","cited_by_count":529},{"id":"2104.03670","title":"3D Shape Generation and Completion through Point-Voxel Diffusion","created":"2021-04-08","cited_by_count":516},{"id":"1906.12320","title":"PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flows","created":"2019-06-28","cited_by_count":488},{"id":"2408.11039","title":"Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model","created":"2024-08-20","cited_by_count":483},{"id":"2206.04670","title":"PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies","created":"2022-06-09","cited_by_count":436},{"id":"1802.08773","title":"GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models","created":"2018-02-24","cited_by_count":413},{"id":"2210.05666","title":"Point Transformer V2: Grouped Vector Attention and Partition-based Pooling","created":"2022-10-11","cited_by_count":388},{"id":"1907.03739","title":"Point-Voxel CNN for Efficient 3D Deep Learning","created":"2019-07-08","cited_by_count":343},{"id":"2001.09382","title":"GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation","created":"2020-01-26","cited_by_count":305},{"id":"1610.06545","title":"Revisiting Classifier Two-Sample Tests","created":"2016-10-20","cited_by_count":268},{"id":"2008.06520","title":"Learning Gradient Fields for Shape Generation","created":"2020-08-14","cited_by_count":245},{"id":"1811.02633","title":"Molecular Transformer - A Model for Uncertainty-Calibrated Chemical Reaction Prediction","created":"2018-11-06","cited_by_count":229},{"id":"1801.06761","title":"PU-Net: Point Cloud Upsampling Network","created":"2018-01-21","cited_by_count":209},{"id":"2203.09516","title":"AutoSDF: Shape Priors for 3D Completion, Reconstruction and Generation","created":"2022-03-17","cited_by_count":208},{"id":"1906.02634","title":"Scaling Autoregressive Video Models","created":"2019-06-06","cited_by_count":190},{"id":"1905.06292","title":"3D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutions","created":"2019-05-15","cited_by_count":173},{"id":"1907.10844","title":"PU-GAN: a Point Cloud Upsampling Adversarial Network","created":"2019-07-25","cited_by_count":164},{"id":"2305.11487","title":"PointGPT: Auto-regressively Generative Pre-training from Point Clouds","created":"2023-05-19","cited_by_count":115},{"id":"2307.01831","title":"DiT-3D: Exploring Plain Diffusion Transformers for 3D Shape Generation","created":"2023-07-04","cited_by_count":110},{"id":"2201.10326","title":"ShapeFormer: Transformer-based Shape Completion via Sparse Representation","created":"2022-01-25","cited_by_count":106},{"id":"1810.05591","title":"PointGrow: Autoregressively Learned Point Cloud Generation with Self-Attention","created":"2018-10-12","cited_by_count":105},{"id":"1912.03264","title":"PU-GCN: Point Cloud Upsampling using Graph Convolutional Networks","created":"2019-11-30","cited_by_count":101},{"id":"2006.04604","title":"SoftFlow: Probabilistic Framework for Normalizing Flow on Manifolds","created":"2020-06-08","cited_by_count":98},{"id":"2007.10170","title":"Discrete Point Flow Networks for Efficient Point Cloud Generation","created":"2020-07-20","cited_by_count":87},{"id":"2103.15619","title":"SetVAE: Learning Hierarchical Composition for Generative Modeling of Set-Structured Data","created":"2021-03-29","cited_by_count":78},{"id":"2106.04779","title":"Point Cloud Upsampling via Disentangled Refinement","created":"2021-06-09","cited_by_count":74},{"id":"2002.10277","title":"PUGeo-Net: A Geometry-centric Network for 3D Point Cloud Upsampling","created":"2020-02-24","cited_by_count":69},{"id":"2212.01747","title":"Fast Point Cloud Generation with Straight Flows","created":"2022-12-04","cited_by_count":56},{"id":"2112.04148","title":"Neural Points: Point Cloud Representation with Neural Fields for Arbitrary Upsampling","created":"2021-12-08","cited_by_count":48},{"id":"2209.14500","title":"Bidirectional Language Models Are Also Few-shot Learners","created":"2022-09-29","cited_by_count":48},{"id":"2007.05361","title":"Progressive Point Cloud Deconvolution Generation Network","created":"2020-07-10","cited_by_count":46},{"id":"2304.11846","title":"Grad-PU: Arbitrary-Scale Point Cloud Upsampling via Gradient Descent with Learned Distance Functions","created":"2023-04-24","cited_by_count":43},{"id":"2411.16856","title":"SAR3D: Autoregressive 3D Object Generation and Understanding via Multi-scale 3D VQVAE","created":"2024-11-25","cited_by_count":39},{"id":"2204.01955","title":"Autoregressive 3D Shape Generation via Canonical Mapping","created":"2022-04-05","cited_by_count":37},{"id":"2111.12242","title":"PU-Transformer: Point Cloud Upsampling Transformer","created":"2021-11-24","cited_by_count":32},{"id":"2312.02719","title":"A Conditional Denoising Diffusion Probabilistic Model for Point Cloud Upsampling","created":"2023-12-03","cited_by_count":28},{"id":"2011.12745","title":"Deep Magnification-Flexible Upsampling over 3D Point Clouds","created":"2020-11-25","cited_by_count":27},{"id":"2306.02207","title":"SpeechGen: Unlocking the Generative Power of Speech Language Models with Prompts","created":"2023-06-03","cited_by_count":21},{"id":"2203.00914","title":"PUFA-GAN: A Frequency-Aware Generative Adversarial Network for 3D Point Cloud Upsampling","created":"2022-03-02","cited_by_count":18},{"id":"2204.10750","title":"PU-EVA: An Edge Vector based Approximation Solution for Flexible-scale Point Cloud Upsampling","created":"2022-04-22","cited_by_count":16},{"id":"2210.03942","title":"Point Cloud Upsampling via Cascaded Refinement Network","created":"2022-10-08","cited_by_count":14}],"next_cursor":null}