{"list":"other","total":93,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"1609.02907","title":"Semi-Supervised Classification with Graph Convolutional Networks","created":"2016-09-09","cited_by_count":11361},{"id":"1706.02216","title":"Inductive Representation Learning on Large Graphs","created":"2017-06-07","cited_by_count":6320},{"id":"1812.04948","title":"A Style-Based Generator Architecture for Generative Adversarial Networks","created":"2018-12-12","cited_by_count":6013},{"id":"1612.00593","title":"PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation","created":"2016-12-02","cited_by_count":5248},{"id":"1506.02142","title":"Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning","created":"2015-06-06","cited_by_count":4448},{"id":"1810.00826","title":"How Powerful are Graph Neural Networks?","created":"2018-10-01","cited_by_count":4393},{"id":"1612.01474","title":"Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles","created":"2016-12-05","cited_by_count":3926},{"id":"1704.01212","title":"Neural Message Passing for Quantum Chemistry","created":"2017-04-04","cited_by_count":3487},{"id":"1710.10196","title":"Progressive Growing of GANs for Improved Quality, Stability, and Variation","created":"2017-10-27","cited_by_count":2988},{"id":"1901.00596","title":"A Comprehensive Survey on Graph Neural Networks","created":"2019-01-03","cited_by_count":2919},{"id":"1702.08734","title":"Billion-scale similarity search with GPUs","created":"2017-02-28","cited_by_count":2497},{"id":"1903.02428","title":"Fast Graph Representation Learning with PyTorch Geometric","created":"2019-03-06","cited_by_count":2434},{"id":"1801.07829","title":"Dynamic Graph CNN for Learning on Point Clouds","created":"2018-01-24","cited_by_count":2223},{"id":"2005.00687","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","created":"2020-05-02","cited_by_count":2163},{"id":"1812.08434","title":"Graph Neural Networks: A Review of Methods and Applications","created":"2018-12-20","cited_by_count":1576},{"id":"1703.06114","title":"Deep Sets","created":"2017-03-10","cited_by_count":1421},{"id":"1806.01261","title":"Relational inductive biases, deep learning, and graph networks","created":"2018-06-04","cited_by_count":1360},{"id":"2104.13478","title":"Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges","created":"2021-04-27","cited_by_count":1167},{"id":"1312.6203","title":"Spectral Networks and Locally Connected Networks on Graphs","created":"2013-12-21","cited_by_count":1102},{"id":"1705.09406","title":"Multimodal Machine Learning: A Survey and Taxonomy","created":"2017-05-26","cited_by_count":1013},{"id":"1511.05493","title":"Gated Graph Sequence Neural Networks","created":"2015-11-17","cited_by_count":796},{"id":"1806.08804","title":"Hierarchical Graph Representation Learning with Differentiable Pooling","created":"2018-06-22","cited_by_count":787},{"id":"2007.08501","title":"Accelerating 3D Deep Learning with PyTorch3D","created":"2020-07-16","cited_by_count":784},{"id":"1806.01768","title":"Evidential Deep Learning to Quantify Classification Uncertainty","created":"2018-06-05","cited_by_count":732},{"id":"1704.04760","title":"In-Datacenter Performance Analysis of a Tensor Processing Unit","created":"2017-04-16","cited_by_count":618},{"id":"2009.07896","title":"Captum: A unified and generic model interpretability library for PyTorch","created":"2020-09-16","cited_by_count":547},{"id":"2006.10637","title":"Temporal Graph Networks for Deep Learning on Dynamic Graphs","created":"2020-06-18","cited_by_count":530},{"id":"1612.00222","title":"Interaction Networks for Learning about Objects, Relations and Physics","created":"2016-12-01","cited_by_count":509},{"id":"1812.04202","title":"Deep Learning on Graphs: A Survey","created":"2018-12-11","cited_by_count":393},{"id":"1712.04621","title":"The Effectiveness of Data Augmentation in Image Classification using Deep Learning","created":"2017-12-13","cited_by_count":370},{"id":"2006.11287","title":"Discovering Symbolic Models from Deep Learning with Inductive Biases","created":"2020-06-19","cited_by_count":344},{"id":"2003.01251","title":"Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud","created":"2020-03-02","cited_by_count":335},{"id":"2103.09430","title":"OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs","created":"2021-03-17","cited_by_count":329},{"id":"1605.05273","title":"Learning Convolutional Neural Networks for Graphs","created":"2016-05-17","cited_by_count":314},{"id":"2006.09252","title":"Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting","created":"2020-06-16","cited_by_count":313},{"id":"1511.02136","title":"Diffusion-Convolutional Neural Networks","created":"2015-11-06","cited_by_count":268},{"id":"1902.08412","title":"Adversarial Attacks on Graph Neural Networks via Meta Learning","created":"2019-02-22","cited_by_count":261},{"id":"1506.05163","title":"Deep Convolutional Networks on Graph-Structured Data","created":"2015-06-16","cited_by_count":246},{"id":"1909.01315","title":"Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks","created":"2019-09-03","cited_by_count":242},{"id":"2001.06216","title":"GraphLIME: Local Interpretable Model Explanations for Graph Neural Networks","created":"2020-01-17","cited_by_count":209},{"id":"2006.06830","title":"Data Augmentation for Graph Neural Networks","created":"2020-06-11","cited_by_count":203},{"id":"1906.04214","title":"Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective","created":"2019-06-10","cited_by_count":196},{"id":"1612.07119","title":"FINN: A Framework for Fast, Scalable Binarized Neural Network Inference","created":"2016-12-01","cited_by_count":150},{"id":"2110.04126","title":"3D Infomax improves GNNs for Molecular Property Prediction","created":"2021-10-08","cited_by_count":146},{"id":"1804.06913","title":"Fast inference of deep neural networks in FPGAs for particle physics","created":"2018-04-16","cited_by_count":133},{"id":"2102.03322","title":"CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks","created":"2021-02-05","cited_by_count":119},{"id":"2003.04078","title":"A Survey on The Expressive Power of Graph Neural Networks","created":"2020-03-09","cited_by_count":111},{"id":"1810.05165","title":"Energy Flow Networks: Deep Sets for Particle Jets","created":"2018-10-11","cited_by_count":84},{"id":"2006.10159","title":"Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors","created":"2020-06-15","cited_by_count":80},{"id":"2110.14012","title":"Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification","created":"2021-10-26","cited_by_count":80},{"id":"2102.02770","title":"A Living Review of Machine Learning for Particle Physics","created":"2021-02-02","cited_by_count":78},{"id":"2001.02514","title":"HyGCN: A GCN Accelerator with Hybrid Architecture","created":"2020-01-07","cited_by_count":74},{"id":"2103.05579","title":"hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices","created":"2021-03-09","cited_by_count":62},{"id":"2006.12138","title":"Graph Neural Networks in TensorFlow and Keras with Spektral","created":"2020-06-22","cited_by_count":60},{"id":"1904.10631","title":"Low-Memory Neural Network Training: A Technical Report","created":"2019-04-24","cited_by_count":57},{"id":"1809.04570","title":"FINN-R: An End-to-End Deep-Learning Framework for Fast Exploration of Quantized Neural Networks","created":"2018-09-12","cited_by_count":56},{"id":"2005.03300","title":"Reducing Communication in Graph Neural Network Training","created":"2020-05-07","cited_by_count":50},{"id":"2201.00491","title":"KerGNNs: Interpretable Graph Neural Networks with Graph Kernels","created":"2022-01-03","cited_by_count":50},{"id":"1807.02876","title":"Machine Learning in High Energy Physics Community White Paper","created":"2018-07-08","cited_by_count":49},{"id":"1912.03413","title":"Dissecting the Graphcore IPU Architecture via Microbenchmarking","created":"2019-12-07","cited_by_count":47},{"id":"2106.11535","title":"Particle Cloud Generation with Message Passing Generative Adversarial Networks","created":"2021-06-22","cited_by_count":46},{"id":"1902.07987","title":"Learning representations of irregular particle-detector geometry with distance-weighted graph networks","created":"2019-02-21","cited_by_count":43},{"id":"2003.06308","title":"Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4ML","created":"2020-03-11","cited_by_count":42},{"id":"2005.08225","title":"Deep Learning for Community Detection: Progress, Challenges and Opportunities","created":"2020-05-17","cited_by_count":42},{"id":"2101.05108","title":"Fast convolutional neural networks on FPGAs with hls4ml","created":"2021-01-13","cited_by_count":42},{"id":"2007.13828","title":"GRIP: A Graph Neural Network Accelerator Architecture","created":"2020-07-27","cited_by_count":37},{"id":"2101.08578","title":"MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks","created":"2021-01-21","cited_by_count":36},{"id":"2008.03601","title":"Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics","created":"2020-08-08","cited_by_count":34},{"id":"2001.02498","title":"GraphACT: Accelerating GCN Training on CPU-FPGA Heterogeneous Platforms","created":"2019-12-31","cited_by_count":31},{"id":"1910.11436","title":"Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling","created":"2019-10-24","cited_by_count":30},{"id":"2103.06995","title":"Performance of a Geometric Deep Learning Pipeline for HL-LHC Particle Tracking","created":"2021-03-11","cited_by_count":30},{"id":"1901.00121","title":"FPGA-based Accelerators of Deep Learning Networks for Learning and Classification: A Review","created":"2019-01-01","cited_by_count":29},{"id":"2002.02534","title":"Fast inference of Boosted Decision Trees in FPGAs for particle physics","created":"2020-02-05","cited_by_count":26},{"id":"2007.12374","title":"A Survey on Graph Neural Networks for Knowledge Graph Completion","created":"2020-07-24","cited_by_count":26},{"id":"2012.05716","title":"Utilising Graph Machine Learning within Drug Discovery and Development","created":"2020-12-09","cited_by_count":26},{"id":"2002.03605","title":"Object condensation: one-stage grid-free multi-object reconstruction in physics detectors, graph and image data","created":"2020-02-10","cited_by_count":24},{"id":"2012.01563","title":"Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs","created":"2020-11-30","cited_by_count":23},{"id":"2010.09206","title":"Permutationless Many-Jet Event Reconstruction with Symmetry Preserving Attention Networks","created":"2020-10-19","cited_by_count":22},{"id":"2111.12951","title":"Reliable Graph Neural Networks for Drug Discovery Under Distributional Shift","created":"2021-11-25","cited_by_count":22},{"id":"2103.16701","title":"Charged particle tracking via edge-classifying interaction networks","created":"2021-03-30","cited_by_count":19},{"id":"2112.02048","title":"Graph Neural Networks for Charged Particle Tracking on FPGAs","created":"2021-12-03","cited_by_count":17},{"id":"2003.08863","title":"Towards a Computer Vision Particle Flow","created":"2020-03-19","cited_by_count":15},{"id":"2009.10235","title":"Uncertainty-aware Attention Graph Neural Network for Defending Adversarial Attacks","created":"2020-09-22","cited_by_count":15},{"id":"2203.00330","title":"Machine Learning for Particle Flow Reconstruction at CMS","created":"2022-03-01","cited_by_count":14},{"id":"2111.00180","title":"Hierarchical Heterogeneous Graph Representation Learning for Short Text Classification","created":"2021-10-30","cited_by_count":13},{"id":"2111.12840","title":"Explaining machine-learned particle-flow reconstruction","created":"2021-11-24","cited_by_count":12},{"id":"2010.03660","title":"Fast Stencil-Code Computation on a Wafer-Scale Processor","created":"2020-10-07","cited_by_count":10},{"id":"2010.08556","title":"FPGAs-as-a-Service Toolkit (FaaST)","created":"2020-10-16","cited_by_count":10},{"id":"2007.10359","title":"GPU coprocessors as a service for deep learning inference in high energy physics","created":"2020-07-20","cited_by_count":9},{"id":"2103.06509","title":"Instance Segmentation GNNs for One-Shot Conformal Tracking at the LHC","created":"2021-03-11","cited_by_count":4},{"id":"2003.08013","title":"A Dynamic Reduction Network for Point Clouds","created":"2020-03-18","cited_by_count":1},{"id":"2108.00780","title":"Angle Based Feature Learning in GNN for 3D Object Detection using Point Cloud","created":"2021-08-02","cited_by_count":1}],"next_cursor":null}