{"list":"other","total":99,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"1412.6980","title":"Adam: A Method for Stochastic Optimization","created":"2014-12-22","cited_by_count":37240},{"id":"2005.14165","title":"Language Models are Few-Shot Learners","created":"2020-05-28","cited_by_count":32419},{"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":"2103.14030","title":"Swin Transformer: Hierarchical Vision Transformer using Shifted Windows","created":"2021-03-25","cited_by_count":12139},{"id":"1609.02907","title":"Semi-Supervised Classification with Graph Convolutional Networks","created":"2016-09-09","cited_by_count":11361},{"id":"1710.10903","title":"Graph Attention Networks","created":"2017-10-30","cited_by_count":8923},{"id":"1706.02216","title":"Inductive Representation Learning on Large Graphs","created":"2017-06-07","cited_by_count":6320},{"id":"1607.06450","title":"Layer Normalization","created":"2016-07-21","cited_by_count":5669},{"id":"1409.0473","title":"Neural Machine Translation by Jointly Learning to Align and Translate","created":"2014-09-01","cited_by_count":4548},{"id":"1810.00826","title":"How Powerful are Graph Neural Networks?","created":"2018-10-01","cited_by_count":4393},{"id":"2205.14135","title":"FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness","created":"2022-05-27","cited_by_count":3673},{"id":"1704.01212","title":"Neural Message Passing for Quantum Chemistry","created":"2017-04-04","cited_by_count":3487},{"id":"2004.05150","title":"Longformer: The Long-Document Transformer","created":"2020-04-10","cited_by_count":3413},{"id":"1409.3215","title":"Sequence to Sequence Learning with Neural Networks","created":"2014-09-10","cited_by_count":2986},{"id":"1901.00596","title":"A Comprehensive Survey on Graph Neural Networks","created":"2019-01-03","cited_by_count":2919},{"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":"1901.02860","title":"Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context","created":"2019-01-09","cited_by_count":1850},{"id":"2002.05202","title":"GLU Variants Improve Transformer","created":"2020-02-12","cited_by_count":1804},{"id":"1812.08434","title":"Graph Neural Networks: A Review of Methods and Applications","created":"2018-12-20","cited_by_count":1576},{"id":"1902.07153","title":"Simplifying Graph Convolutional Networks","created":"2019-02-19","cited_by_count":1477},{"id":"2105.01601","title":"MLP-Mixer: An all-MLP Architecture for Vision","created":"2021-05-04","cited_by_count":1446},{"id":"1806.01261","title":"Relational inductive biases, deep learning, and graph networks","created":"2018-06-04","cited_by_count":1360},{"id":"1709.04875","title":"Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting","created":"2017-09-14","cited_by_count":1136},{"id":"1611.08097","title":"Geometric deep learning: going beyond Euclidean data","created":"2016-11-24","cited_by_count":1048},{"id":"1810.02244","title":"Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks","created":"2018-10-04","cited_by_count":881},{"id":"2002.09405","title":"Learning to Simulate Complex Physics with Graph Networks","created":"2020-02-21","cited_by_count":880},{"id":"2010.03409","title":"Learning Mesh-Based Simulation with Graph Networks","created":"2020-10-07","cited_by_count":868},{"id":"1802.09691","title":"Link Prediction Based on Graph Neural Networks","created":"2018-02-27","cited_by_count":832},{"id":"1906.00121","title":"Graph WaveNet for Deep Spatial-Temporal Graph Modeling","created":"2019-05-31","cited_by_count":829},{"id":"2305.13048","title":"RWKV: Reinventing RNNs for the Transformer Era","created":"2023-05-22","cited_by_count":827},{"id":"1912.09363","title":"Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting","created":"2019-12-19","cited_by_count":761},{"id":"1911.05507","title":"Compressive Transformers for Long-Range Sequence Modelling","created":"2019-11-13","cited_by_count":726},{"id":"2006.10637","title":"Temporal Graph Networks for Deep Learning on Dynamic Graphs","created":"2020-06-18","cited_by_count":530},{"id":"2007.02842","title":"Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting","created":"2020-07-06","cited_by_count":530},{"id":"1611.08402","title":"Geometric deep learning on graphs and manifolds using mixture model CNNs","created":"2016-11-25","cited_by_count":468},{"id":"2002.07962","title":"Inductive Representation Learning on Temporal Graphs","created":"2020-02-19","cited_by_count":462},{"id":"1709.05584","title":"Representation Learning on Graphs: Methods and Applications","created":"2017-09-17","cited_by_count":457},{"id":"1908.01207","title":"Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks","created":"2019-08-03","cited_by_count":448},{"id":"1905.00067","title":"MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing","created":"2019-04-30","cited_by_count":443},{"id":"2102.04906","title":"Dynamic Neural Networks: A Survey","created":"2021-02-09","cited_by_count":408},{"id":"1812.04202","title":"Deep Learning on Graphs: A Survey","created":"2018-12-11","cited_by_count":393},{"id":"1605.07736","title":"Learning Multiagent Communication with Backpropagation","created":"2016-05-25","cited_by_count":366},{"id":"1711.07553","title":"Residual Gated Graph ConvNets","created":"2017-11-20","cited_by_count":346},{"id":"2004.11198","title":"SIGN: Scalable Inception Graph Neural Networks","created":"2020-04-23","cited_by_count":284},{"id":"1905.11485","title":"Representation Learning for Dynamic Graphs: A Survey","created":"2019-05-27","cited_by_count":265},{"id":"2106.12575","title":"Weisfeiler and Lehman Go Cellular: CW Networks","created":"2021-06-23","cited_by_count":218},{"id":"2101.05974","title":"Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks","created":"2021-01-15","cited_by_count":193},{"id":"2303.13047","title":"Towards Better Dynamic Graph Learning: New Architecture and Unified Library","created":"2023-03-23","cited_by_count":157},{"id":"2005.03675","title":"Machine Learning on Graphs: A Model and Comprehensive Taxonomy","created":"2020-05-07","cited_by_count":156},{"id":"2302.11636","title":"Do We Really Need Complicated Model Architectures For Temporal Networks?","created":"2023-02-22","cited_by_count":143},{"id":"2006.01764","title":"Networks beyond pairwise interactions: structure and dynamics","created":"2020-06-02","cited_by_count":130},{"id":"1506.02075","title":"Large-scale Simple Question Answering with Memory Networks","created":"2015-06-05","cited_by_count":127},{"id":"2005.07496","title":"Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey","created":"2020-05-13","cited_by_count":124},{"id":"2207.10128","title":"Towards Better Evaluation for Dynamic Link Prediction","created":"2022-07-20","cited_by_count":122},{"id":"2206.11140","title":"Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries","created":"2022-06-22","cited_by_count":112},{"id":"1910.10781","title":"Hierarchical Transformers for Long Document Classification","created":"2019-10-23","cited_by_count":104},{"id":"1803.03735","title":"Attention-based Graph Neural Network for Semi-supervised Learning","created":"2018-03-10","cited_by_count":101},{"id":"2105.12723","title":"Nested Hierarchical Transformer: Towards Accurate, Data-Efficient and Interpretable Visual Understanding","created":"2021-05-26","cited_by_count":97},{"id":"1802.06916","title":"Simplicial Closure and higher-order link prediction","created":"2018-02-20","cited_by_count":93},{"id":"1810.10627","title":"Streaming Graph Neural Networks","created":"2018-10-24","cited_by_count":91},{"id":"2011.11545","title":"APAN: Asynchronous Propagation Attention Network for Real-time Temporal Graph Embedding","created":"2020-11-23","cited_by_count":78},{"id":"1902.09362","title":"Session-based Social Recommendation via Dynamic Graph Attention Networks","created":"2019-02-25","cited_by_count":76},{"id":"2001.02514","title":"HyGCN: A GCN Accelerator with Hybrid Architecture","created":"2020-01-07","cited_by_count":74},{"id":"2108.00202","title":"HiFT: Hierarchical Feature Transformer for Aerial Tracking","created":"2021-07-31","cited_by_count":72},{"id":"2108.06625","title":"Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer","created":"2021-08-14","cited_by_count":69},{"id":"2105.07944","title":"TCL: Transformer-based Dynamic Graph Modelling via Contrastive Learning","created":"2021-05-17","cited_by_count":68},{"id":"2104.11329","title":"What are higher-order networks?","created":"2021-04-20","cited_by_count":66},{"id":"2011.03880","title":"Learning Continuous System Dynamics from Irregularly-Sampled Partial Observations","created":"2020-11-08","cited_by_count":61},{"id":"2108.09084","title":"Fastformer: Additive Attention Can Be All You Need","created":"2021-08-20","cited_by_count":61},{"id":"2101.01229","title":"A Survey on Embedding Dynamic Graphs","created":"2021-01-04","cited_by_count":60},{"id":"2104.07368","title":"Dynamic Graph Neural Networks for Sequential Recommendation","created":"2021-04-15","cited_by_count":57},{"id":"1703.03906","title":"Massive Exploration of Neural Machine Translation Architectures","created":"2017-03-11","cited_by_count":55},{"id":"2005.03300","title":"Reducing Communication in Graph Neural Network Training","created":"2020-05-07","cited_by_count":50},{"id":"1805.11921","title":"Anonymous Walk Embeddings","created":"2018-05-30","cited_by_count":46},{"id":"2203.10428","title":"PipeGCN: Efficient Full-Graph Training of Graph Convolutional Networks with Pipelined Feature Communication","created":"2022-03-20","cited_by_count":45},{"id":"1909.00155","title":"EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks","created":"2019-08-31","cited_by_count":43},{"id":"2007.13828","title":"GRIP: A Graph Neural Network Accelerator Architecture","created":"2020-07-27","cited_by_count":37},{"id":"2008.11359","title":"FeatGraph: A Flexible and Efficient Backend for Graph Neural Network Systems","created":"2020-08-26","cited_by_count":36},{"id":"2303.14617","title":"Neural Graph Reasoning: Complex Logical Query Answering Meets Graph Databases","created":"2023-03-26","cited_by_count":35},{"id":"2101.02844","title":"Dynamic Graph Collaborative Filtering","created":"2021-01-08","cited_by_count":33},{"id":"2205.09702","title":"Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis","created":"2022-05-19","cited_by_count":29},{"id":"2001.10313","title":"Evolutionary dynamics of higher-order interactions in social networks","created":"2020-01-28","cited_by_count":26},{"id":"2006.02870","title":"The why, how, and when of representations for complex systems","created":"2020-06-04","cited_by_count":23},{"id":"1910.09017","title":"Demystifying Graph Databases: Analysis and Taxonomy of Data Organization, System Designs, and Graph Queries","created":"2019-10-20","cited_by_count":22},{"id":"2106.00761","title":"Motif Prediction with Graph Neural Networks","created":"2021-05-26","cited_by_count":18},{"id":"1912.12740","title":"Practice of Streaming Processing of Dynamic Graphs: Concepts, Models, and Systems","created":"2019-12-29","cited_by_count":12},{"id":"1801.00036","title":"An introduction to Graph Data Management","created":"2017-12-29","cited_by_count":11},{"id":"2103.03653","title":"GraphMineSuite: Enabling High-Performance and Programmable Graph Mining Algorithms with Set Algebra","created":"2021-03-05","cited_by_count":10},{"id":"2106.03594","title":"Learning Combinatorial Node Labeling Algorithms","created":"2021-06-07","cited_by_count":10},{"id":"1911.04129","title":"Higher-order Weighted Graph Convolutional Networks","created":"2019-11-11","cited_by_count":8},{"id":"2208.11469","title":"ProbGraph: High-Performance and High-Accuracy Graph Mining with Probabilistic Set Representations","created":"2022-08-24","cited_by_count":7},{"id":"1606.02314","title":"NOUS: Construction and Querying of Dynamic Knowledge Graphs","created":"2016-06-07","cited_by_count":6},{"id":"1801.09303","title":"HONE: Higher-Order Network Embeddings","created":"2018-01-28","cited_by_count":6},{"id":"1904.08380","title":"Low-Latency Graph Streaming Using Compressed Purely-Functional Trees","created":"2019-04-17","cited_by_count":6},{"id":"2305.11162","title":"The Graph Database Interface: Scaling Online Transactional and Analytical Graph Workloads to Hundreds of Thousands of Cores","created":"2023-05-18","cited_by_count":5},{"id":"1703.10643","title":"Dynamic Graph Metrics: Tutorial, Toolbox, and Tale","created":"2017-03-30","cited_by_count":3}],"next_cursor":null}