{"list":"other","total":63,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"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":"2002.05709","title":"A Simple Framework for Contrastive Learning of Visual Representations","created":"2020-02-13","cited_by_count":12049},{"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":"1409.0575","title":"ImageNet Large Scale Visual Recognition Challenge","created":"2014-09-01","cited_by_count":8498},{"id":"2104.14294","title":"Emerging Properties in Self-Supervised Vision Transformers","created":"2021-04-29","cited_by_count":6968},{"id":"1706.02216","title":"Inductive Representation Learning on Large Graphs","created":"2017-06-07","cited_by_count":6320},{"id":"1703.03400","title":"Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks","created":"2017-03-09","cited_by_count":4897},{"id":"1806.07366","title":"Neural Ordinary Differential Equations","created":"2018-06-19","cited_by_count":3779},{"id":"1605.07146","title":"Wide Residual Networks","created":"2016-05-23","cited_by_count":3070},{"id":"1703.05175","title":"Prototypical Networks for Few-shot Learning","created":"2017-03-15","cited_by_count":3015},{"id":"2006.09882","title":"Unsupervised Learning of Visual Features by Contrasting Cluster Assignments","created":"2020-06-17","cited_by_count":2857},{"id":"1606.04080","title":"Matching Networks for One Shot Learning","created":"2016-06-13","cited_by_count":2255},{"id":"2001.07685","title":"FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence","created":"2020-01-21","cited_by_count":2085},{"id":"1905.02249","title":"MixMatch: A Holistic Approach to Semi-Supervised Learning","created":"2019-05-06","cited_by_count":1391},{"id":"1711.06025","title":"Learning to Compare: Relation Network for Few-Shot Learning","created":"2017-11-16","cited_by_count":1143},{"id":"1801.07606","title":"Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning","created":"2018-01-22","cited_by_count":1102},{"id":"1610.02242","title":"Temporal Ensembling for Semi-Supervised Learning","created":"2016-10-07","cited_by_count":1057},{"id":"1811.05868","title":"Pitfalls of Graph Neural Network Evaluation","created":"2018-11-14","cited_by_count":1007},{"id":"1603.09382","title":"Deep Networks with Stochastic Depth","created":"2016-03-30","cited_by_count":979},{"id":"1603.08861","title":"Revisiting Semi-Supervised Learning with Graph Embeddings","created":"2016-03-29","cited_by_count":976},{"id":"1704.03976","title":"Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning","created":"2017-04-13","cited_by_count":920},{"id":"2007.02133","title":"Simple and Deep Graph Convolutional Networks","created":"2020-07-04","cited_by_count":860},{"id":"1904.04232","title":"A Closer Look at Few-shot Classification","created":"2019-04-08","cited_by_count":684},{"id":"1707.02568","title":"Solving high-dimensional partial differential equations using deep learning","created":"2017-07-09","cited_by_count":556},{"id":"1807.05960","title":"Meta-Learning with Latent Embedding Optimization","created":"2018-07-16","cited_by_count":476},{"id":"1904.05046","title":"Generalizing from a Few Examples: A Survey on Few-Shot Learning","created":"2019-04-10","cited_by_count":463},{"id":"1904.03758","title":"Meta-Learning with Differentiable Convex Optimization","created":"2019-04-07","cited_by_count":461},{"id":"1911.05485","title":"Diffusion Improves Graph Learning","created":"2019-10-28","cited_by_count":414},{"id":"1805.10123","title":"TADAM: Task dependent adaptive metric for improved few-shot learning","created":"2018-05-23","cited_by_count":408},{"id":"1804.09458","title":"Dynamic Few-Shot Visual Learning without Forgetting","created":"2018-04-25","cited_by_count":385},{"id":"1705.03341","title":"Stable Architectures for Deep Neural Networks","created":"2017-05-09","cited_by_count":341},{"id":"1711.04043","title":"Few-Shot Learning with Graph Neural Networks","created":"2017-11-10","cited_by_count":324},{"id":"1909.02729","title":"A Baseline for Few-Shot Image Classification","created":"2019-09-06","cited_by_count":298},{"id":"1812.03664","title":"Few-Shot Learning via Embedding Adaptation with Set-to-Set Functions","created":"2018-12-10","cited_by_count":294},{"id":"1511.02136","title":"Diffusion-Convolutional Neural Networks","created":"2015-11-06","cited_by_count":268},{"id":"1808.01204","title":"Learning Overparameterized Neural Networks via Stochastic Gradient Descent on Structured Data","created":"2018-08-03","cited_by_count":261},{"id":"1412.4864","title":"Learning with Pseudo-Ensembles","created":"2014-12-16","cited_by_count":241},{"id":"1910.07677","title":"Cross Attention Network for Few-shot Classification","created":"2019-10-17","cited_by_count":219},{"id":"1605.07648","title":"FractalNet: Ultra-Deep Neural Networks without Residuals","created":"2016-05-24","cited_by_count":216},{"id":"1805.10002","title":"Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning","created":"2018-05-25","cited_by_count":211},{"id":"1710.10121","title":"Beyond Finite Layer Neural Networks: Bridging Deep Architectures and Numerical Differential Equations","created":"2017-10-27","cited_by_count":203},{"id":"1911.04623","title":"SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning","created":"2019-11-12","cited_by_count":195},{"id":"2101.06395","title":"Free Lunch for Few-shot Learning: Distribution Calibration","created":"2021-01-16","cited_by_count":165},{"id":"1911.07532","title":"Graph Neural Ordinary Differential Equations","created":"2019-11-18","cited_by_count":154},{"id":"1906.05186","title":"Boosting Few-Shot Visual Learning with Self-Supervision","created":"2019-06-12","cited_by_count":140},{"id":"1706.03466","title":"Few-Shot Image Recognition by Predicting Parameters from Activations","created":"2017-06-12","cited_by_count":122},{"id":"1912.00967","title":"Continuous Graph Neural Networks","created":"2019-12-02","cited_by_count":114},{"id":"1911.10713","title":"Prototype Rectification for Few-Shot Learning","created":"2019-11-25","cited_by_count":97},{"id":"2006.15486","title":"Laplacian Regularized Few-Shot Learning","created":"2020-06-28","cited_by_count":92},{"id":"1705.07485","title":"Shake-Shake regularization","created":"2017-05-21","cited_by_count":87},{"id":"2003.04151","title":"Embedding Propagation: Smoother Manifold for Few-Shot Classification","created":"2020-03-09","cited_by_count":80},{"id":"1710.09513","title":"Maximum Principle Based Algorithms for Deep Learning","created":"2017-10-26","cited_by_count":78},{"id":"2006.03806","title":"Leveraging the Feature Distribution in Transfer-based Few-Shot Learning","created":"2020-06-06","cited_by_count":74},{"id":"1905.01102","title":"Generating Classification Weights with GNN Denoising Autoencoders for Few-Shot Learning","created":"2019-05-03","cited_by_count":69},{"id":"1910.02224","title":"Transductive Episodic-Wise Adaptive Metric for Few-Shot Learning","created":"2019-10-05","cited_by_count":67},{"id":"1611.05725","title":"PolyNet: A Pursuit of Structural Diversity in Very Deep Networks","created":"2016-11-17","cited_by_count":39},{"id":"1803.01299","title":"An Optimal Control Approach to Deep Learning and Applications to Discrete-Weight Neural Networks","created":"2018-03-04","cited_by_count":26},{"id":"1811.10745","title":"ResNets Ensemble via the Feynman-Kac Formalism to Improve Natural and Robust Accuracies","created":"2018-11-26","cited_by_count":18},{"id":"1805.08015","title":"DifNet: Semantic Segmentation by Diffusion Networks","created":"2018-05-21","cited_by_count":11},{"id":"2003.10339","title":"Diffusion-based Deep Active Learning","created":"2020-03-23","cited_by_count":10},{"id":"2006.05749","title":"Interpolation between Residual and Non-Residual Networks","created":"2020-06-10","cited_by_count":6}],"next_cursor":null}