{"list":"other","total":58,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"1412.6980","title":"Adam: A Method for Stochastic Optimization","created":"2014-12-22","cited_by_count":37240},{"id":"1912.01703","title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","created":"2019-12-03","cited_by_count":20305},{"id":"1502.03167","title":"Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift","created":"2015-02-11","cited_by_count":7005},{"id":"1706.04599","title":"On Calibration of Modern Neural Networks","created":"2017-06-14","cited_by_count":4960},{"id":"1703.03400","title":"Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks","created":"2017-03-09","cited_by_count":4897},{"id":"1312.6114","title":"Auto-Encoding Variational Bayes","created":"2013-12-20","cited_by_count":4053},{"id":"1703.05175","title":"Prototypical Networks for Few-shot Learning","created":"2017-03-15","cited_by_count":3015},{"id":"1802.03426","title":"UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction","created":"2018-02-09","cited_by_count":2898},{"id":"1606.04080","title":"Matching Networks for One Shot Learning","created":"2016-06-13","cited_by_count":2255},{"id":"1711.06025","title":"Learning to Compare: Relation Network for Few-Shot Learning","created":"2017-11-16","cited_by_count":1143},{"id":"1803.02999","title":"On First-Order Meta-Learning Algorithms","created":"2018-03-08","cited_by_count":1018},{"id":"1904.04232","title":"A Closer Look at Few-shot Classification","created":"2019-04-08","cited_by_count":684},{"id":"1909.04630","title":"Meta-Learning with Implicit Gradients","created":"2019-09-10","cited_by_count":525},{"id":"1406.5298","title":"Semi-Supervised Learning with Deep Generative Models","created":"2014-06-20","cited_by_count":496},{"id":"1807.05960","title":"Meta-Learning with Latent Embedding Optimization","created":"2018-07-16","cited_by_count":476},{"id":"1904.03758","title":"Meta-Learning with Differentiable Convex Optimization","created":"2019-04-07","cited_by_count":461},{"id":"1803.00676","title":"Meta-Learning for Semi-Supervised Few-Shot Classification","created":"2018-03-02","cited_by_count":456},{"id":"1805.10123","title":"TADAM: Task dependent adaptive metric for improved few-shot learning","created":"2018-05-23","cited_by_count":408},{"id":"1711.04043","title":"Few-Shot Learning with Graph Neural Networks","created":"2017-11-10","cited_by_count":324},{"id":"1810.09502","title":"How to train your MAML","created":"2018-10-22","cited_by_count":309},{"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":"1806.02817","title":"Probabilistic Model-Agnostic Meta-Learning","created":"2018-06-07","cited_by_count":256},{"id":"1910.07677","title":"Cross Attention Network for Few-shot Classification","created":"2019-10-17","cited_by_count":219},{"id":"1805.10002","title":"Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning","created":"2018-05-25","cited_by_count":211},{"id":"1911.04623","title":"SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning","created":"2019-11-12","cited_by_count":195},{"id":"1806.03836","title":"Bayesian Model-Agnostic Meta-Learning","created":"2018-06-11","cited_by_count":194},{"id":"2001.08735","title":"Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation","created":"2020-01-23","cited_by_count":185},{"id":"1907.12087","title":"Charting the Right Manifold: Manifold Mixup for Few-shot Learning","created":"2019-07-28","cited_by_count":136},{"id":"1906.07697","title":"Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processes","created":"2019-06-18","cited_by_count":131},{"id":"1301.7375","title":"Learning by Transduction","created":"2013-01-30","cited_by_count":121},{"id":"1606.02185","title":"Towards a Neural Statistician","created":"2016-06-07","cited_by_count":121},{"id":"2012.01506","title":"Few-Shot Classification with Feature Map Reconstruction Networks","created":"2020-12-02","cited_by_count":116},{"id":"1912.03432","title":"Improved Few-Shot Visual Classification","created":"2019-12-07","cited_by_count":115},{"id":"1805.09921","title":"Meta-Learning Probabilistic Inference For Prediction","created":"2018-05-24","cited_by_count":110},{"id":"1905.11116","title":"Finding Task-Relevant Features for Few-Shot Learning by Category Traversal","created":"2019-05-27","cited_by_count":101},{"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":"1804.00104","title":"Learning Disentangled Joint Continuous and Discrete Representations","created":"2018-03-31","cited_by_count":83},{"id":"2003.04151","title":"Embedding Propagation: Smoother Manifold for Few-Shot Classification","created":"2020-03-09","cited_by_count":80},{"id":"2003.14247","title":"DPGN: Distribution Propagation Graph Network for Few-shot Learning","created":"2020-03-31","cited_by_count":79},{"id":"2006.03806","title":"Leveraging the Feature Distribution in Transfer-based Few-Shot Learning","created":"2020-06-06","cited_by_count":74},{"id":"2008.12284","title":"learn2learn: A Library for Meta-Learning Research","created":"2020-08-27","cited_by_count":69},{"id":"1912.05094","title":"Associative Alignment for Few-shot Image Classification","created":"2019-12-11","cited_by_count":58},{"id":"2009.04960","title":"Prototype Completion with Primitive Knowledge for Few-Shot Learning","created":"2020-09-10","cited_by_count":50},{"id":"2004.12696","title":"Empirical Bayes Transductive Meta-Learning with Synthetic Gradients","created":"2020-04-27","cited_by_count":48},{"id":"2101.02833","title":"Shallow Bayesian Meta Learning for Real-World Few-Shot Recognition","created":"2021-01-08","cited_by_count":48},{"id":"2109.07607","title":"Partner-Assisted Learning for Few-Shot Image Classification","created":"2021-09-15","cited_by_count":36},{"id":"2006.12245","title":"Enhancing Few-Shot Image Classification with Unlabelled Examples","created":"2020-06-17","cited_by_count":32},{"id":"2007.10417","title":"Bayesian Few-Shot Classification with One-vs-Each P\\'olya-Gamma Augmented Gaussian Processes","created":"2020-07-20","cited_by_count":32},{"id":"2206.04496","title":"Mitigating Modality Collapse in Multimodal VAEs via Impartial Optimization","created":"2022-06-09","cited_by_count":32},{"id":"2006.10102","title":"Capturing Label Characteristics in VAEs","created":"2020-06-17","cited_by_count":30},{"id":"1907.11864","title":"Uncertainty in Model-Agnostic Meta-Learning using Variational Inference","created":"2019-07-27","cited_by_count":23},{"id":"1905.09670","title":"Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation","created":"2019-05-23","cited_by_count":16},{"id":"2201.09699","title":"EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients","created":"2022-01-24","cited_by_count":16},{"id":"2008.12037","title":"Meta-Learning with Shared Amortized Variational Inference","created":"2020-08-27","cited_by_count":14},{"id":"2110.09446","title":"Squeezing Backbone Feature Distributions to the Max for Efficient Few-Shot Learning","created":"2021-10-18","cited_by_count":14}],"next_cursor":null}