{"list":"other","total":65,"rows":[{"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":"1405.0312","title":"Microsoft COCO: Common Objects in Context","created":"2014-05-01","cited_by_count":17608},{"id":"1409.0575","title":"ImageNet Large Scale Visual Recognition Challenge","created":"2014-09-01","cited_by_count":8498},{"id":"1703.03400","title":"Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks","created":"2017-03-09","cited_by_count":4897},{"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":"1606.04080","title":"Matching Networks for One Shot Learning","created":"2016-06-13","cited_by_count":2255},{"id":"1505.05770","title":"Variational Inference with Normalizing Flows","created":"2015-05-21","cited_by_count":2170},{"id":"1311.3618","title":"Describing Textures in the Wild","created":"2013-11-14","cited_by_count":2125},{"id":"1605.08803","title":"Density estimation using Real NVP","created":"2016-05-27","cited_by_count":2042},{"id":"1306.5151","title":"Fine-Grained Visual Classification of Aircraft","created":"2013-06-21","cited_by_count":1726},{"id":"1807.03039","title":"Glow: Generative Flow with Invertible 1x1 Convolutions","created":"2018-07-09","cited_by_count":1545},{"id":"1703.06114","title":"Deep Sets","created":"2017-03-10","cited_by_count":1421},{"id":"1812.01754","title":"Moment Matching for Multi-Source Domain Adaptation","created":"2018-12-04","cited_by_count":1331},{"id":"1912.02762","title":"Normalizing Flows for Probabilistic Modeling and Inference","created":"2019-12-05","cited_by_count":1323},{"id":"1711.06025","title":"Learning to Compare: Relation Network for Few-Shot Learning","created":"2017-11-16","cited_by_count":1143},{"id":"1410.8516","title":"NICE: Non-linear Independent Components Estimation","created":"2014-10-30","cited_by_count":1058},{"id":"2004.05439","title":"Meta-Learning in Neural Networks: A Survey","created":"2020-04-11","cited_by_count":950},{"id":"1705.07057","title":"Masked Autoregressive Flow for Density Estimation","created":"2017-05-19","cited_by_count":684},{"id":"1606.04934","title":"Improving Variational Inference with Inverse Autoregressive Flow","created":"2016-06-15","cited_by_count":640},{"id":"1606.04474","title":"Learning to learn by gradient descent by gradient descent","created":"2016-06-14","cited_by_count":620},{"id":"1909.04630","title":"Meta-Learning with Implicit Gradients","created":"2019-09-10","cited_by_count":525},{"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":"2003.11539","title":"Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need?","created":"2020-03-25","cited_by_count":437},{"id":"1805.10123","title":"TADAM: Task dependent adaptive metric for improved few-shot learning","created":"2018-05-23","cited_by_count":408},{"id":"1805.08136","title":"Meta-learning with differentiable closed-form solvers","created":"2018-05-21","cited_by_count":397},{"id":"1804.09458","title":"Dynamic Few-Shot Visual Learning without Forgetting","created":"2018-04-25","cited_by_count":385},{"id":"1707.09835","title":"Meta-SGD: Learning to Learn Quickly for Few-Shot Learning","created":"2017-07-31","cited_by_count":379},{"id":"1711.04043","title":"Few-Shot Learning with Graph Neural Networks","created":"2017-11-10","cited_by_count":324},{"id":"1903.03096","title":"Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples","created":"2019-03-07","cited_by_count":315},{"id":"1811.00002","title":"WaveGlow: A Flow-based Generative Network for Speech Synthesis","created":"2018-10-31","cited_by_count":309},{"id":"1812.02391","title":"Meta-Transfer Learning for Few-Shot Learning","created":"2018-12-06","cited_by_count":306},{"id":"1901.05761","title":"Attentive Neural Processes","created":"2019-01-17","cited_by_count":283},{"id":"1703.00837","title":"Meta Networks","created":"2017-03-02","cited_by_count":269},{"id":"1902.00275","title":"Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design","created":"2019-02-01","cited_by_count":262},{"id":"1906.02735","title":"Residual Flows for Invertible Generative Modeling","created":"2019-06-06","cited_by_count":257},{"id":"1806.02817","title":"Probabilistic Model-Agnostic Meta-Learning","created":"2018-06-07","cited_by_count":256},{"id":"1805.10002","title":"Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning","created":"2018-05-25","cited_by_count":211},{"id":"2001.08735","title":"Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation","created":"2020-01-23","cited_by_count":185},{"id":"1912.00042","title":"Learning Likelihoods with Conditional Normalizing Flows","created":"2019-11-29","cited_by_count":172},{"id":"1802.07088","title":"i-RevNet: Deep Invertible Networks","created":"2018-02-20","cited_by_count":137},{"id":"1302.4245","title":"Gaussian Process Kernels for Pattern Discovery and Extrapolation","created":"2013-02-18","cited_by_count":132},{"id":"1706.03466","title":"Few-Shot Image Recognition by Predicting Parameters from Activations","created":"2017-06-12","cited_by_count":122},{"id":"1606.02185","title":"Towards a Neural Statistician","created":"2016-06-07","cited_by_count":121},{"id":"1805.09921","title":"Meta-Learning Probabilistic Inference For Prediction","created":"2018-05-24","cited_by_count":110},{"id":"1902.04552","title":"Infinite Mixture Prototypes for Few-Shot Learning","created":"2019-02-12","cited_by_count":99},{"id":"1610.09072","title":"Orthogonal Random Features","created":"2016-10-28","cited_by_count":91},{"id":"1905.06549","title":"TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning","created":"2019-05-16","cited_by_count":91},{"id":"1302.4922","title":"Structure Discovery in Nonparametric Regression through Compositional Kernel Search","created":"2013-02-20","cited_by_count":88},{"id":"1611.06740","title":"Variational Fourier features for Gaussian processes","created":"2016-11-21","cited_by_count":85},{"id":"1803.07551","title":"Meta Reinforcement Learning with Latent Variable Gaussian Processes","created":"2018-03-20","cited_by_count":44},{"id":"1412.8293","title":"Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels","created":"2014-12-29","cited_by_count":42},{"id":"1909.11722","title":"A Theoretical Analysis of the Number of Shots in Few-Shot Learning","created":"2019-09-25","cited_by_count":39},{"id":"1807.06343","title":"Learning with SGD and Random Features","created":"2018-07-17","cited_by_count":33},{"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":"1412.6493","title":"A la Carte - Learning Fast Kernels","created":"2014-12-19","cited_by_count":18},{"id":"2006.06707","title":"Learning to Learn Kernels with Variational Random Features","created":"2020-06-11","cited_by_count":18},{"id":"1907.00971","title":"Universal audio synthesizer control with normalizing flows","created":"2019-07-01","cited_by_count":17},{"id":"2010.10341","title":"Learning to Learn Variational Semantic Memory","created":"2020-10-20","cited_by_count":17},{"id":"1902.10214","title":"Implicit Kernel Learning","created":"2019-02-26","cited_by_count":13},{"id":"1706.00326","title":"Discriminative k-shot learning using probabilistic models","created":"2017-06-01","cited_by_count":12},{"id":"1705.08525","title":"Data-driven Random Fourier Features using Stein Effect","created":"2017-05-23","cited_by_count":5},{"id":"1710.10230","title":"Not-So-Random Features","created":"2017-10-27","cited_by_count":5}],"next_cursor":null}