{"list":"other","total":54,"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":"1409.1556","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","created":"2014-09-04","cited_by_count":15364},{"id":"1503.02531","title":"Distilling the Knowledge in a Neural Network","created":"2015-03-09","cited_by_count":10324},{"id":"1801.01290","title":"Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor","created":"2018-01-04","cited_by_count":4885},{"id":"1801.04381","title":"MobileNetV2: Inverted Residuals and Linear Bottlenecks","created":"2018-01-13","cited_by_count":4795},{"id":"1709.01507","title":"Squeeze-and-Excitation Networks","created":"2017-09-05","cited_by_count":4789},{"id":"1506.02142","title":"Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning","created":"2015-06-06","cited_by_count":4448},{"id":"1605.07146","title":"Wide Residual Networks","created":"2016-05-23","cited_by_count":3070},{"id":"2001.07685","title":"FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence","created":"2020-01-21","cited_by_count":2085},{"id":"2103.13413","title":"Vision Transformers for Dense Prediction","created":"2021-03-24","cited_by_count":1803},{"id":"1412.6550","title":"FitNets: Hints for Thin Deep Nets","created":"2014-12-19","cited_by_count":1653},{"id":"1707.01083","title":"ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices","created":"2017-07-04","cited_by_count":1200},{"id":"1807.11164","title":"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design","created":"2018-07-30","cited_by_count":1136},{"id":"1612.03928","title":"Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer","created":"2016-12-12","cited_by_count":1077},{"id":"1904.12848","title":"Unsupervised Data Augmentation for Consistency Training","created":"2019-04-29","cited_by_count":1060},{"id":"1604.07316","title":"End to End Learning for Self-Driving Cars","created":"2016-04-25","cited_by_count":1009},{"id":"2107.07511","title":"A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification","created":"2021-07-15","cited_by_count":792},{"id":"1910.10699","title":"Contrastive Representation Distillation","created":"2019-10-23","cited_by_count":748},{"id":"2110.08263","title":"FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling","created":"2021-10-15","cited_by_count":540},{"id":"1904.06019","title":"Conformal Prediction Under Covariate Shift","created":"2019-04-12","cited_by_count":519},{"id":"1805.01954","title":"Behavioral Cloning from Observation","created":"2018-05-04","cited_by_count":492},{"id":"2106.00170","title":"Adaptive Conformal Inference Under Distribution Shift","created":"2021-06-01","cited_by_count":375},{"id":"2009.14193","title":"Uncertainty Sets for Image Classifiers using Conformal Prediction","created":"2020-09-29","cited_by_count":366},{"id":"2306.13831","title":"Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks","created":"2023-06-24","cited_by_count":327},{"id":"1802.04977","title":"Paraphrasing Complex Network: Network Compression via Factor Transfer","created":"2018-02-14","cited_by_count":260},{"id":"1803.10837","title":"Learning Deep Representations with Probabilistic Knowledge Transfer","created":"2018-03-28","cited_by_count":213},{"id":"2306.08543","title":"MiniLLM: On-Policy Distillation of Large Language Models","created":"2023-06-14","cited_by_count":139},{"id":"2403.01427","title":"Logit Standardization in Knowledge Distillation","created":"2024-03-03","cited_by_count":102},{"id":"2311.02198","title":"Imitation Bootstrapped Reinforcement Learning","created":"2023-11-03","cited_by_count":64},{"id":"2106.04972","title":"Understanding Softmax Confidence and Uncertainty","created":"2021-06-09","cited_by_count":63},{"id":"1901.11228","title":"Uncertainty Quantification in Deep MRI Reconstruction","created":"2019-01-31","cited_by_count":49},{"id":"2302.09656","title":"Credal Bayesian Deep Learning","created":"2023-02-19","cited_by_count":45},{"id":"2010.09298","title":"Double-Uncertainty Weighted Method for Semi-supervised Learning","created":"2020-10-19","cited_by_count":44},{"id":"2206.06487","title":"The Modality Focusing Hypothesis: Towards Understanding Crossmodal Knowledge Distillation","created":"2022-06-13","cited_by_count":44},{"id":"2112.02250","title":"Dense Extreme Inception Network for Edge Detection","created":"2021-12-04","cited_by_count":39},{"id":"2207.02238","title":"Improving Trustworthiness of AI Disease Severity Rating in Medical Imaging with Ordinal Conformal Prediction Sets","created":"2022-07-05","cited_by_count":38},{"id":"1603.04416","title":"Criteria of efficiency for conformal prediction","created":"2016-03-14","cited_by_count":36},{"id":"2303.09830","title":"Prototype Knowledge Distillation for Medical Segmentation with Missing Modality","created":"2023-03-17","cited_by_count":24},{"id":"1902.06579","title":"Conformal calibrators","created":"2019-02-18","cited_by_count":22},{"id":"2405.05145","title":"Conformal Semantic Image Segmentation: Post-hoc Quantification of Predictive Uncertainty","created":"2024-04-16","cited_by_count":20},{"id":"2411.04852","title":"Conformalized Credal Regions for Classification with Ambiguous Ground Truth","created":"2024-11-07","cited_by_count":19},{"id":"2408.07221","title":"A Review of Pseudo-Labeling for Computer Vision","created":"2024-08-13","cited_by_count":16},{"id":"2306.00876","title":"Quantifying Deep Learning Model Uncertainty in Conformal Prediction","created":"2023-06-01","cited_by_count":13},{"id":"2308.14815","title":"Distributionally Robust Statistical Verification with Imprecise Neural Networks","created":"2023-08-28","cited_by_count":12},{"id":"2403.12876","title":"LAVA: Long-horizon Visual Action based Food Acquisition","created":"2024-03-19","cited_by_count":10},{"id":"2403.12891","title":"Adaptive Visual Imitation Learning for Robotic Assisted Feeding Across Varied Bowl Configurations and Food Types","created":"2024-03-19","cited_by_count":8},{"id":"2403.08955","title":"Towards Efficient Risk-Sensitive Policy Gradient: An Iteration Complexity Analysis","created":"2024-03-13","cited_by_count":6},{"id":"2408.04054","title":"PLANRL: A Motion Planning and Imitation Learning Framework to Bootstrap Reinforcement Learning","created":"2024-08-07","cited_by_count":6},{"id":"2303.02293","title":"Data-Driven Distributionally Robust Optimal Control with State-Dependent Noise","created":"2023-03-04","cited_by_count":5},{"id":"2305.14782","title":"IBCL: Zero-shot Model Generation under Stability-Plasticity Trade-offs","created":"2023-05-24","cited_by_count":5},{"id":"2403.08936","title":"Beyond Joint Demonstrations: Personalized Expert Guidance for Efficient Multi-Agent Reinforcement Learning","created":"2024-03-13","cited_by_count":5},{"id":"2409.12092","title":"IMRL: Integrating Visual, Physical, Temporal, and Geometric Representations for Enhanced Food Acquisition","created":"2024-09-18","cited_by_count":5},{"id":"2210.10426","title":"Pseudo-Label Noise Suppression Techniques for Semi-Supervised Semantic Segmentation","created":"2022-10-19","cited_by_count":3}],"next_cursor":null}