{"list":"other","total":67,"rows":[{"id":"1412.6572","title":"Explaining and Harnessing Adversarial Examples","created":"2014-12-20","cited_by_count":7364},{"id":"1506.02142","title":"Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning","created":"2015-06-06","cited_by_count":4448},{"id":"1612.01474","title":"Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles","created":"2016-12-05","cited_by_count":3926},{"id":"2006.11477","title":"wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations","created":"2020-06-20","cited_by_count":3905},{"id":"1903.12261","title":"Benchmarking Neural Network Robustness to Common Corruptions and Perturbations","created":"2019-03-28","cited_by_count":2877},{"id":"1804.02767","title":"YOLOv3: An Incremental Improvement","created":"2018-04-08","cited_by_count":2659},{"id":"1610.02136","title":"A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks","created":"2016-10-07","cited_by_count":2385},{"id":"1606.06565","title":"Concrete Problems in AI Safety","created":"2016-06-21","cited_by_count":1642},{"id":"1807.03888","title":"A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks","created":"2018-07-10","cited_by_count":1485},{"id":"1912.06670","title":"Common Voice: A Massively-Multilingual Speech Corpus","created":"2019-12-13","cited_by_count":1384},{"id":"2012.07421","title":"WILDS: A Benchmark of in-the-Wild Distribution Shifts","created":"2020-12-14","cited_by_count":1375},{"id":"1706.02690","title":"Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks","created":"2017-06-08","cited_by_count":1328},{"id":"2010.03759","title":"Energy-based Out-of-distribution Detection","created":"2020-10-08","cited_by_count":1299},{"id":"1906.02530","title":"Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift","created":"2019-06-06","cited_by_count":1287},{"id":"1904.07850","title":"Objects as Points","created":"2019-04-16","cited_by_count":1175},{"id":"2107.08430","title":"YOLOX: Exceeding YOLO Series in 2021","created":"2021-07-18","cited_by_count":1071},{"id":"1604.07316","title":"End to End Learning for Self-Driving Cars","created":"2016-04-25","cited_by_count":1009},{"id":"1801.09414","title":"CosFace: Large Margin Cosine Loss for Deep Face Recognition","created":"2018-01-29","cited_by_count":971},{"id":"1812.04606","title":"Deep Anomaly Detection with Outlier Exposure","created":"2018-12-11","cited_by_count":942},{"id":"1703.07402","title":"Simple Online and Realtime Tracking with a Deep Association Metric","created":"2017-03-21","cited_by_count":910},{"id":"1602.00763","title":"Simple Online and Realtime Tracking","created":"2016-02-02","cited_by_count":868},{"id":"2110.06864","title":"ByteTrack: Multi-Object Tracking by Associating Every Detection Box","created":"2021-10-13","cited_by_count":823},{"id":"2011.06225","title":"A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges","created":"2020-11-12","cited_by_count":819},{"id":"1806.01768","title":"Evidential Deep Learning to Quantify Classification Uncertainty","created":"2018-06-05","cited_by_count":732},{"id":"2107.03342","title":"A Survey of Uncertainty in Deep Neural Networks","created":"2021-07-07","cited_by_count":680},{"id":"1802.10501","title":"Predictive Uncertainty Estimation via Prior Networks","created":"2018-02-28","cited_by_count":557},{"id":"1603.00831","title":"MOT16: A Benchmark for Multi-Object Tracking","created":"2016-03-02","cited_by_count":532},{"id":"1512.02595","title":"Deep Speech 2: End-to-End Speech Recognition in English and Mandarin","created":"2015-12-08","cited_by_count":451},{"id":"2002.11297","title":"Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Data","created":"2020-02-26","cited_by_count":408},{"id":"1711.09325","title":"Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples","created":"2017-11-26","cited_by_count":389},{"id":"1412.5567","title":"Deep Speech: Scaling up end-to-end speech recognition","created":"2014-12-17","cited_by_count":354},{"id":"1901.09192","title":"SelectiveNet: A Deep Neural Network with an Integrated Reject Option","created":"2019-01-26","cited_by_count":350},{"id":"1705.06640","title":"DeepXplore: Automated Whitebox Testing of Deep Learning Systems","created":"2017-05-18","cited_by_count":311},{"id":"1807.07356","title":"Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks","created":"2018-07-19","cited_by_count":280},{"id":"1604.04562","title":"A Network-based End-to-End Trainable Task-oriented Dialogue System","created":"2016-04-15","cited_by_count":252},{"id":"1708.08559","title":"DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars","created":"2017-08-28","cited_by_count":251},{"id":"2007.12720","title":"MultiWOZ 2.2 : A Dialogue Dataset with Additional Annotation Corrections and State Tracking Baselines","created":"2020-07-10","cited_by_count":204},{"id":"1906.01620","title":"Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision","created":"2019-06-04","cited_by_count":182},{"id":"1506.02158","title":"Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference","created":"2015-06-06","cited_by_count":159},{"id":"1803.07519","title":"DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems","created":"2018-03-20","cited_by_count":147},{"id":"1508.01745","title":"Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems","created":"2015-08-07","cited_by_count":134},{"id":"1909.09116","title":"Self-Training for End-to-End Speech Recognition","created":"2019-09-19","cited_by_count":129},{"id":"1808.08444","title":"Guiding Deep Learning System Testing using Surprise Adequacy","created":"2018-08-25","cited_by_count":124},{"id":"1911.08460","title":"End-to-end ASR: from Supervised to Semi-Supervised Learning with Modern Architectures","created":"2019-11-19","cited_by_count":120},{"id":"2010.12138","title":"Rethinking the competition between detection and ReID in Multi-Object Tracking","created":"2020-10-23","cited_by_count":101},{"id":"1804.05132","title":"Towards Safe Autonomous Driving: Capture Uncertainty in the Deep Neural Network For Lidar 3D Vehicle Detection","created":"2018-04-13","cited_by_count":82},{"id":"2112.00787","title":"Provable Guarantees for Understanding Out-of-distribution Detection","created":"2021-12-01","cited_by_count":78},{"id":"1805.05206","title":"DeepMutation: Mutation Testing of Deep Learning Systems","created":"2018-05-14","cited_by_count":67},{"id":"1807.01771","title":"Direct Uncertainty Prediction for Medical Second Opinions","created":"2018-07-04","cited_by_count":61},{"id":"1703.01008","title":"End-to-End Task-Completion Neural Dialogue Systems","created":"2017-03-03","cited_by_count":52},{"id":"2002.04793","title":"ConvLab-2: An Open-Source Toolkit for Building, Evaluating, and Diagnosing Dialogue Systems","created":"2020-02-12","cited_by_count":52},{"id":"1812.10812","title":"DeepBillboard: Systematic Physical-World Testing of Autonomous Driving Systems","created":"2018-12-27","cited_by_count":50},{"id":"1901.10501","title":"Repairing without Retraining: Avoiding Disparate Impact with Counterfactual Distributions","created":"2019-01-29","cited_by_count":48},{"id":"1908.10719","title":"Guided Dialog Policy Learning: Reward Estimation for Multi-Domain Task-Oriented Dialog","created":"2019-08-28","cited_by_count":37},{"id":"2201.06494","title":"AugLy: Data Augmentations for Robustness","created":"2022-01-17","cited_by_count":34},{"id":"1904.08637","title":"ConvLab: Multi-Domain End-to-End Dialog System Platform","created":"2019-04-18","cited_by_count":29},{"id":"1801.06734","title":"End-to-end Multi-Modal Multi-Task Vehicle Control for Self-Driving Cars with Visual Perception","created":"2018-01-20","cited_by_count":27},{"id":"1904.13195","title":"Test Selection for Deep Learning Systems","created":"2019-04-30","cited_by_count":27},{"id":"1908.07235","title":"Density estimation in representation space to predict model uncertainty","created":"2019-08-20","cited_by_count":27},{"id":"2004.11573","title":"Towards Characterizing Adversarial Defects of Deep Learning Software from the Lens of Uncertainty","created":"2020-04-24","cited_by_count":26},{"id":"2012.05329","title":"Know Your Limits: Uncertainty Estimation with ReLU Classifiers Fails at Reliable OOD Detection","created":"2020-12-09","cited_by_count":26},{"id":"1811.02759","title":"Learning to Steer by Mimicking Features from Heterogeneous Auxiliary Networks","created":"2018-11-07","cited_by_count":19},{"id":"1912.05440","title":"Self-Driving Car Steering Angle Prediction Based on Image Recognition","created":"2019-12-11","cited_by_count":15},{"id":"2010.08776","title":"The NVIDIA PilotNet Experiments","created":"2020-10-17","cited_by_count":15},{"id":"1801.03458","title":"Real-to-Virtual Domain Unification for End-to-End Autonomous Driving","created":"2018-01-10","cited_by_count":10},{"id":"1708.03798","title":"Deep Steering: Learning End-to-End Driving Model from Spatial and Temporal Visual Cues","created":"2017-08-12","cited_by_count":9},{"id":"1503.00072","title":"DeepTrack: Learning Discriminative Feature Representations Online for Robust Visual Tracking","created":"2015-02-28","cited_by_count":3}],"next_cursor":null}