{"list":"other","total":144,"rows":[{"id":"1810.04805","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","created":"2018-10-11","cited_by_count":40399},{"id":"2005.14165","title":"Language Models are Few-Shot Learners","created":"2020-05-28","cited_by_count":32419},{"id":"2006.11239","title":"Denoising Diffusion Probabilistic Models","created":"2020-06-19","cited_by_count":21076},{"id":"2112.10752","title":"High-Resolution Image Synthesis with Latent Diffusion Models","created":"2021-12-20","cited_by_count":18863},{"id":"2203.02155","title":"Training language models to follow instructions with human feedback","created":"2022-03-04","cited_by_count":15971},{"id":"1706.06083","title":"Towards Deep Learning Models Resistant to Adversarial Attacks","created":"2017-06-19","cited_by_count":6415},{"id":"1710.09412","title":"mixup: Beyond Empirical Risk Minimization","created":"2017-10-25","cited_by_count":5183},{"id":"1706.03741","title":"Deep reinforcement learning from human preferences","created":"2017-06-12","cited_by_count":3912},{"id":"1312.5602","title":"Playing Atari with Deep Reinforcement Learning","created":"2013-12-19","cited_by_count":3274},{"id":"1903.12261","title":"Benchmarking Neural Network Robustness to Common Corruptions and Perturbations","created":"2019-03-28","cited_by_count":2877},{"id":"1509.01626","title":"Character-level Convolutional Networks for Text Classification","created":"2015-09-04","cited_by_count":2330},{"id":"2107.13586","title":"Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing","created":"2021-07-28","cited_by_count":2234},{"id":"2206.04615","title":"Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models","created":"2022-06-09","cited_by_count":1998},{"id":"1908.09635","title":"A Survey on Bias and Fairness in Machine Learning","created":"2019-08-23","cited_by_count":1917},{"id":"1607.02533","title":"Adversarial examples in the physical world","created":"2016-07-08","cited_by_count":1769},{"id":"1812.08434","title":"Graph Neural Networks: A Review of Methods and Applications","created":"2018-12-20","cited_by_count":1576},{"id":"2012.07421","title":"WILDS: A Benchmark of in-the-Wild Distribution Shifts","created":"2020-12-14","cited_by_count":1375},{"id":"1511.04599","title":"DeepFool: a simple and accurate method to fool deep neural networks","created":"2015-11-14","cited_by_count":1318},{"id":"2012.15723","title":"Making Pre-trained Language Models Better Few-shot Learners","created":"2020-12-31","cited_by_count":1256},{"id":"2106.15282","title":"Cascaded Diffusion Models for High Fidelity Image Generation","created":"2021-05-30","cited_by_count":1163},{"id":"2107.00630","title":"Variational Diffusion Models","created":"2021-07-01","cited_by_count":1153},{"id":"1711.00399","title":"Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR","created":"2017-11-01","cited_by_count":1089},{"id":"1506.06724","title":"Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books","created":"2015-06-22","cited_by_count":1043},{"id":"1911.12543","title":"How Can We Know What Language Models Know?","created":"2019-11-28","cited_by_count":959},{"id":"2001.07676","title":"Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference","created":"2020-01-21","cited_by_count":938},{"id":"1602.02697","title":"Practical Black-Box Attacks against Machine Learning","created":"2016-02-08","cited_by_count":903},{"id":"1805.08318","title":"Self-Attention Generative Adversarial Networks","created":"2018-05-21","cited_by_count":809},{"id":"1805.09501","title":"AutoAugment: Learning Augmentation Policies from Data","created":"2018-05-24","cited_by_count":730},{"id":"1908.07125","title":"Universal Adversarial Triggers for Attacking and Analyzing NLP","created":"2019-08-20","cited_by_count":722},{"id":"2106.11520","title":"BARTScore: Evaluating Generated Text as Text Generation","created":"2021-06-22","cited_by_count":722},{"id":"1708.06131","title":"Evasion Attacks against Machine Learning at Test Time","created":"2017-08-21","cited_by_count":689},{"id":"2009.07118","title":"It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners","created":"2020-09-15","cited_by_count":669},{"id":"1708.03999","title":"ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models","created":"2017-08-14","cited_by_count":666},{"id":"1904.02868","title":"Data Shapley: Equitable Valuation of Data for Machine Learning","created":"2019-04-05","cited_by_count":629},{"id":"2009.00236","title":"A Survey of Deep Active Learning","created":"2020-08-30","cited_by_count":554},{"id":"1804.00792","title":"Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks","created":"2018-04-03","cited_by_count":519},{"id":"2108.04884","title":"Retiring Adult: New Datasets for Fair Machine Learning","created":"2021-08-10","cited_by_count":449},{"id":"2108.13624","title":"Towards Out-Of-Distribution Generalization: A Survey","created":"2021-08-31","cited_by_count":436},{"id":"2105.03075","title":"A Survey of Data Augmentation Approaches for NLP","created":"2021-05-07","cited_by_count":409},{"id":"1711.10160","title":"Snorkel: Rapid Training Data Creation with Weak Supervision","created":"2017-11-28","cited_by_count":372},{"id":"1802.03916","title":"Detecting and Correcting for Label Shift with Black Box Predictors","created":"2018-02-12","cited_by_count":366},{"id":"1611.06455","title":"Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline","created":"2016-11-20","cited_by_count":360},{"id":"2006.06882","title":"Rethinking Pre-training and Self-training","created":"2020-06-11","cited_by_count":352},{"id":"2102.09508","title":"Domain Adaptation for Medical Image Analysis: A Survey","created":"2021-02-18","cited_by_count":352},{"id":"2010.03978","title":"A Brief Review of Domain Adaptation","created":"2020-10-07","cited_by_count":288},{"id":"2002.06278","title":"Algorithmic Recourse: from Counterfactual Explanations to Interventions","created":"2020-02-14","cited_by_count":250},{"id":"1605.07723","title":"Data Programming: Creating Large Training Sets, Quickly","created":"2016-05-25","cited_by_count":246},{"id":"1909.09369","title":"FACE: Feasible and Actionable Counterfactual Explanations","created":"2019-09-20","cited_by_count":233},{"id":"2002.12478","title":"Time Series Data Augmentation for Deep Learning: A Survey","created":"2020-02-27","cited_by_count":226},{"id":"1805.12317","title":"Multiaccuracy: Black-Box Post-Processing for Fairness in Classification","created":"2018-05-31","cited_by_count":222},{"id":"2011.12945","title":"No Subclass Left Behind: Fine-Grained Robustness in Coarse-Grained Classification Problems","created":"2020-11-25","cited_by_count":209},{"id":"2004.12239","title":"MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification","created":"2020-04-25","cited_by_count":187},{"id":"2104.08826","title":"GPT3Mix: Leveraging Large-scale Language Models for Text Augmentation","created":"2021-04-18","cited_by_count":187},{"id":"2203.06486","title":"Chart-to-Text: A Large-Scale Benchmark for Chart Summarization","created":"2022-03-12","cited_by_count":169},{"id":"2004.11165","title":"Multi-Objective Counterfactual Explanations","created":"2020-04-23","cited_by_count":157},{"id":"2202.07179","title":"G-Mixup: Graph Data Augmentation for Graph Classification","created":"2022-02-15","cited_by_count":146},{"id":"1801.02385","title":"Synthetic Data Augmentation using GAN for Improved Liver Lesion Classification","created":"2018-01-08","cited_by_count":136},{"id":"2202.08235","title":"Data Augmentation for Deep Graph Learning: A Survey","created":"2022-02-16","cited_by_count":133},{"id":"2007.15951","title":"An Empirical Survey of Data Augmentation for Time Series Classification with Neural Networks","created":"2020-07-31","cited_by_count":128},{"id":"1903.09734","title":"Regularized Learning for Domain Adaptation under Label Shifts","created":"2019-03-22","cited_by_count":120},{"id":"2112.06409","title":"Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective","created":"2021-12-13","cited_by_count":116},{"id":"2006.06972","title":"Towards Deeper Graph Neural Networks with Differentiable Group Normalization","created":"2020-06-12","cited_by_count":114},{"id":"2107.02392","title":"Dirichlet Energy Constrained Learning for Deep Graph Neural Networks","created":"2021-07-06","cited_by_count":97},{"id":"2208.07204","title":"USB: A Unified Semi-supervised Learning Benchmark for Classification","created":"2022-08-12","cited_by_count":95},{"id":"2002.05786","title":"Deep Learning for Financial Applications : A Survey","created":"2020-02-09","cited_by_count":91},{"id":"2207.10062","title":"DataPerf: Benchmarks for Data-Centric AI Development","created":"2022-07-20","cited_by_count":90},{"id":"2108.00783","title":"CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms","created":"2021-08-02","cited_by_count":86},{"id":"2103.05327","title":"BERTese: Learning to Speak to BERT","created":"2021-03-09","cited_by_count":82},{"id":"2112.09238","title":"Benchmarking Differentially Private Synthetic Data Generation Algorithms","created":"2021-12-16","cited_by_count":81},{"id":"2002.12334","title":"A Distributional Framework for Data Valuation","created":"2020-02-27","cited_by_count":80},{"id":"1612.00835","title":"Scribbler: Controlling Deep Image Synthesis with Sketch and Color","created":"2016-12-02","cited_by_count":78},{"id":"1709.07150","title":"Feature Engineering for Predictive Modeling using Reinforcement Learning","created":"2017-09-21","cited_by_count":78},{"id":"1805.08125","title":"A Marketplace for Data: An Algorithmic Solution","created":"2018-05-21","cited_by_count":76},{"id":"1807.06068","title":"Automated Data Slicing for Model Validation:A Big data - AI Integration Approach","created":"2018-07-16","cited_by_count":70},{"id":"2004.00448","title":"Rethinking Data Augmentation for Image Super-resolution: A Comprehensive Analysis and a New Strategy","created":"2020-04-01","cited_by_count":66},{"id":"2106.06135","title":"DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning","created":"2021-06-11","cited_by_count":66},{"id":"2202.05433","title":"A Survey on Programmatic Weak Supervision","created":"2022-02-11","cited_by_count":66},{"id":"1906.02168","title":"Do Image Classifiers Generalize Across Time?","created":"2019-06-05","cited_by_count":59},{"id":"2109.03506","title":"Towards Natural Language Interfaces for Data Visualization: A Survey","created":"2021-09-08","cited_by_count":59},{"id":"2207.12560","title":"AMLB: an AutoML Benchmark","created":"2022-07-25","cited_by_count":55},{"id":"2012.06046","title":"Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling","created":"2020-12-11","cited_by_count":52},{"id":"1906.08387","title":"Experience Replay Optimization","created":"2019-06-19","cited_by_count":48},{"id":"2006.15097","title":"Policy-GNN: Aggregation Optimization for Graph Neural Networks","created":"2020-06-26","cited_by_count":48},{"id":"1906.02560","title":"An End-to-End Learning-based Cost Estimator","created":"2019-06-06","cited_by_count":43},{"id":"1911.12199","title":"FOCUS: Flexible Optimizable Counterfactual Explanations for Tree Ensembles","created":"2019-11-27","cited_by_count":43},{"id":"2301.04819","title":"Data-centric AI: Perspectives and Challenges","created":"2023-01-12","cited_by_count":43},{"id":"1906.00117","title":"Model Agnostic Contrastive Explanations for Structured Data","created":"2019-05-31","cited_by_count":41},{"id":"2003.11547","title":"A Survey on Trajectory Data Management, Analytics, and Learning","created":"2020-03-25","cited_by_count":41},{"id":"2009.09822","title":"TODS: An Automated Time Series Outlier Detection System","created":"2020-09-18","cited_by_count":41},{"id":"2202.02691","title":"TTS-GAN: A Transformer-based Time-Series Generative Adversarial Network","created":"2022-02-06","cited_by_count":41},{"id":"1611.08618","title":"A Benchmark and Comparison of Active Learning for Logistic Regression","created":"2016-11-25","cited_by_count":36},{"id":"2202.04187","title":"FMP: Toward Fair Graph Message Passing against Topology Bias","created":"2022-02-08","cited_by_count":35},{"id":"2206.10935","title":"A Study on the Evaluation of Generative Models","created":"2022-06-22","cited_by_count":35},{"id":"1905.07857","title":"CERTIFAI: Counterfactual Explanations for Robustness, Transparency, Interpretability, and Fairness of Artificial Intelligence models","created":"2019-05-20","cited_by_count":34},{"id":"2009.07415","title":"Meta-AAD: Active Anomaly Detection with Deep Reinforcement Learning","created":"2020-09-16","cited_by_count":34},{"id":"2011.10427","title":"Dataset Discovery in Data Lakes","created":"2020-11-20","cited_by_count":31},{"id":"2212.11854","title":"Data-Centric Artificial Intelligence","created":"2022-12-22","cited_by_count":31},{"id":"2101.08152","title":"Rank the Episodes: A Simple Approach for Exploration in Procedurally-Generated Environments","created":"2021-01-20","cited_by_count":28},{"id":"1707.06265","title":"Unsupervised Domain Adaptation for Robust Speech Recognition via Variational Autoencoder-Based Data Augmentation","created":"2017-07-19","cited_by_count":27},{"id":"2006.12804","title":"Benchmarking Learned Indexes","created":"2020-06-23","cited_by_count":27}],"next_cursor":"100"}