{"list":"other","total":113,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"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":"1907.11692","title":"RoBERTa: A Robustly Optimized BERT Pretraining Approach","created":"2019-07-26","cited_by_count":13372},{"id":"1609.02907","title":"Semi-Supervised Classification with Graph Convolutional Networks","created":"2016-09-09","cited_by_count":11361},{"id":"1706.02216","title":"Inductive Representation Learning on Large Graphs","created":"2017-06-07","cited_by_count":6320},{"id":"1409.0473","title":"Neural Machine Translation by Jointly Learning to Align and Translate","created":"2014-09-01","cited_by_count":4548},{"id":"1910.01108","title":"DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter","created":"2019-10-02","cited_by_count":4307},{"id":"1312.6114","title":"Auto-Encoding Variational Bayes","created":"2013-12-20","cited_by_count":4053},{"id":"1310.4546","title":"Distributed Representations of Words and Phrases and their Compositionality","created":"2013-10-16","cited_by_count":3474},{"id":"1901.00596","title":"A Comprehensive Survey on Graph Neural Networks","created":"2019-01-03","cited_by_count":2919},{"id":"1802.03426","title":"UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction","created":"2018-02-09","cited_by_count":2898},{"id":"2012.07805","title":"Extracting Training Data from Large Language Models","created":"2020-12-14","cited_by_count":2189},{"id":"1607.00653","title":"node2vec: Scalable Feature Learning for Networks","created":"2016-07-03","cited_by_count":1918},{"id":"1812.08434","title":"Graph Neural Networks: A Review of Methods and Applications","created":"2018-12-20","cited_by_count":1576},{"id":"1403.6652","title":"DeepWalk: Online Learning of Social Representations","created":"2014-03-26","cited_by_count":1547},{"id":"2102.09690","title":"Calibrate Before Use: Improving Few-Shot Performance of Language Models","created":"2021-02-19","cited_by_count":1392},{"id":"1607.06520","title":"Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings","created":"2016-07-21","cited_by_count":1312},{"id":"2101.06804","title":"What Makes Good In-Context Examples for GPT-$3$?","created":"2021-01-17","cited_by_count":1195},{"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":"1902.01046","title":"Towards Federated Learning at Scale: System Design","created":"2019-02-04","cited_by_count":832},{"id":"1701.07875","title":"Wasserstein GAN","created":"2017-01-26","cited_by_count":639},{"id":"1503.03578","title":"LINE: Large-scale Information Network Embedding","created":"2015-03-12","cited_by_count":582},{"id":"1905.12616","title":"Defending Against Neural Fake News","created":"2019-05-29","cited_by_count":559},{"id":"1908.09203","title":"Release Strategies and the Social Impacts of Language Models","created":"2019-08-24","cited_by_count":513},{"id":"1809.09401","title":"Hypergraph Neural Networks","created":"2018-09-25","cited_by_count":488},{"id":"1709.05584","title":"Representation Learning on Graphs: Methods and Applications","created":"2017-09-17","cited_by_count":457},{"id":"1902.04885","title":"Federated Machine Learning: Concept and Applications","created":"2019-02-13","cited_by_count":412},{"id":"1910.12933","title":"Hyperbolic Graph Convolutional Neural Networks","created":"2019-10-28","cited_by_count":402},{"id":"1810.08473","title":"From Louvain to Leiden: guaranteeing well-connected communities","created":"2018-10-19","cited_by_count":385},{"id":"2101.05783","title":"Persistent Anti-Muslim Bias in Large Language Models","created":"2021-01-14","cited_by_count":377},{"id":"1609.05473","title":"SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient","created":"2016-09-18","cited_by_count":376},{"id":"1708.01967","title":"Fake News Detection on Social Media: A Data Mining Perspective","created":"2017-08-07","cited_by_count":336},{"id":"2005.03545","title":"MISA: Modality-Invariant and -Specific Representations for Multimodal Sentiment Analysis","created":"2020-05-07","cited_by_count":265},{"id":"1911.03437","title":"SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization","created":"2019-11-08","cited_by_count":254},{"id":"1610.09038","title":"Professor Forcing: A New Algorithm for Training Recurrent Networks","created":"2016-10-27","cited_by_count":216},{"id":"1804.03329","title":"Representation Tradeoffs for Hyperbolic Embeddings","created":"2018-04-10","cited_by_count":206},{"id":"1308.6297","title":"Crowdsourcing a Word-Emotion Association Lexicon","created":"2013-08-28","cited_by_count":196},{"id":"1606.06259","title":"MOSI: Multimodal Corpus of Sentiment Intensity and Subjectivity Analysis in Online Opinion Videos","created":"2016-06-20","cited_by_count":194},{"id":"1404.4679","title":"Graph-based Anomaly Detection and Description: A Survey","created":"2014-04-18","cited_by_count":183},{"id":"1703.01488","title":"Autoencoding Variational Inference For Topic Models","created":"2017-03-04","cited_by_count":169},{"id":"2006.03955","title":"Detecting Emergent Intersectional Biases: Contextualized Word Embeddings Contain a Distribution of Human-like Biases","created":"2020-06-06","cited_by_count":167},{"id":"2011.01314","title":"Automatic Detection of Machine Generated Text: A Critical Survey","created":"2020-11-02","cited_by_count":166},{"id":"2102.02503","title":"Understanding the Capabilities, Limitations, and Societal Impact of Large Language Models","created":"2021-02-04","cited_by_count":160},{"id":"1810.04304","title":"Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation","created":"2018-10-10","cited_by_count":149},{"id":"1904.04047","title":"Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings","created":"2019-04-03","cited_by_count":147},{"id":"1810.08810","title":"The Frontiers of Fairness in Machine Learning","created":"2018-10-20","cited_by_count":141},{"id":"1707.04638","title":"Predicting multicellular function through multi-layer tissue networks","created":"2017-07-14","cited_by_count":138},{"id":"2006.01764","title":"Networks beyond pairwise interactions: structure and dynamics","created":"2020-06-02","cited_by_count":130},{"id":"1903.09588","title":"Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence","created":"2019-03-22","cited_by_count":122},{"id":"2004.03974","title":"Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence","created":"2020-04-08","cited_by_count":121},{"id":"1511.06038","title":"Neural Variational Inference for Text Processing","created":"2015-11-19","cited_by_count":111},{"id":"2004.11648","title":"GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media","created":"2020-04-24","cited_by_count":109},{"id":"1701.06547","title":"Adversarial Learning for Neural Dialogue Generation","created":"2017-01-23","cited_by_count":103},{"id":"1703.06959","title":"CSI: A Hybrid Deep Model for Fake News Detection","created":"2017-03-20","cited_by_count":102},{"id":"1801.05852","title":"Network Representation Learning: A Survey","created":"2017-12-04","cited_by_count":101},{"id":"2008.07939","title":"FANG: Leveraging Social Context for Fake News Detection Using Graph Representation","created":"2020-08-18","cited_by_count":99},{"id":"1407.5225","title":"The Rise of Social Bots","created":"2014-07-19","cited_by_count":96},{"id":"1712.07709","title":"Beyond News Contents: The Role of Social Context for Fake News Detection","created":"2017-12-20","cited_by_count":91},{"id":"2009.06807","title":"The Radicalization Risks of GPT-3 and Advanced Neural Language Models","created":"2020-09-15","cited_by_count":76},{"id":"1908.09805","title":"The Limitations of Stylometry for Detecting Machine-Generated Fake News","created":"2019-08-26","cited_by_count":63},{"id":"1906.00742","title":"Gender-preserving Debiasing for Pre-trained Word Embeddings","created":"2019-06-03","cited_by_count":61},{"id":"1706.03850","title":"Adversarial Feature Matching for Text Generation","created":"2017-06-12","cited_by_count":58},{"id":"2103.12407","title":"Detecting Hate Speech with GPT-3","created":"2021-03-23","cited_by_count":56},{"id":"1906.03820","title":"Open-Domain Targeted Sentiment Analysis via Span-Based Extraction and Classification","created":"2019-06-10","cited_by_count":50},{"id":"1611.04051","title":"GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution","created":"2016-11-12","cited_by_count":48},{"id":"2010.12626","title":"Topic Modeling with Contextualized Word Representation Clusters","created":"2020-10-23","cited_by_count":41},{"id":"2009.02902","title":"TransModality: An End2End Fusion Method with Transformer for Multimodal Sentiment Analysis","created":"2020-09-07","cited_by_count":40},{"id":"1702.07983","title":"Maximum-Likelihood Augmented Discrete Generative Adversarial Networks","created":"2017-02-26","cited_by_count":39},{"id":"1705.10929","title":"Adversarial Generation of Natural Language","created":"2017-05-31","cited_by_count":33},{"id":"2001.03241","title":"Network Geometry","created":"2020-01-09","cited_by_count":31},{"id":"1904.11679","title":"Fake News Early Detection: An Interdisciplinary Study","created":"2019-04-26","cited_by_count":29},{"id":"1904.13341","title":"Learning Fair Representations via an Adversarial Framework","created":"2019-04-30","cited_by_count":29},{"id":"1704.05973","title":"Call Attention to Rumors: Deep Attention Based Recurrent Neural Networks for Early Rumor Detection","created":"2017-04-20","cited_by_count":25},{"id":"2010.02377","title":"Improving Neural Topic Models using Knowledge Distillation","created":"2020-10-05","cited_by_count":25},{"id":"1410.8586","title":"DeepSentiBank: Visual Sentiment Concept Classification with Deep Convolutional Neural Networks","created":"2014-10-30","cited_by_count":24},{"id":"1608.00763","title":"A Comparative Analysis of Community Detection Algorithms on Artificial Networks","created":"2016-08-02","cited_by_count":23},{"id":"1703.04837","title":"SNE: Signed Network Embedding","created":"2017-03-14","cited_by_count":20},{"id":"1605.02019","title":"Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vec","created":"2016-05-06","cited_by_count":19},{"id":"2002.00843","title":"Artificial Benchmark for Community Detection (ABCD): Fast Random Graph Model with Community Structure","created":"2020-01-14","cited_by_count":19},{"id":"1904.02670","title":"What Twitter Profile and Posted Images Reveal About Depression and Anxiety","created":"2019-04-04","cited_by_count":17},{"id":"2004.12199","title":"Local Graph Clustering with Network Lasso","created":"2020-04-25","cited_by_count":16},{"id":"2010.11731","title":"Improving BERT Performance for Aspect-Based Sentiment Analysis","created":"2020-10-22","cited_by_count":15},{"id":"1905.01976","title":"TextKD-GAN: Text Generation using KnowledgeDistillation and Generative Adversarial Networks","created":"2019-04-23","cited_by_count":14},{"id":"2004.03997","title":"Assessing the risks of \"infodemics\" in response to COVID-19 epidemics","created":"2020-04-08","cited_by_count":14},{"id":"2010.13900","title":"Incorporating Symbolic Domain Knowledge into Graph Neural Networks","created":"2020-10-23","cited_by_count":13},{"id":"2009.10557","title":"GRACE: Gradient Harmonized and Cascaded Labeling for Aspect-based Sentiment Analysis","created":"2020-09-22","cited_by_count":12},{"id":"2009.05265","title":"Community detection in networks using graph embeddings","created":"2020-09-11","cited_by_count":11},{"id":"2106.10198","title":"Systematic comparison of graph embedding methods in practical tasks","created":"2021-06-18","cited_by_count":11},{"id":"1306.0519","title":"Random Walks on Multiplex Networks","created":"2013-06-03","cited_by_count":10},{"id":"2008.04545","title":"Context Reinforced Neural Topic Modeling over Short Texts","created":"2020-08-11","cited_by_count":10},{"id":"2011.00169","title":"Understanding Pre-trained BERT for Aspect-based Sentiment Analysis","created":"2020-10-31","cited_by_count":9},{"id":"1802.05196","title":"Generative Models for Spear Phishing Posts on Social Media","created":"2018-02-14","cited_by_count":8},{"id":"2010.16407","title":"TopicBERT for Energy Efficient Document Classification","created":"2020-10-15","cited_by_count":8},{"id":"1906.04562","title":"An Unsupervised Framework for Comparing Graph Embeddings","created":"2019-05-29","cited_by_count":7},{"id":"1508.03868","title":"Visual Affect Around the World: A Large-scale Multilingual Visual Sentiment Ontology","created":"2015-08-16","cited_by_count":6},{"id":"1910.13090","title":"Hyperbolic Node Embedding for Signed Networks","created":"2019-10-29","cited_by_count":6},{"id":"2112.00075","title":"A Multi-purposed Unsupervised Framework for Comparing Embeddings of Undirected and Directed Graphs","created":"2021-11-30","cited_by_count":6},{"id":"1803.08086","title":"Influence of augmented humans in online interactions during voting events","created":"2018-03-21","cited_by_count":5},{"id":"1906.01946","title":"Automated Speech Generation from UN General Assembly Statements: Mapping Risks in AI Generated Texts","created":"2019-06-05","cited_by_count":5}],"next_cursor":"100"}