{"list":"other","total":71,"rows":[{"id":"1905.11946","title":"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks","created":"2019-05-28","cited_by_count":6442},{"id":"1801.04381","title":"MobileNetV2: Inverted Residuals and Linear Bottlenecks","created":"2018-01-13","cited_by_count":4795},{"id":"2012.12877","title":"Training data-efficient image transformers & distillation through attention","created":"2020-12-23","cited_by_count":4586},{"id":"2108.07258","title":"On the Opportunities and Risks of Foundation Models","created":"2021-08-16","cited_by_count":4033},{"id":"1704.04861","title":"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications","created":"2017-04-17","cited_by_count":3845},{"id":"2303.18223","title":"A Survey of Large Language Models","created":"2023-03-31","cited_by_count":2576},{"id":"1510.00149","title":"Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding","created":"2015-10-01","cited_by_count":2329},{"id":"2006.05525","title":"Knowledge Distillation: A Survey","created":"2020-06-09","cited_by_count":1603},{"id":"1610.05492","title":"Federated Learning: Strategies for Improving Communication Efficiency","created":"2016-10-18","cited_by_count":1475},{"id":"1707.01083","title":"ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices","created":"2017-07-04","cited_by_count":1200},{"id":"2005.04118","title":"Beyond Accuracy: Behavioral Testing of NLP models with CheckList","created":"2020-05-08","cited_by_count":1006},{"id":"2103.13630","title":"A Survey of Quantization Methods for Efficient Neural Network Inference","created":"2021-03-25","cited_by_count":815},{"id":"2102.00554","title":"Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks","created":"2021-01-31","cited_by_count":527},{"id":"1902.09574","title":"The State of Sparsity in Deep Neural Networks","created":"2019-02-25","cited_by_count":476},{"id":"1909.11875","title":"Federated Learning in Mobile Edge Networks: A Comprehensive Survey","created":"2019-09-26","cited_by_count":408},{"id":"2302.09419","title":"A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT","created":"2023-02-18","cited_by_count":368},{"id":"1802.05668","title":"Model compression via distillation and quantization","created":"2018-02-15","cited_by_count":289},{"id":"1812.05239","title":"Improving fairness in machine learning systems: What do industry practitioners need?","created":"2018-12-13","cited_by_count":262},{"id":"2001.02478","title":"Questioning the AI: Informing Design Practices for Explainable AI User Experiences","created":"2020-01-08","cited_by_count":253},{"id":"1905.10083","title":"Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing","created":"2019-05-24","cited_by_count":228},{"id":"2101.00288","title":"Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Models","created":"2021-01-01","cited_by_count":222},{"id":"1909.06342","title":"Explainable Machine Learning in Deployment","created":"2019-09-13","cited_by_count":211},{"id":"1907.04135","title":"The What-If Tool: Interactive Probing of Machine Learning Models","created":"2019-07-09","cited_by_count":207},{"id":"2303.15647","title":"Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning","created":"2023-03-28","cited_by_count":173},{"id":"1911.05248","title":"What Do Compressed Deep Neural Networks Forget?","created":"2019-11-13","cited_by_count":171},{"id":"2010.03058","title":"Characterising Bias in Compressed Models","created":"2020-10-06","cited_by_count":167},{"id":"2303.14189","title":"FastViT: A Fast Hybrid Vision Transformer using Structural Reparameterization","created":"2023-03-24","cited_by_count":162},{"id":"2106.08962","title":"Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and Better","created":"2021-06-16","cited_by_count":154},{"id":"2002.03231","title":"Soft Threshold Weight Reparameterization for Learnable Sparsity","created":"2020-02-08","cited_by_count":151},{"id":"2211.15583","title":"On the Effectiveness of Parameter-Efficient Fine-Tuning","created":"2022-11-28","cited_by_count":141},{"id":"1801.06889","title":"Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers","created":"2018-01-21","cited_by_count":117},{"id":"1904.05419","title":"FairVis: Visual Analytics for Discovering Intersectional Bias in Machine Learning","created":"2019-04-10","cited_by_count":93},{"id":"2306.05949","title":"Evaluating the Social Impact of Generative AI Systems in Systems and Society","created":"2023-06-09","cited_by_count":92},{"id":"1908.00080","title":"Machine Learning at the Network Edge: A Survey","created":"2019-07-31","cited_by_count":84},{"id":"1909.02309","title":"Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI","created":"2019-09-05","cited_by_count":79},{"id":"2209.00099","title":"Efficient Methods for Natural Language Processing: A Survey","created":"2022-08-31","cited_by_count":78},{"id":"2004.11440","title":"Human Factors in Model Interpretability: Industry Practices, Challenges, and Needs","created":"2020-04-23","cited_by_count":74},{"id":"2001.06684","title":"How do Data Science Workers Collaborate? Roles, Workflows, and Tools","created":"2020-01-18","cited_by_count":64},{"id":"1911.00623","title":"On-Device Machine Learning: An Algorithms and Learning Theory Perspective","created":"2019-11-02","cited_by_count":63},{"id":"2112.05675","title":"Assessing the Fairness of AI Systems: AI Practitioners' Processes, Challenges, and Needs for Support","created":"2021-12-10","cited_by_count":63},{"id":"2103.03014","title":"Lost in Pruning: The Effects of Pruning Neural Networks beyond Test Accuracy","created":"2021-03-04","cited_by_count":62},{"id":"2103.05579","title":"hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices","created":"2021-03-09","cited_by_count":62},{"id":"2004.15004","title":"CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization","created":"2020-04-30","cited_by_count":57},{"id":"2202.07105","title":"A Survey on Model Compression and Acceleration for Pretrained Language Models","created":"2022-02-15","cited_by_count":45},{"id":"1809.01587","title":"GAN Lab: Understanding Complex Deep Generative Models using Interactive Visual Experimentation","created":"2018-09-05","cited_by_count":41},{"id":"2006.12358","title":"Where Responsible AI meets Reality: Practitioner Perspectives on Enablers for shifting Organizational Practices","created":"2020-06-22","cited_by_count":39},{"id":"2207.02852","title":"Machine Learning Model Sizes and the Parameter Gap","created":"2022-07-05","cited_by_count":39},{"id":"1806.09228","title":"Deep $k$-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions","created":"2018-06-24","cited_by_count":33},{"id":"2101.05273","title":"AutoDS: Towards Human-Centered Automation of Data Science","created":"2021-01-13","cited_by_count":32},{"id":"2106.02898","title":"Dynamic Resolution Network","created":"2021-06-05","cited_by_count":31},{"id":"2108.12659","title":"DKM: Differentiable K-Means Clustering Layer for Neural Network Compression","created":"2021-08-28","cited_by_count":31},{"id":"2101.06098","title":"How AI Developers Overcome Communication Challenges in a Multidisciplinary Team: A Case Study","created":"2021-01-13","cited_by_count":29},{"id":"1708.03788","title":"Direct-Manipulation Visualization of Deep Networks","created":"2017-08-12","cited_by_count":28},{"id":"2111.01122","title":"Stakeholder Participation in AI: Beyond \"Add Diverse Stakeholders and Stir\"","created":"2021-11-01","cited_by_count":26},{"id":"2002.03389","title":"Trust in Data Science: Collaboration, Translation, and Accountability in Corporate Data Science Projects","created":"2020-02-09","cited_by_count":24},{"id":"2110.02932","title":"Machine Learning Practices Outside Big Tech: How Resource Constraints Challenge Responsible Development","created":"2021-10-06","cited_by_count":21},{"id":"2101.03970","title":"How Much Automation Does a Data Scientist Want?","created":"2021-01-07","cited_by_count":20},{"id":"2202.08946","title":"Symphony: Composing Interactive Interfaces for Machine Learning","created":"2022-02-18","cited_by_count":18},{"id":"2110.12536","title":"Neo: Generalizing Confusion Matrix Visualization to Hierarchical and Multi-Output Labels","created":"2021-10-24","cited_by_count":17},{"id":"2305.03509","title":"Diffusion Explainer: Visual Explanation for Text-to-image Stable Diffusion","created":"2023-05-04","cited_by_count":17},{"id":"2110.08350","title":"Differentiable Network Pruning for Microcontrollers","created":"2021-10-15","cited_by_count":16},{"id":"2203.15239","title":"Enabling hand gesture customization on wrist-worn devices","created":"2022-03-29","cited_by_count":15},{"id":"2211.02738","title":"Intriguing Properties of Compression on Multilingual Models","created":"2022-11-04","cited_by_count":15},{"id":"2009.10643","title":"mage: Fluid Moves Between Code and Graphical Work in Computational Notebooks","created":"2020-09-22","cited_by_count":13},{"id":"2101.10367","title":"Modeling Assumptions Clash with the Real World: Transparency, Equity, and Community Challenges for Student Assignment Algorithms","created":"2021-01-25","cited_by_count":11},{"id":"2010.11761","title":"A Comparative Analysis of Industry Human-AI Interaction Guidelines","created":"2020-10-22","cited_by_count":10},{"id":"1909.08329","title":"From Server-Based to Client-Based Machine Learning: A Comprehensive Survey","created":"2019-09-18","cited_by_count":7},{"id":"2209.15560","title":"Designing and Training of Lightweight Neural Networks on Edge Devices using Early Halting in Knowledge Distillation","created":"2022-09-30","cited_by_count":7},{"id":"1811.06471","title":"Towards Explainable Deep Learning for Credit Lending: A Case Study","created":"2018-11-15","cited_by_count":6},{"id":"1901.02144","title":"Guidelines and Benchmarks for Deployment of Deep Learning Models on Smartphones as Real-Time Apps","created":"2019-01-08","cited_by_count":4},{"id":"2012.07626","title":"On Lightweight Privacy-Preserving Collaborative Learning for Internet of Things by Independent Random Projections","created":"2020-12-11","cited_by_count":3}],"next_cursor":null}