Institution · Salesforce (United States)
Most cited papers from Salesforce (United States)
Salesforce (United States)
What are the most cited papers from Salesforce (United States)?
The most cited paper from Salesforce (United States) is "Knowing When to Look: Adaptive Attention via a Visual Sentinel for Image Captioning" (Jiasen Lu, Caiming Xiong, Devi Parikh et al., 2017), with 1,742 citations. Next come "Deep learning-enabled medical computer vision" (1,400) and "CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation" (1,396).
Showing 50 of 59 papers. Every paper with more than 110 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | Knowing When to Look: Adaptive Attention via a Visual Sentinel for Image Captioning Jiasen Lu, Caiming Xiong, Devi Parikh et al. | Jul 2017 | 1,742 |
| 2 | Deep learning-enabled medical computer vision Andre Esteva, Katherine Chou, Serena Y. Yeung et al. · npj Digital Medicine | 8 Jan 2021 | 1,400 |
| 3 | CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation Yue Wang, Weishi Wang, Shafiq Joty et al. · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing | 2021 | 1,396 |
| 4 | Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension Xiaoxuan Liu et al. · Nature Medicine | Sep 2020 | 1,301 |
| 5 | Large language models generate functional protein sequences across diverse families Ali Madani, Ben Krause, Eric R. Greene et al. · Nature Biotechnology | 26 Jan 2023 | 1,151 |
| 6 | Recent advances in deep learning for object detection Xiongwei Wu, Doyen Sahoo, Steven C. H. Hoi · Neurocomputing | 24 Jan 2020 | 1,049 |
| 7 | Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension Samantha Cruz Rivera et al. · Nature Medicine | Sep 2020 | 726 |
| 8 | Ask Me Anything: Dynamic Memory Networks for Natural Language Processing Ankit Kumar, Ozan İrsoy, Peter Ondrúška et al. · arXiv (Cornell University) | 24 Jun 2015 | 630 |
| 9 | Dynamic Memory Networks for Visual and Textual Question Answering Caiming Xiong, Stephen Merity, Richard Socher · arXiv (Cornell University) | 4 Mar 2016 | 595 |
| 10 | End-to-End Dense Video Captioning with Masked Transformer Luowei Zhou, Yingbo Zhou, Jason J. Corso et al. | Jun 2018 | 577 |
| 11 | A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks Kazuma Hashimoto, Caiming Xiong, Yoshimasa Tsuruoka et al. | 2017 | 533 |
| 12 | Prototypical Contrastive Learning of Unsupervised Representations Junnan Li, Pan Zhou, Caiming Xiong et al. · arXiv (Cornell University) | 11 May 2020 | 479 |
| 13 | ProGen2: Exploring the boundaries of protein language models Erik Nijkamp, Jeffrey A. Ruffolo, Eli N. Weinstein et al. · Cell Systems | 30 Oct 2023 | 472 |
| 14 | Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systems Chien-Sheng Wu, Andrea Madotto, Ehsan Hosseini-Asl et al. | 2019 | 424 |
| 15 | Intent Contrastive Learning for Sequential Recommendation Yongjun Chen, Zhiwei Liu, Jia Li et al. · Proceedings of the ACM Web Conference 2022 | 25 Apr 2022 | 411 |
| 16 | A comprehensive survey on pretrained foundation models: a history from BERT to ChatGPT Ce Zhou, Qian Li, Chen Li et al. · International Journal of Machine Learning and Cybernetics | 24 Nov 2024 | 404 |
| 17 | Multi-Hop Knowledge Graph Reasoning with Reward Shaping Xi Lin, Richard Socher, Caiming Xiong | 2018 | 393 |
| 18 | Dye-sensitized solar cells under ambient light powering machine learning: towards autonomous smart sensors for the internet of things Hannes Michaels, Michael Rinderle, Richard Freitag et al. · Chemical Science | 2020 | 316 |
| 19 | CoMatch: Semi-supervised Learning with Contrastive Graph Regularization Junnan Li, Caiming Xiong, Steven C. H. Hoi · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) | Oct 2021 | 278 |
| 20 | A quality assessment tool for artificial intelligence-centered diagnostic test accuracy studies: QUADAS-AI Viknesh Sounderajah et al. · Nature Medicine | Oct 2021 | 271 |
| 21 | Deep learning-enabled breast cancer hormonal receptor status determination from base-level H&E stains Nikhil Naik, Ali Madani, Andre Esteva et al. · Nature Communications | 16 Nov 2020 | 265 |
| 22 | ProGen: Language Modeling for Protein Generation Ali Madani, Bryan McCann, Nikhil Naik et al. · bioRxiv (Cold Spring Harbor Laboratory) | 8 Mar 2020 | 253 |
| 23 | ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding Le Xue, Mingfei Gao, Xing Chen et al. | Jun 2023 | 240 |
| 24 | Global-Locally Self-Attentive Encoder for Dialogue State Tracking Victor W. Zhong, Caiming Xiong, Richard Socher | 2018 | 236 |
| 25 | UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models Tianbao Xie et al. | 2022 | 233 |
| 26 | AdaFrame: Adaptive Frame Selection for Fast Video Recognition Zuxuan Wu, Caiming Xiong, Chih‐Yao Ma et al. | Jun 2019 | 229 |
| 27 | The design of the force.com multitenant internet application development platform Craig D. Weissman, Steve Bobrowski | 29 Jun 2009 | 219 |
| 28 | GeDi: Generative Discriminator Guided Sequence Generation Ben Krause, Akhilesh Deepak Gotmare, Bryan McCann et al. | 2021 | 215 |
| 29 | Prostate cancer therapy personalization via multi-modal deep learning on randomized phase III clinical trials Andre Esteva et al. · npj Digital Medicine | 8 Jun 2022 | 213 |
| 30 | TOD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialogue Chien-Sheng Wu, Steven C. H. Hoi, Richard Socher et al. | 2020 | 213 |
| 31 | RegNet: Self-Regulated Network for Image Classification Jing Xu, Yu Pan, Xinglin Pan et al. · IEEE Transactions on Neural Networks and Learning Systems | 25 Mar 2022 | 193 |
| 32 | Technology readiness levels for machine learning systems Alexander Lavin, Ciarán Lee, Alessya Visnjic et al. · Nature Communications | 20 Oct 2022 | 189 |
| 33 | Lineage and species-specific long noncoding RNAs during erythro-megakaryocytic development Vikram R. Paralkar, Tejaswini Mishra, Jing Yuan Luan et al. · Blood | 5 Feb 2014 | 188 |
| 34 | A mountable toilet system for personalized health monitoring via the analysis of excreta Seung Min Park et al. · Nature Biomedical Engineering | 6 Apr 2020 | 179 |
| 35 | The Regretful Agent: Heuristic-Aided Navigation Through Progress Estimation Chih‐Yao Ma, Zuxuan Wu, Ghassan AlRegib et al. | Jun 2019 | 175 |
| 36 | Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsing Xi Lin, Richard Socher, Caiming Xiong | 2020 | 170 |
| 37 | DE-FAKE: Detection and Attribution of Fake Images Generated by Text-to-Image Generation Models Zeyang Sha, Zheng Li, Ning Yu et al. | 15 Nov 2023 | 166 |
| 38 | A Simple Language Model for Task-Oriented Dialogue Ehsan Hosseini-Asl, Bryan McCann, Chien-Sheng Wu et al. · arXiv (Cornell University) | 2 May 2020 | 159 |
| 39 | CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases Yu Tao et al. | 2019 | 157 |
| 40 | Diffusion Model Alignment Using Direct Preference Optimization Bram Wallace, Meihua Dang, Rafael Rafailov et al. | 16 Jun 2024 | 152 |
| 41 | Efficient and Robust Question Answering from Minimal Context over Documents Sewon Min, Victor W. Zhong, Richard Socher et al. | 2018 | 148 |
| 42 | Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question Answering Akari Asai, Kazuma Hashimoto, Hannaneh Hajishirzi et al. · arXiv (Cornell University) | 24 Nov 2019 | 146 |
| 43 | Learning Cross-Modal Embeddings With Adversarial Networks for Cooking Recipes and Food Images Hao Wang, Doyen Sahoo, Chenghao Liu et al. | Jun 2019 | 141 |
| 44 | From Images to Textual Prompts: Zero-shot Visual Question Answering with Frozen Large Language Models Jiaxian Guo, Junnan Li, Dongxu Li et al. | Jun 2023 | 140 |
| 45 | Self-Monitoring Navigation Agent via Auxiliary Progress Estimation Chih‐Yao Ma, Jiasen Lu, Zuxuan Wu et al. · arXiv (Cornell University) | 10 Jan 2019 | 134 |
| 46 | Clove Naga Praveen Kumar Katta, Aditi Ghag, Mukesh M. Hira et al. | 22 Nov 2017 | 129 |
| 47 | ULIP-2: Towards Scalable Multimodal Pre-Training for 3D Understanding Le Xue, Ning Yu, Shu Zhang et al. | 16 Jun 2024 | 121 |
| 48 | EDICT: Exact Diffusion Inversion via Coupled Transformations Bram Wallace, Akash Gokul, Nikhil Naik | Jun 2023 | 120 |
| 49 | Tackling Data Heterogeneity in Federated Learning with Class Prototypes Yutong Dai, Zeyuan Chen, Junnan Li et al. · Proceedings of the AAAI Conference on Artificial Intelligence | 26 Jun 2023 | 120 |
| 50 | Art or Artifice? Large Language Models and the False Promise of Creativity Tuhin Chakrabarty, Philippe Laban, D. Agarwal et al. | 11 May 2024 | 119 |
Citations are OpenAlex's own counts, from its 2026-09-23 snapshot, so the ranking can differ from Google Scholar or Web of Science. A paper is listed for an institution when any of its authors gave it as an affiliation; retracted papers and papers whose citation count cannot be traced to citing works are left out, and a preprint and the article it became are shown once. Release dates are OpenAlex's: where a source gave only a month or a year, that is what is shown. How the lists are chosen.