---
title: "Who cites Judy Hoffman — 558 papers of our library"
description: "558 papers of the Elamur Research library cite the works of Judy Hoffman, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/judy-hoffman/cited-by/"
---
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# Who cites Judy Hoffman

754 citations of their works from 558 papers of our library (of the 149,439 whose bibliographies we read) · 52 with a page here, the most cited first · [← Judy Hoffman](https://md.research.elamur.ai/free/en/authors/judy-hoffman/)

1. [**Very Deep Convolutional Networks for Large-Scale Image Recognition arXiv:1409.1556**Karen Simonyan, Andrew Zisserman*2014 · 73,393 citations*](https://md.research.elamur.ai/free/en/papers/simonyan-2014-very-deep-convolutional-networks-large/)
2. **[Rich feature hierarchies for accurate object detection and semantic segmentation](https://md.research.elamur.ai/free/en/papers/girshick-2013-rich-feature-hierarchies-accurate-object/) [IEEE Computer Society Conference on Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/venues/ieee-computer-society-conference-on-computer-vision-and-pattern-recognition/)**Ross Girshick, Jeff Donahue, Trevor Darrell, et al.*2013 · 31,930 citations*
3. [**A Simple Framework for Contrastive Learning of Visual Representations arXiv:2002.05709**Ting Chen, Simon Kornblith, Mohammad Norouzi, et al.*2020 · 8,000 citations*](https://md.research.elamur.ai/free/en/papers/chen-2020-simple-framework-contrastive-learning-visual/)
4. **[Learning Transferable Architectures for Scalable Image Recognition](https://md.research.elamur.ai/free/en/papers/zoph-2017-learning-transferable-architectures-scalable-image/) [IEEE Computer Society Conference on Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/venues/ieee-computer-society-conference-on-computer-vision-and-pattern-recognition/)**Barret Zoph, Vijay Vasudevan, Jonathon Shlens, et al.*2017 · 6,583 citations*
5. [**Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks arXiv:1703.03400**Chelsea Finn, Pieter Abbeel, Sergey Levine*2017 · 6,549 citations*](https://md.research.elamur.ai/free/en/papers/finn-2017-model-agnostic-meta-learning-fast/)
6. **[A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://md.research.elamur.ai/free/en/papers/williams-2018-broad-coverage-challenge-corpus-sentence/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Adina Williams, Nikita Nangia, Samuel R. Bowman*2018 · 5,551 citations*
7. [**Caffe: Convolutional Architecture for Fast Feature Embedding arXiv:1408.5093**Yangqing Jia, Evan Shelhamer, Jeff Donahue, et al.*2014 · 4,209 citations*](https://md.research.elamur.ai/free/en/papers/jia-2014-caffe-convolutional-architecture-fast-feature/)
8. [**DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition arXiv:1310.1531**Jeff Donahue, Yangqing Jia, Oriol Vinyals, et al.*2013 · 3,463 citations*](https://md.research.elamur.ai/free/en/papers/donahue-2013-decaf-deep-convolutional-activation-feature/)
9. [**Continual Lifelong Learning with Neural Networks: A Review arXiv:1802.07569**German I. Parisi, Ronald Kemker, Jose L. Part, et al.*2018 · 3,224 citations*](https://md.research.elamur.ai/free/en/papers/parisi-2018-continual-lifelong-learning-neural-networks/)
10. [**Understanding Black-box Predictions via Influence Functions arXiv:1703.04730**Pang Wei Koh, Percy Liang*2017 · 1,514 citations*](https://md.research.elamur.ai/free/en/papers/koh-2017-understanding-black-box-predictions-influence/)
11. [**Matching Networks for One Shot Learning arXiv:1606.04080**Oriol Vinyals, Charles Blundell, Timothy Lillicrap, et al.*2016 · 1,197 citations*](https://md.research.elamur.ai/free/en/papers/vinyals-2016-matching-networks-one-shot-learning/)
12. [**VL-BERT: Pre-training of Generic Visual-Linguistic Representations arXiv:1908.08530**Weijie Su, Xizhou Zhu, Yue Cao, et al.*2019 · 1,066 citations*](https://md.research.elamur.ai/free/en/papers/su-2019-vl-bert-pre-training-generic/)
13. [**BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain arXiv:1708.06733**Tianyu Gu, Brendan Dolan-Gavitt, Siddharth Garg*2017 · 1,024 citations*](https://md.research.elamur.ai/free/en/papers/gu-2017-badnets-identifying-vulnerabilities-machine-learning/)
14. [**Visualizing and Understanding Convolutional Networks arXiv:1311.2901**Matthew D Zeiler, Rob Fergus*2013 · 771 citations*](https://md.research.elamur.ai/free/en/papers/zeiler-2013-visualizing-understanding-convolutional-networks/)
15. [**Show and Tell: A Neural Image Caption Generator arXiv:1411.4555**Oriol Vinyals, Alexander Toshev, Samy Bengio, et al.*2014 · 729 citations*](https://md.research.elamur.ai/free/en/papers/vinyals-2014-show-tell-neural-image-caption/)
16. [**Learning multiple visual domains with residual adapters arXiv:1705.08045**Sylvestre-Alvise Rebuffi, Hakan Bilen, Andrea Vedaldi*2017 · 694 citations*](https://md.research.elamur.ai/free/en/papers/rebuffi-2017-learning-multiple-visual-domains-residual/)
17. [**A Corpus for Reasoning About Natural Language Grounded in Photographs (NLVR2)**Alane Suhr, Stephanie Zhou, Ally Zhang, et al.*2019 · 670 citations*](https://md.research.elamur.ai/free/en/papers/suhr-2019-corpus-reasoning-natural-language-grounded/)
18. [**A simple neural network module for relational reasoning arXiv:1706.01427**Adam Santoro, David Raposo, David G. T. Barrett, et al.*2017 · 632 citations*](https://md.research.elamur.ai/free/en/papers/santoro-2017-simple-neural-network-module-relational/)
19. **[Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm](https://md.research.elamur.ai/free/en/papers/felbo-2017-millions-emoji-occurrences-learn-any/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Bjarke Felbo, Alan Mislove, Anders Søgaard, et al.*2017 · 621 citations*
20. [**Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time arXiv:2203.05482**Mitchell Wortsman, Gabriel Ilharco, Samir Yitzhak Gadre, et al.*2022 · 507 citations*](https://md.research.elamur.ai/free/en/papers/wortsman-2022-model-soups-averaging-weights-multiple/)
21. [**Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding arXiv:1810.02338**Kexin Yi, Jiajun Wu, Chuang Gan, et al.*2018 · 407 citations*](https://md.research.elamur.ai/free/en/papers/yi-2018-neural-symbolic-vqa-disentangling-reasoning/)
22. **[ActivityNet-QA: A Dataset for Understanding Complex Web Videos via Question Answering](https://md.research.elamur.ai/free/en/papers/yu-2019-activitynet-qa-dataset-understanding-complex/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Zhou Yu, Dejing Xu, Jun Yu, et al.*2019 · 403 citations*
23. [**FiLM: Visual Reasoning with a General Conditioning Layer arXiv:1709.07871**Ethan Perez, Florian Strub, Harm de Vries, et al.*2017 · 400 citations*](https://md.research.elamur.ai/free/en/papers/perez-2017-film-visual-reasoning-general-conditioning/)
24. **[ViperGPT: Visual Inference via Python Execution for Reasoning](https://md.research.elamur.ai/free/en/papers/suris-2023-vipergpt-visual-inference-python-execution/) [IEEE/CVF International Conference on Computer Vision (ICCV)](https://md.research.elamur.ai/free/en/venues/ieee-cvf-international-conference-on-computer-vision-iccv/)**Didac Suris, Sachit Menon, Carl Vondrick*2023 · 383 citations*
25. **[Visual Programming: Compositional visual reasoning without training](https://md.research.elamur.ai/free/en/papers/gupta-2022-visual-programming-compositional-visual-reasoning/) [IEEE Computer Society Conference on Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/venues/ieee-computer-society-conference-on-computer-vision-and-pattern-recognition/)**Tanmay Gupta, Aniruddha Kembhavi*2022 · 347 citations*
26. **[How Transferable are Neural Networks in NLP Applications?](https://md.research.elamur.ai/free/en/papers/mou-2016-how-transferable-are-neural-networks/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Lili Mou, Zhao Meng, Rui Yan, et al.*2016 · 328 citations*
27. **[Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models](https://md.research.elamur.ai/free/en/papers/lu-2023-chameleon-plug-play-compositional-reasoning/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Pan Lu, Baolin Peng, Hao Cheng, et al.*2023 · 327 citations*
28. [**Multi-task Sequence to Sequence Learning arXiv:1511.06114**Minh-Thang Luong, Quoc V. Le, Ilya Sutskever, et al.*2015 · 321 citations*](https://md.research.elamur.ai/free/en/papers/luong-2015-multi-task-sequence-sequence-learning/)
29. **[A Survey of Domain Adaptation for Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/chu-2018-survey-domain-adaptation-neural-machine/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Chenhui Chu, Rui Wang*2018 · 308 citations*
30. [**Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language arXiv:2204.00598**Andy Zeng, Maria Attarian, Brian Ichter, et al.*2022 · 302 citations*](https://md.research.elamur.ai/free/en/papers/zeng-2022-socratic-models-composing-zero-shot/)
31. **[CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/le-2022-coderl-mastering-code-generation-through/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Hung Le, Yue Wang, Akhilesh Deepak Gotmare, et al.*2022 · 297 citations*
32. [**Hierarchical Multiscale Recurrent Neural Networks arXiv:1609.01704**Junyoung Chung, Sungjin Ahn, Yoshua Bengio*2016 · 288 citations*](https://md.research.elamur.ai/free/en/papers/chung-2016-hierarchical-multiscale-recurrent-neural-networks/)
33. **[Hubness and Pollution: Delving into Cross-Space Mapping for Zero-Shot Learning](https://md.research.elamur.ai/free/en/papers/lazaridou-2015-hubness-pollution-delving-into-cross/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Angeliki Lazaridou, Georgiana Dinu, Marco Baroni*2015 · 276 citations*
34. [**The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision arXiv:1904.12584**Jiayuan Mao, Chuang Gan, Pushmeet Kohli, et al.*2019 · 241 citations*](https://md.research.elamur.ai/free/en/papers/mao-2019-neuro-symbolic-concept-learner-interpreting/)
35. **[Unsupervised Question Decomposition for Question Answering](https://md.research.elamur.ai/free/en/papers/perez-2020-unsupervised-question-decomposition-question-answering/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ethan Perez, Patrick Lewis, Wen-tau Yih, et al.*2020 · 222 citations*
36. [**Compositional Attention Networks for Machine Reasoning arXiv:1803.03067**Drew A. Hudson, Christopher D. Manning*2018 · 213 citations*](https://md.research.elamur.ai/free/en/papers/hudson-2018-compositional-attention-networks-machine-reasoning/)
37. **[Rethinking Cooperative Rationalization: Introspective Extraction and Complement Control](https://md.research.elamur.ai/free/en/papers/yu-2019-rethinking-cooperative-rationalization-introspective-extraction/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Mo Yu, Shiyu Chang, Yang Zhang, et al.*2019 · 179 citations*
38. **[Generating Sentences from Disentangled Syntactic and Semantic Spaces](https://md.research.elamur.ai/free/en/papers/bao-2019-generating-sentences-disentangled-syntactic-semantic/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yu Bao, Hao Zhou, Shujian Huang, et al.*2019 · 154 citations*
39. **[Grounded Adaptation for Zero-shot Executable Semantic Parsing](https://md.research.elamur.ai/free/en/papers/zhong-2020-grounded-adaptation-zero-shot-executable/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Victor Zhong, Mike Lewis, Sida I. Wang, et al.*2020 · 136 citations*
40. [**Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning arXiv:2303.15647**Vladislav Lialin, Vijeta Deshpande, Xiaowei Yao, et al.*2023 · 129 citations*](https://md.research.elamur.ai/free/en/papers/lialin-2023-scaling-down-scale-up-guide/)
41. [**Finding Neurons in a Haystack: Case Studies with Sparse Probing arXiv:2305.01610**Wes Gurnee, Neel Nanda, Matthew Pauly, et al.*2023 · 123 citations*](https://md.research.elamur.ai/free/en/papers/gurnee-2023-finding-neurons-haystack-case-studies/)
42. **[Cross-Domain Label-Adaptive Stance Detection](https://md.research.elamur.ai/free/en/papers/hardalov-2021-cross-domain-label-adaptive-stance/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Momchil Hardalov, Arnav Arora, Preslav Nakov, et al.*2021 · 96 citations*
43. [**Systematic Generalization: What Is Required and Can It Be Learned? arXiv:1811.12889**Dzmitry Bahdanau, Shikhar Murty, Michael Noukhovitch, et al.*2018 · 73 citations*](https://md.research.elamur.ai/free/en/papers/bahdanau-2018-systematic-generalization-required-learned/)
44. [**Part-of-Speech Tagging for Historical English**Yi Yang, Jacob Eisenstein*2016 · 45 citations*](https://md.research.elamur.ai/free/en/papers/yang-2016-part-speech-tagging-historical-english/)
45. **[Improving Stance Detection with Multi-Dataset Learning and Knowledge Distillation](https://md.research.elamur.ai/free/en/papers/li-2021-improving-stance-detection-multi-dataset/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yingjie Li, Chenye Zhao, Cornelia Caragea*2021 · 39 citations*
46. **[TADA : Task Agnostic Dialect Adapters for English](https://md.research.elamur.ai/free/en/papers/held-2023-tada-task-agnostic-dialect-adapters/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**William Held, Caleb Ziems, Diyi Yang*2023 · 17 citations*
47. **[Adversarial Network with External Knowledge for Zero-Shot Stance Detection](https://md.research.elamur.ai/free/en/papers/wang-2023-adversarial-network-external-knowledge-zero/) [Chinese National Conference on Computational Linguistics](https://md.research.elamur.ai/free/en/venues/ccl/)**Chunling Wang, Yijia Zhang, Xingyu Yu, et al.*2023 · 11 citations*
48. **[Improved Generalization of Arabic Text Classifiers](https://md.research.elamur.ai/free/en/papers/khaddaj-2019-improved-generalization-arabic-text-classifiers/) [Arabic Natural Language Processing Workshop (WANLP)](https://md.research.elamur.ai/free/en/venues/wanlp/)**Alaa Khaddaj, Hazem Hajj, Wassim El-Hajj*2019 · 10 citations*
49. **[Exploring Textual and Speech information in Dialogue Act Classification with Speaker Domain Adaptation](https://md.research.elamur.ai/free/en/papers/he-2018-exploring-textual-speech-information-dialogue/) [Annual Workshop of the Australasian Language Technology Association (ALTA)](https://md.research.elamur.ai/free/en/venues/alta/)**Xuanli He, Quan Tran, William Havard, et al.*2018 · 3 citations*
50. **[Generative-to-Discriminative Test-Time Adaptation via Manifold-Aware Diffusion and Bayesian Distillation](https://md.research.elamur.ai/free/en/papers/zhang-2026-generative-discriminative-test-time-adaptation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Boyun Zhang, Zequn Xie, Fangming Feng, et al.*2026*
51. **[GeoQuery-LSFB: A French Belgian Sign Language Corpus with Procedural Semantic Annotations](https://md.research.elamur.ai/free/en/papers/vos-2026-geoquery-lsfb-french-belgian-sign/) [LREC Workshop on the Representation and Processing of Sign Languages: Language in Motion](https://md.research.elamur.ai/free/en/venues/signlang/)**Liesbet De Vos, Laurence Meurant, Paul Van Eecke, et al.*2026*
52. **[Learning Flexible Large Multimodal Models with Arbitrary Modality Combinations](https://md.research.elamur.ai/free/en/papers/zhao-2026-learning-flexible-large-multimodal-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xinyu Zhao, Kangqi Ni, Jie Peng, et al.*2026*

506 more citing papers of our library have no page here yet.

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