---
title: "Who cites Geoffrey E. Hinton — page 2 — 10,217 citing papers"
description: "10,217 papers of the Elamur Research library cite the works of Geoffrey E. Among them: Incorporating Copying Mechanism in…"
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# Who cites Geoffrey E. Hinton

11,868 citations of their works from 10,217 papers of our library (of the 149,439 whose bibliographies we read) · 1,205 with a page here, the most cited first · page 2 of 13 · 101–200 · [← Geoffrey E. Hinton](https://md.research.elamur.ai/free/en/authors/geoffrey-e-hinton/)

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101. **[Incorporating Copying Mechanism in Sequence-to-Sequence Learning](https://md.research.elamur.ai/free/en/papers/gu-2016-incorporating-copying-mechanism-sequence-sequence/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jiatao Gu, Zhengdong Lu, Hang Li, et al.*2016 · 1,915 citations*
102. [**BinaryConnect: Training Deep Neural Networks with binary weights during propagations arXiv:1511.00363**Matthieu Courbariaux, Yoshua Bengio, Jean-Pierre David*2015 · 1,885 citations*](https://md.research.elamur.ai/free/en/papers/courbariaux-2015-binaryconnect-training-deep-neural-networks/)
103. [**Towards Deep Learning Models Resistant to Adversarial Attacks arXiv:1706.06083**Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, et al.*2017 · 1,874 citations*](https://md.research.elamur.ai/free/en/papers/madry-2017-towards-deep-learning-models-resistant/)
104. **[Character-Aware Neural Language Models](https://md.research.elamur.ai/free/en/papers/kim-2015-character-aware-neural-language-models/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Yoon Kim, Yacine Jernite, David Sontag, et al.*2015 · 1,874 citations*
105. [**Unsupervised Data Augmentation for Consistency Training arXiv:1904.12848**Qizhe Xie, Zihang Dai, Eduard Hovy, et al.*2019 · 1,871 citations*](https://md.research.elamur.ai/free/en/papers/xie-2019-unsupervised-data-augmentation-consistency-training/)
106. [**DeBERTa: Decoding-enhanced BERT with Disentangled Attention arXiv:2006.03654**Pengcheng He, Xiaodong Liu, Jianfeng Gao, et al.*2020 · 1,828 citations*](https://md.research.elamur.ai/free/en/papers/he-2020-deberta-decoding-enhanced-bert-disentangled/)
107. [**Layer Normalization arXiv:1607.06450**Jimmy Lei Ba, Jamie Ryan Kiros, Geoffrey E. Hinton*2016 · 1,819 citations*](https://md.research.elamur.ai/free/en/papers/ba-2016-layer-normalization/)
108. [**Attention-Based Models for Speech Recognition arXiv:1506.07503**Jan Chorowski, Dzmitry Bahdanau, Dmitriy Serdyuk, et al.*2015 · 1,819 citations*](https://md.research.elamur.ai/free/en/papers/chorowski-2015-attention-based-models-speech-recognition/)
109. **[Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference](https://md.research.elamur.ai/free/en/papers/schick-2021-exploiting-cloze-questions-few-shot/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Timo Schick, Hinrich Schütze*2021 · 1,817 citations*
110. [**A Neural Conversational Model arXiv:1506.05869**Oriol Vinyals, Quoc Le*2015 · 1,791 citations*](https://md.research.elamur.ai/free/en/papers/vinyals-2015-neural-conversational-model/)
111. [**DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients arXiv:1606.06160**Shuchang Zhou, Yuxin Wu, Zekun Ni, et al.*2016 · 1,762 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2016-dorefa-net-training-low-bitwidth/)
112. [**Deep Unsupervised Learning using Nonequilibrium Thermodynamics arXiv:1503.03585**Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, et al.*2015 · 1,761 citations*](https://md.research.elamur.ai/free/en/papers/sohl-dickstein-2015-deep-unsupervised-learning-using-nonequilibrium/)
113. [**Pre-trained Models for Natural Language Processing: A Survey arXiv:2003.08271 · SCIENCE CHINA Technological Sciences,2020, 63, 1872-1897**Xipeng Qiu, Tianxiang Sun, Yige Xu, et al.*2020 · 1,759 citations*](https://md.research.elamur.ai/free/en/papers/qiu-2020-pre-trained-models-natural-language/)
114. **[YFCC100M: The New Data in Multimedia Research](https://md.research.elamur.ai/free/en/papers/thomee-2015-yfcc100m-new-data-multimedia-research/) [Communications of the ACM](https://md.research.elamur.ai/free/en/venues/communications-of-the-acm/)**Bart Thomee, David A. Shamma, Gerald Friedland, et al.*2015 · 1,671 citations*
115. [**Qwen Technical Report arXiv:2309.16609**Jinze Bai, Shuai Bai, Yunfei Chu, et al.*2023 · 1,669 citations*](https://md.research.elamur.ai/free/en/papers/bai-2023-qwen-technical-report/)
116. **[Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://md.research.elamur.ai/free/en/papers/jia-2021-scaling-up-visual-vision-language/) [International Conference on Machine Learning (ICML)](https://md.research.elamur.ai/free/en/venues/international-conference-on-machine-learning/)**Chao Jia, Yinfei Yang, Ye Xia, et al.*2021 · 1,615 citations*
117. [**Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations arXiv:1609.07061**Itay Hubara, Matthieu Courbariaux, Daniel Soudry, et al.*2016 · 1,610 citations*](https://md.research.elamur.ai/free/en/papers/hubara-2016-quantized-neural-networks-training-neural/)
118. **[Memory Aware Synapses: Learning what (not) to forget](https://md.research.elamur.ai/free/en/papers/aljundi-2017-memory-aware-synapses-learning-forget/) [Lecture notes in computer science](https://md.research.elamur.ai/free/en/venues/lecture-notes-in-computer-science/)**Rahaf Aljundi, Francesca Babiloni, Mohamed Elhoseiny, et al.*2017 · 1,608 citations*
119. **[End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures](https://md.research.elamur.ai/free/en/papers/miwa-2016-end-end-relation-extraction-lstms/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Makoto Miwa, Mohit Bansal*2016 · 1,606 citations*
120. [**Deep Speech: Scaling up end-to-end speech recognition arXiv:1412.5567**Awni Hannun, Carl Case, Jared Casper, et al.*2014 · 1,600 citations*](https://md.research.elamur.ai/free/en/papers/hannun-2014-deep-speech-scaling-up-end/)
121. [**On the Convergence of Adam and Beyond arXiv:1904.09237**Sashank J. Reddi, Satyen Kale, Sanjiv Kumar*2019 · 1,598 citations*](https://md.research.elamur.ai/free/en/papers/reddi-2019-convergence-adam/)
122. [**Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context arXiv:2403.05530**Gemini Team, Petko Georgiev, Ving Ian Lei, et al.*2024 · 1,589 citations*](https://md.research.elamur.ai/free/en/papers/gemini-team-2024-gemini-1-5-unlocking-multimodal/)
123. **[Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates](https://md.research.elamur.ai/free/en/papers/kudo-2018-subword-regularization-improving-neural-network/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Taku Kudo*2018 · 1,573 citations*
124. **[Universal Sentence Encoder for English](https://md.research.elamur.ai/free/en/papers/cer-2018-universal-sentence-encoder-english/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Daniel Cer, Yinfei Yang, Sheng-yi Kong, et al.*2018 · 1,546 citations*
125. **[Efficient Transformers: A Survey](https://md.research.elamur.ai/free/en/papers/tay-2020-efficient-transformers-survey/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Yi Tay, Mostafa Dehghani, Dara Bahri, et al.*2020 · 1,539 citations*
126. [**BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation arXiv:2201.12086**Junnan Li, Dongxu Li, Caiming Xiong, et al.*2022 · 1,523 citations*](https://md.research.elamur.ai/free/en/papers/li-2022-blip-bootstrapping-language-image-pre/)
127. **[Multimodal Language Analysis in the Wild: CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph](https://md.research.elamur.ai/free/en/papers/zadeh-2018-multimodal-language-analysis-wild-cmu/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**AmirAli Bagher Zadeh, Paul Pu Liang, Soujanya Poria, et al.*2018 · 1,522 citations*
128. [**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/)
129. [**DINOv2: Learning Robust Visual Features without Supervision arXiv:2304.07193**Maxime Oquab, Timothee Darcet, Theo Moutakanni, et al.*2023 · 1,513 citations*](https://md.research.elamur.ai/free/en/papers/oquab-2023-dinov2-learning-robust-visual-features/)
130. **[Sigmoid Loss for Language Image Pre-Training](https://md.research.elamur.ai/free/en/papers/zhai-2023-sigmoid-loss-language-image-pre/) [IEEE/CVF International Conference on Computer Vision (ICCV)](https://md.research.elamur.ai/free/en/venues/ieee-cvf-international-conference-on-computer-vision-iccv/)**Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, et al.*2023 · 1,477 citations*
131. **[GLM: General Language Model Pretraining with Autoregressive Blank Infilling](https://md.research.elamur.ai/free/en/papers/du-2022-glm-general-language-model-pretraining/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhengxiao Du, Yujie Qian, Xiao Liu, et al.*2022 · 1,455 citations*
132. **[From Captions to Visual Concepts and Back](https://md.research.elamur.ai/free/en/papers/fang-2014-captions-visual-concepts-back/) [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/)**Hao Fang, Saurabh Gupta, Forrest Iandola, et al.*2014 · 1,450 citations*
133. [**OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks arXiv:1312.6229**Pierre Sermanet, David Eigen, Xiang Zhang, et al.*2013 · 1,423 citations*](https://md.research.elamur.ai/free/en/papers/sermanet-2013-overfeat-integrated-recognition-localization-detection/)
134. **[wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://md.research.elamur.ai/free/en/papers/baevski-2020-wav2vec-2-0-framework-self/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, et al.*2020 · 1,421 citations*
135. **[Language-agnostic BERT Sentence Embedding](https://md.research.elamur.ai/free/en/papers/feng-2020-language-agnostic-bert-sentence-embedding/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Fangxiaoyu Feng, Yinfei Yang, Daniel Cer, et al.*2020 · 1,413 citations*
136. **[Generation and Comprehension of Unambiguous Object Descriptions](https://md.research.elamur.ai/free/en/papers/mao-2015-generation-comprehension-unambiguous-object-descriptions/) [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/)**Junhua Mao, Jonathan Huang, Alexander Toshev, et al.*2015 · 1,388 citations*
137. [**Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models arXiv:1411.2539**Ryan Kiros, Ruslan Salakhutdinov, Richard S. Zemel*2014 · 1,371 citations*](https://md.research.elamur.ai/free/en/papers/kiros-2014-unifying-visual-semantic-embeddings-multimodal/)
138. [**Language Modeling with Gated Convolutional Networks arXiv:1612.08083**Yann N. Dauphin, Angela Fan, Michael Auli, et al.*2016 · 1,346 citations*](https://md.research.elamur.ai/free/en/papers/dauphin-2016-language-modeling-gated-convolutional-networks/)
139. **[ReferItGame: Referring to Objects in Photographs of Natural Scenes](https://md.research.elamur.ai/free/en/papers/kazemzadeh-2014-referitgame-referring-objects-photographs-natural/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Sahar Kazemzadeh, Vicente Ordonez, Mark Matten, et al.*2014 · 1,317 citations*
140. **[A Primer on Neural Network Models for Natural Language Processing](https://md.research.elamur.ai/free/en/papers/goldberg-2015-primer-neural-network-models-natural/) [Journal of Artificial Intelligence Research](https://md.research.elamur.ai/free/en/venues/journal-of-artificial-intelligence-research/)**Yoav Goldberg*2015 · 1,303 citations*
141. **[COMET: Commonsense Transformers for Automatic Knowledge Graph Construction](https://md.research.elamur.ai/free/en/papers/bosselut-2019-comet-commonsense-transformers-automatic-knowledge/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Antoine Bosselut, Hannah Rashkin, Maarten Sap, et al.*2019 · 1,294 citations*
142. [**CTRL: A Conditional Transformer Language Model for Controllable Generation arXiv:1909.05858**Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney, et al.*2019 · 1,291 citations*](https://md.research.elamur.ai/free/en/papers/keskar-2019-ctrl-conditional-transformer-language-model/)
143. **[A Comprehensive Overview of Large Language Models](https://md.research.elamur.ai/free/en/papers/naveed-2023-comprehensive-overview-large-language-models/) [ACM Transactions on Intelligent Systems and Technology](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-intelligent-systems-and-technology/)**Humza Naveed, Asad Ullah Khan, Shi Qiu, et al.*2023 · 1,287 citations*
144. **[Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies](https://md.research.elamur.ai/free/en/papers/linzen-2016-assessing-ability-lstms-learn-syntax/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Tal Linzen, Emmanuel Dupoux, Yoav Goldberg*2016 · 1,256 citations*
145. [**cuDNN: Efficient Primitives for Deep Learning arXiv 2014 (NVIDIA)**Sharan Chetlur, Cliff Woolley, Philippe Vandermersch, et al.*2014 · 1,240 citations*](https://md.research.elamur.ai/free/en/papers/chetlur-2014-cudnn-efficient-primitives-deep-learning/)
146. [**Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning arXiv:1709.00103**Victor Zhong, Caiming Xiong, Richard Socher*2017 · 1,226 citations*](https://md.research.elamur.ai/free/en/papers/zhong-2017-seq2sql-generating-structured-queries-natural/)
147. [**Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps arXiv:1312.6034**Karen Simonyan, Andrea Vedaldi, Andrew Zisserman*2013 · 1,210 citations*](https://md.research.elamur.ai/free/en/papers/simonyan-2013-deep-inside-convolutional-networks-visualising/)
148. **[Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems](https://md.research.elamur.ai/free/en/papers/wen-2015-semantically-conditioned-lstm-based-natural/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Tsung-Hsien Wen, Milica Gašić, Nikola Mrkšić, et al.*2015 · 1,202 citations*
149. [**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/)
150. **[EIE: Efficient Inference Engine on Compressed Deep Neural Network](https://md.research.elamur.ai/free/en/papers/han-2016-eie-efficient-inference-engine-compressed/) [ACM/IEEE International Symposium on Computer Architecture (ISCA)](https://md.research.elamur.ai/free/en/venues/acm-ieee-international-symposium-on-computer-architecture-isca/)**Song Han, Xingyu Liu, Huizi Mao, et al.*2016 · 1,193 citations*
151. **[Event Extraction via Dynamic Multi-Pooling Convolutional Neural Networks](https://md.research.elamur.ai/free/en/papers/chen-2015-event-extraction-dynamic-multi-pooling/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yubo Chen, Liheng Xu, Kang Liu, et al.*2015 · 1,177 citations*
152. **[It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners](https://md.research.elamur.ai/free/en/papers/schick-2021-it-s-not-just-size/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Timo Schick, Hinrich Schütze*2021 · 1,132 citations*
153. **[Very Deep Convolutional Networks for Text Classification](https://md.research.elamur.ai/free/en/papers/conneau-2017-very-deep-convolutional-networks-text/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Alexis Conneau, Holger Schwenk, Loïc Barrault, et al.*2017 · 1,094 citations*
154. **[Bias and Fairness in Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/gallegos-2023-bias-fairness-large-language-models/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Isabel O. Gallegos, Ryan A. Rossi, Joe Barrow, et al.*2023 · 1,082 citations*
155. [**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/)
156. **[Vision-Language Models for Vision Tasks: A Survey](https://md.research.elamur.ai/free/en/papers/zhang-2023-vision-language-models-vision-tasks/) [IEEE Transactions on Pattern Analysis and Machine Intelligence](https://md.research.elamur.ai/free/en/venues/ieee-transactions-on-pattern-analysis-and-machine-intelligence/)**Jingyi Zhang, Jiaxing Huang, Sheng Jin, et al.*2023 · 1,047 citations*
157. **[What you can cram into a single $&!#* vector: Probing sentence embeddings for linguistic properties](https://md.research.elamur.ai/free/en/papers/conneau-2018-what-you-can-cram-into/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Alexis Conneau, German Kruszewski, Guillaume Lample, et al.*2018 · 1,045 citations*
158. [**A Theoretically Grounded Application of Dropout in Recurrent Neural Networks arXiv:1512.05287**Yarin Gal, Zoubin Ghahramani*2015 · 1,032 citations*](https://md.research.elamur.ai/free/en/papers/gal-2015-theoretically-grounded-application-dropout-recurrent/)
159. [**Exact solutions to the nonlinear dynamics of learning in deep linear neural networks arXiv:1312.6120**Andrew M. Saxe, James L. McClelland, Surya Ganguli*2013 · 1,027 citations*](https://md.research.elamur.ai/free/en/papers/saxe-2013-exact-solutions-nonlinear-dynamics-learning/)
160. **[GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints](https://md.research.elamur.ai/free/en/papers/ainslie-2023-gqa-training-generalized-multi-query/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Joshua Ainslie, James Lee-Thorp, Michiel de Jong, et al.*2023 · 1,026 citations*
161. **[A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents](https://md.research.elamur.ai/free/en/papers/cohan-2018-discourse-aware-attention-model-abstractive/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Arman Cohan, Franck Dernoncourt, Doo Soon Kim, et al.*2018 · 1,026 citations*
162. [**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/)
163. **[MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices](https://md.research.elamur.ai/free/en/papers/sun-2020-mobilebert-compact-task-agnostic-bert/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhiqing Sun, Hongkun Yu, Xiaodan Song, et al.*2020 · 1,016 citations*
164. **[Matching the Blanks: Distributional Similarity for Relation Learning](https://md.research.elamur.ai/free/en/papers/soares-2019-matching-blanks-distributional-similarity-relation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Livio Baldini Soares, Nicholas FitzGerald, Jeffrey Ling, et al.*2019 · 999 citations*
165. **[Chinese NER Using Lattice LSTM](https://md.research.elamur.ai/free/en/papers/zhang-2018-chinese-ner-lattice-lstm/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yue Zhang, Jie Yang*2018 · 998 citations*
166. [**U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597**Olaf Ronneberger, Philipp Fischer, Thomas Brox*2015 · 997 citations*](https://md.research.elamur.ai/free/en/papers/ronneberger-2015-u-net-convolutional-networks-biomedical/)
167. [**Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities arXiv:2507.06261**Gheorghe Comanici, Eric Bieber, Mike Schaekermann, et al.*2025 · 974 citations*](https://md.research.elamur.ai/free/en/papers/comanici-2025-gemini-2-5-pushing-frontier/)
168. [**Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges arXiv:2103.11251 · Statistics Surveys, 2021**Cynthia Rudin; Chaofan Chen; Zhi Chen; Haiyang Huang; Lesia Semenova;, Chudi Zhong*2021 · 974 citations*](https://md.research.elamur.ai/free/en/papers/rudin-2021-interpretable-machine-learning-fundamental/)
169. **[Effective Use of Word Order for Text Categorization with Convolutional Neural Networks](https://md.research.elamur.ai/free/en/papers/johnson-2015-effective-use-word-order-text/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Rie Johnson, Tong Zhang*2015 · 971 citations*
170. [**Generating Long Sequences with Sparse Transformers arXiv:1904.10509**Rewon Child, Scott Gray, Alec Radford, et al.*2019 · 967 citations*](https://md.research.elamur.ai/free/en/papers/child-2019-generating-long-sequences-sparse-transformers/)
171. [**QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding arXiv:1610.02132**Dan Alistarh, Demjan Grubic, Jerry Li, et al.*2016 · 962 citations*](https://md.research.elamur.ai/free/en/papers/alistarh-2016-qsgd-communication-efficient-sgd-gradient/)
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