Fetching the paper…
Reading the bibliography…
Knowledge distillation (KD) is the de facto standard for compressing large-scale models into smaller ones.
Model compression
Cristian Buciluǎ, Rich Caruana, and Alexandru Niculescu-Mizil · 2006
Earlier work this paper cites.
The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2007
Earlier work this paper cites.
Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
Self-paced learning for latent variable models
M Kumar, Benjamin Packer, and Daphne Koller · 2010
Earlier work this paper cites.
Sun database: Large-scale scene recognition from abbey to zoo
Jianxiong Xiao, James Hays, Krista A Ehinger, Aude Oliva, and Antonio Torralba · 2010
Earlier work this paper cites.
An analysis of single-layer networks in unsupervised feature learning
Adam Coates, Andrew Ng, and Honglak Lee · 2011
Earlier work this paper cites.
Statistical meta-analysis with applications
Joachim Hartung, Guido Knapp, and Bimal K Sinha · 2011
Earlier work this paper cites.
Are we ready for autonomous driving? the kitti vision benchmark suite
Andreas Geiger, Philip Lenz, and Raquel Urtasun · 2012
Earlier work this paper cites.
Knowledge distillation thrives on data augmentation
Huan Wang, Suhas Lohit, Michael Jones, and Yun Fu · 2012
Earlier work this paper cites.
3d object representations for fine-grained categorization
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei · 2013
Earlier work this paper cites.
Fine-grained visual classification of aircraft
S. Maji, J. Kannala, E. Rahtu, M. Blaschko, and A. Vedaldi · 2013
Earlier work this paper cites.
Do deep nets really need to be deep?
Jimmy Ba and Rich Caruana · 2014
Earlier work this paper cites.
Food-101–mining discriminative components with random forests
Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool · 2014
Earlier work this paper cites.
Describing textures in the wild
M. Cimpoi, S. Maji, I. Kokkinos, S. Mohamed, , and A. Vedaldi · 2014
Earlier work this paper cites.
Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
Earlier work this paper cites.
Fitnets: Hints for thin deep nets
Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio · 2014
Earlier work this paper cites.
Distilling the knowledge in a neural network
Geoffrey Hinton · 2015
Earlier work this paper cites.
Online batch selection for faster training of neural networks
Ilya Loshchilov and Frank Hutter · 2015
Earlier work this paper cites.
Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models
Bryan A Plummer, Liwei Wang, Chris M Cervantes, Juan C Caicedo, Julia Hockenmaier, and Svetlana Lazebnik · 2015
Earlier work this paper cites.
Tom Schaul · 2015
Earlier work this paper cites.
Cider: Consensus-based image description evaluation
Ramakrishna Vedantam, C Lawrence Zitnick, and Devi Parikh · 2015
Earlier work this paper cites.
Distilling knowledge from ensembles of neural networks for speech recognition
Yevgen Chebotar and Austin Waters · 2016
Earlier work this paper cites.
Neural data filter for bootstrapping stochastic gradient descent
Yang Fan, Fei Tian, Tao Qin, and Tie-Yan Liu · 2016
Earlier work this paper cites.
Sequence-level knowledge distillation
Yoon Kim and Alexander M Rush · 2016
Earlier work this paper cites.
Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification, 2017
Patrick Helber, Benjamin Bischke, Andreas Dengel, and Damian Borth · 2017
Earlier work this paper cites.
Clevr: A diagnostic dataset for compositional language and elementary visual reasoning
Justin Johnson, Bharath Hariharan, Laurens Van Der Maaten, Li Fei-Fei, C Lawrence Zitnick, and Ross Girshick · 2017
Earlier work this paper cites.
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Antti Tarvainen and Harri Valpola · 2017
Earlier work this paper cites.
Learning from multiple teacher networks
Shan You, Chang Xu, Chao Xu, and Dacheng Tao · 2017
Earlier work this paper cites.
JAX: composable transformations of Python+NumPy programs, 2018
James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, and Qiao Zhang · 2018
Earlier work this paper cites.
Functional map of the world
Gordon Christie, Neil Fendley, James Wilson, and Ryan Mukherjee · 2018
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin · 2018
Earlier work this paper cites.
Not all samples are created equal: Deep learning with importance sampling
Angelos Katharopoulos and François Fleuret · 2018
Earlier work this paper cites.
T Kudo · 2018
Earlier work this paper cites.
An empirical study of example forgetting during deep neural network learning
Mariya Toneva, Alessandro Sordoni, Remi Tachet des Combes, Adam Trischler, Yoshua Bengio, and Geoffrey J Gordon · 2018
Earlier work this paper cites.
Rotation equivariant cnns for digital pathology
Bastiaan S Veeling, Jasper Linmans, Jim Winkens, Taco Cohen, and Max Welling · 2018
Earlier work this paper cites.
Objectnet: A large-scale bias-controlled dataset for pushing the limits of object recognition models
Andrei Barbu, David Mayo, Julian Alverio, William Luo, Christopher Wang, Dan Gutfreund, Josh Tenenbaum, and Boris Katz · 2019
Earlier work this paper cites.
Data-free learning of student networks
Hanting Chen, Yunhe Wang, Chang Xu, Zhaohui Yang, Chuanjian Liu, Boxin Shi, Chunjing Xu, Chao Xu, and Qi Tian · 2019
Earlier work this paper cites.
On the efficacy of knowledge distillation
Jang Hyun Cho and Bharath Hariharan · 2019
Earlier work this paper cites.
Self-knowledge distillation in natural language processing
Sangchul Hahn and Heeyoul Choi · 2019
Earlier work this paper cites.
Knowledge distillation with adversarial samples supporting decision boundary
Byeongho Heo, Minsik Lee, Sangdoo Yun, and Jin Young Choi · 2019
Earlier work this paper cites.
Accelerating deep learning by focusing on the biggest losers
Angela H Jiang, Daniel L-K Wong, Giulio Zhou, David G Andersen, Jeffrey Dean, Gregory R Ganger, Gauri Joshi, Michael Kaminksy, Michael Kozuch, Zachary C Lipton, et al · 2019
Earlier work this paper cites.
Tinybert: Distilling bert for natural language understanding
Xiaoqi Jiao, Yichun Yin, Lifeng Shang, Xin Jiang, Xiao Chen, Linlin Li, Fang Wang, and Qun Liu · 2019
Earlier work this paper cites.
Submodular batch selection for training deep neural networks
KJ Joseph, Krishnakant Singh, Vineeth N Balasubramanian, et al · 2019
Earlier work this paper cites.
Set transformer: A framework for attention-based permutation-invariant neural networks
Juho Lee, Yoonho Lee, Jungtaek Kim, Adam Kosiorek, Seungjin Choi, and Yee Whye Teh · 2019
Earlier work this paper cites.
Ensemble distribution distillation
Andrey Malinin, Bruno Mlodozeniec, and Mark Gales · 2019
Earlier work this paper cites.
Do imagenet classifiers generalize to imagenet?
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar · 2019
Earlier work this paper cites.
Distilbert, a distilled version of bert: Smaller, faster, cheaper and lighter. arxiv 2019
Victor Sanh, L Debut, J Chaumond, and T Wolf · 2019
Earlier work this paper cites.
Contrastive representation distillation
Yonglong Tian, Dilip Krishnan, and Phillip Isola · 2019
Earlier work this paper cites.
Learning robust global representations by penalizing local predictive power
Haohan Wang, Songwei Ge, Zachary Lipton, and Eric P Xing · 2019
Earlier work this paper cites.
A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
Earlier work this paper cites.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy · 2020
Earlier work this paper cites.
Does learning require memorization? a short tale about a long tail
Vitaly Feldman · 2020
Earlier work this paper cites.
Bootstrap your own latent-a new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al · 2020
Cited alongside, same era.
Big transfer (bit): General visual representation learning
Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, and Neil Houlsby · 2020
Cited alongside, same era.
Mixkd: Towards efficient distillation of large-scale language models
Kevin J Liang, Weituo Hao, Dinghan Shen, Yufan Zhou, Weizhu Chen, Changyou Chen, and Lawrence Carin · 2020
Cited alongside, same era.
Autoregressive knowledge distillation through imitation learning
Alexander Lin, Jeremy Wohlwend, Howard Chen, and Tao Lei · 2020
Cited alongside, same era.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu · 2020
Does clip’s generalization performance mainly stem from high train-test similarity?
Prasanna Mayilvahanan, Thaddäus Wiedemer, Evgenia Rusak, Matthias Bethge, and Wieland Brendel · 2023
Later among the works it cites.
Hard: Hard augmentations for robust distillation
Arne F Nix, Max F Burg, and Fabian H Sinz · 2023
Later among the works it cites.
Dinov2: Learning robust visual features without supervision
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al · 2023
Later among the works it cites.
Filtering, distillation, and hard negatives for vision-language pre-training
Filip Radenovic, Abhimanyu Dubey, Abhishek Kadian, Todor Mihaylov, Simon Vandenhende, Yash Patel, Yi Wen, Vignesh Ramanathan, and Dhruv Mahajan · 2023
Later among the works it cites.
Dynamic contrastive distillation for image-text retrieval
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Meal v2: Boosting vanilla resnet-50 to 80%+ top-1 accuracy on imagenet without tricks
Zhiqiang Shen and Marios Savvides · 2020
Cited alongside, same era.
Carpe diem, seize the samples uncertain “at the moment” for adaptive batch selection
Hwanjun Song, Minseok Kim, Sundong Kim, and Jae-Gil Lee · 2020
Cited alongside, same era.
Self-training with noisy student improves imagenet classification
Qizhe Xie, Minh-Thang Luong, Eduard Hovy, and Quoc V Le · 2020
Cited alongside, same era.
The iwildcam 2021 competition dataset
Sara Beery, Arushi Agarwal, Elijah Cole, and Vighnesh Birodkar · 2021
Cited alongside, same era.
On the opportunities and risks of foundation models
Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al · 2021
Cited alongside, same era.
Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts
Soravit Changpinyo, Piyush Sharma, Nan Ding, and Radu Soricut · 2021
Cited alongside, same era.
Teachtext: Crossmodal generalized distillation for text-video retrieval
Ioana Croitoru, Simion-Vlad Bogolin, Marius Leordeanu, Hailin Jin, Andrew Zisserman, Samuel Albanie, and Yang Liu · 2021
Cited alongside, same era.
Jun Rao, Liang Ding, Shuhan Qi, Meng Fang, Yang Liu, Li Shen, and Dacheng Tao · 2023
Later among the works it cites.
Global selection of contrastive batches via optimization on sample permutations
Vin Sachidananda, Ziyi Yang, and Chenguang Zhu · 2023
Later among the works it cites.
Ferkd: Surgical label adaptation for efficient distillation
Zhiqiang Shen · 2023
Later among the works it cites.
Gkd: A general knowledge distillation framework for large-scale pre-trained language model
Shicheng Tan, Weng Lam Tam, Yuanchun Wang, Wenwen Gong, Yang Yang, Hongyin Tang, Keqing He, Jiahao Liu, Jingang Wang, Shu Zhao, et al · 2023
Later among the works it cites.
Sus-x: Training-free name-only transfer of vision-language models
Vishaal Udandarao, Ankush Gupta, and Samuel Albanie · 2023
Later among the works it cites.
Fastvit: A fast hybrid vision transformer using structural reparameterization
Pavan Kumar Anasosalu Vasu, James Gabriel, Jeff Zhu, Oncel Tuzel, and Anurag Ranjan · 2023
Later among the works it cites.
Tinyclip: Clip distillation via affinity mimicking and weight inheritance
Kan Wu, Houwen Peng, Zhenghong Zhou, Bin Xiao, Mengchen Liu, Lu Yuan, Hong Xuan, Michael Valenzuela, Xi Stephen Chen, Xinggang Wang, et al · 2023
Later among the works it cites.
The devil is in the details: A deep dive into the rabbit hole of data filtering
Haichao Yu, Yu Tian, Sateesh Kumar, Linjie Yang, and Heng Wang · 2023
Later among the works it cites.
Sigmoid loss for language image pre-training
Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer · 2023
Later among the works it cites.
Adads: Adaptive data selection for accelerating pre-trained language model knowledge distillation
Qinhong Zhou, Peng Li, Yang Liu, Yuyang Guan, Qizhou Xing, Ming Chen, and Maosong Sun · 2023
Later among the works it cites.
Konrad Zuchniak · 2023
Later among the works it cites.
Phi-3 technical report: A highly capable language model locally on your phone
Marah Abdin, Sam Ade Jacobs, Ammar Ahmad Awan, Jyoti Aneja, Ahmed Awadallah, Hany Awadalla, Nguyen Bach, Amit Bahree, Arash Bakhtiari, Harkirat Behl, et al · 2024
Closest in time.
On-policy distillation of language models: Learning from self-generated mistakes
Rishabh Agarwal, Nino Vieillard, Yongchao Zhou, Piotr Stanczyk, Sabela Ramos Garea, Matthieu Geist, and Olivier Bachem · 2024
Closest in time.
Color-filter: Conditional loss reduction filtering for targeted language model pre-training
David Brandfonbrener, Hanlin Zhang, Andreas Kirsch, Jonathan Richard Schwarz, and Sham Kakade · 2024
Closest in time.
Data curation via joint example selection further accelerates multimodal learning
Talfan Evans, Nikhil Parthasarathy, Hamza Merzic, and Olivier J Henaff · 2024
Closest in time.
Improving clip training with language rewrites
Lijie Fan, Dilip Krishnan, Phillip Isola, Dina Katabi, and Yonglong Tian · 2024
Closest in time.
What makes a good dataset for knowledge distillation?
Logan Frank and Jim Davis · 2024
Closest in time.
Datacomp: In search of the next generation of multimodal datasets
Samir Yitzhak Gadre, Gabriel Ilharco, Alex Fang, Jonathan Hayase, Georgios Smyrnis, Thao Nguyen, Ryan Marten, Mitchell Wortsman, Dhruba Ghosh, Jieyu Zhang, et al · 2024
Closest in time.
Clip-adapter: Better vision-language models with feature adapters
Peng Gao, Shijie Geng, Renrui Zhang, Teli Ma, Rongyao Fang, Yongfeng Zhang, Hongsheng Li, and Yu Qiao · 2024
Closest in time.
Training task experts through retrieval based distillation
Jiaxin Ge, Xueying Jia, Vijay Viswanathan, Hongyin Luo, and Graham Neubig · 2024
Closest in time.
Scaling laws for data filtering–data curation cannot be compute agnostic
Sachin Goyal, Pratyush Maini, Zachary C Lipton, Aditi Raghunathan, and J Zico Kolter · 2024
Closest in time.
Amd: Automatic multi-step distillation of large-scale vision models
Cheng Han, Qifan Wang, Sohail A Dianat, Majid Rabbani, Raghuveer M Rao, Yi Fang, Qiang Guan, Lifu Huang, and Dongfang Liu · 2024
Closest in time.
Revisit the power of vanilla knowledge distillation: from small scale to large scale
Zhiwei Hao, Jianyuan Guo, Kai Han, Han Hu, Chang Xu, and Yunhe Wang · 2024
Closest in time.
Diversified batch selection for training acceleration
Feng Hong, Yueming Lyu, Jiangchao Yao, Ya Zhang, Ivor W Tsang, and Yanfeng Wang · 2024
Closest in time.
Hype: Hyperbolic entailment filtering for underspecified images and texts
Wonjae Kim, Sanghyuk Chun, Taekyung Kim, Dongyoon Han, and Sangdoo Yun · 2024
Closest in time.
Improve knowledge distillation via label revision and data selection
Weichao Lan, Yiu-ming Cheung, Qing Xu, Buhua Liu, Zhikai Hu, Mengke Li, and Zhenghua Chen · 2024
Closest in time.
Modeling caption diversity in contrastive vision-language pretraining
Samuel Lavoie, Polina Kirichenko, Mark Ibrahim, Mahmoud Assran, Andrew Gordon Wilson, Aaron Courville, and Nicolas Ballas · 2024
Closest in time.
Module-wise adaptive distillation for multimodality foundation models
Chen Liang, Jiahui Yu, Ming-Hsuan Yang, Matthew Brown, Yin Cui, Tuo Zhao, Boqing Gong, and Tianyi Zhou · 2024
Closest in time.
Sieve: Multimodal dataset pruning using image captioning models
Anas Mahmoud, Mostafa Elhoushi, Amro Abbas, Yu Yang, Newsha Ardalani, Hugh Leather, and Ari S Morcos · 2024
Closest in time.
Silc: Improving vision language pretraining with self-distillation
Muhammad Ferjad Naeem, Yongqin Xian, Xiaohua Zhai, Lukas Hoyer, Luc Van Gool, and Federico Tombari · 2024
Closest in time.
Geode: a geographically diverse evaluation dataset for object recognition
Vikram V Ramaswamy, Sing Yu Lin, Dora Zhao, Aaron Adcock, Laurens van der Maaten, Deepti Ghadiyaram, and Olga Russakovsky · 2024
Closest in time.
Am-radio: Agglomerative vision foundation model reduce all domains into one
Mike Ranzinger, Greg Heinrich, Jan Kautz, and Pavlo Molchanov · 2024
Closest in time.
A little help goes a long way: Efficient llm training by leveraging small lms
Ankit Singh Rawat, Veeranjaneyulu Sadhanala, Afshin Rostamizadeh, Ayan Chakrabarti, Wittawat Jitkrittum, Vladimir Feinberg, Seungyeon Kim, Hrayr Harutyunyan, Nikunj Saunshi, Zachary Nado, et al · 2024
Closest in time.
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
Machel Reid, Nikolay Savinov, Denis Teplyashin, Dmitry Lepikhin, Timothy Lillicrap, Jean-baptiste Alayrac, Radu Soricut, Angeliki Lazaridou, Orhan Firat, Julian Schrittwieser, et al · 2024
Closest in time.
A practitioner’s guide to continual multimodal pretraining
Karsten Roth, Vishaal Udandarao, Sebastian Dziadzio, Ameya Prabhu, Mehdi Cherti, Oriol Vinyals, Olivier Hénaff, Samuel Albanie, Matthias Bethge, and Zeynep Akata · 2024
Closest in time.
Building vision-language models on solid foundations with masked distillation
Sepehr Sameni, Kushal Kafle, Hao Tan, and Simon Jenni · 2024
Closest in time.
Unic: Universal classification models via multi-teacher distillation
Mert Bulent Sariyildiz, Philippe Weinzaepfel, Thomas Lucas, Diane Larlus, and Yannis Kalantidis · 2024
Closest in time.
Fast high-resolution image synthesis with latent adversarial diffusion distillation
Axel Sauer, Frederic Boesel, Tim Dockhorn, Andreas Blattmann, Patrick Esser, and Robin Rombach · 2024
Closest in time.
Gemma 2: Improving open language models at a practical size
Gemma Team, Morgane Riviere, Shreya Pathak, Pier Giuseppe Sessa, Cassidy Hardin, Surya Bhupatiraju, Léonard Hussenot, Thomas Mesnard, Bobak Shahriari, Alexandre Ramé, et al · 2024
Closest in time.
Vishaal Udandarao, Ameya Prabhu, Adhiraj Ghosh, Yash Sharma, Philip HS Torr, Adel Bibi, Samuel Albanie, and Matthias Bethge · 2024
Closest in time.
Mobileclip: Fast image-text models through multi-modal reinforced training
Pavan Kumar Anasosalu Vasu, Hadi Pouransari, Fartash Faghri, Raviteja Vemulapalli, and Oncel Tuzel · 2024
Closest in time.
Knowledge transfer from vision foundation models for efficient training of small task-specific models
Raviteja Vemulapalli, Hadi Pouransari, Fartash Faghri, Sachin Mehta, Mehrdad Farajtabar, Mohammad Rastegari, and Oncel Tuzel · 2024
Closest in time.
Automatic data curation for self-supervised learning: A clustering-based approach
Huy V Vo, Vasil Khalidov, Timothée Darcet, Théo Moutakanni, Nikita Smetanin, Marc Szafraniec, Hugo Touvron, Camille Couprie, Maxime Oquab, Armand Joulin, et al · 2024
Closest in time.
A survey on knowledge distillation of large language models
Xiaohan Xu, Ming Li, Chongyang Tao, Tao Shen, Reynold Cheng, Jinyang Li, Can Xu, Dacheng Tao, and Tianyi Zhou · 2024
Closest in time.
Clip-cid: Efficient clip distillation via cluster-instance discrimination
Kaicheng Yang, Tiancheng Gu, Xiang An, Haiqiang Jiang, Xiangzi Dai, Ziyong Feng, Weidong Cai, and Jiankang Deng · 2024
Closest in time.
Capsfusion: Rethinking image-text data at scale
Qiying Yu, Quan Sun, Xiaosong Zhang, Yufeng Cui, Fan Zhang, Yue Cao, Xinlong Wang, and Jingjing Liu · 2024
Closest in time.
Wenbo Zhang, Yifan Zhang, Jianfeng Lin, Binqiang Huang, Jinlu Zhang, and Wenhao Yu · 2024
Closest in time.
Multi-label adaptive batch selection by highlighting hard and imbalanced samples
Ao Zhou, Bin Liu, Zhaoyang Peng, Jin Wang, and Grigorios Tsoumakas · 2024
Closest in time.
Mobilenetv4: Universal models for the mobile ecosystem
Danfeng Qin, Chas Leichner, Manolis Delakis, Marco Fornoni, Shixin Luo, Fan Yang, Weijun Wang, Colby Banbury, Chengxi Ye, Berkin Akin, et al · 2025
Closest in time.