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Generating bitmap graphics from text has gained considerable attention, yet for scientific figures, vector graphics are often preferred.
The visual display of quantitative information
Edward Rolf Tufte · 1992
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Let’s practice what we preach: Turning tables into graphs
Andrew Gelman, Cristian Pasarica, and Rahul Dodhia · 2002
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BLEU: A method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu · 2002
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Single authors are not alone: Colleagues often help
James Hartley · 2003
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Model compression
Cristian Bucila, Rich Caruana, and Alexandru Niculescu-Mizil · 2006
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PSTricks: PostScript macros for Generic TeX , 2007
Timothy van Zandt · 2007
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MaxDiff analysis: Simple counting, individual-level logit, and HB
Bryan K. Orme · 2009
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Featherweight TeX and parser correctness
Sebastian Thore Erdweg and Klaus Ostermann · 2010
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MetaPost , 2014
John D. Hobby · 2014
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Microsoft COCO: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge J. Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick · 2014
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Best-Worst Scaling: Theory, Methods and Applications
Jordan J. Louviere, Terry N. Flynn, and A. A. J. Marley · 2015
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Capturing reliable fine-grained sentiment associations by crowdsourcing and best–worst scaling
Svetlana Kiritchenko and Saif M. Mohammad · 2016
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Generative adversarial text to image synthesis
Scott E. Reed, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele, and Honglak Lee · 2016
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FigureSeer: Parsing result-figures in research papers
Noah Siegel, Zachary Horvitz, Roie Levin, Santosh Kumar Divvala, and Ali Farhadi · 2016
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StackGAN: Text to photo-realistic image synthesis with stacked generative adversarial networks
Han Zhang, Tao Xu, and Hongsheng Li · 2017
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Demystifying MMD GANs
Mikolaj Binkowski, Danica J. Sutherland, Michael Arbel, and Arthur Gretton · 2018
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A neural representation of sketch drawings
David Ha and Douglas Eck · 2018
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DVQA: Understanding data visualizations via question answering
Kushal Kafle, Brian L. Price, Scott Cohen, and Christopher Kanan · 2018
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FigureQA: An annotated figure dataset for visual reasoning
Samira Ebrahimi Kahou, Vincent Michalski, Adam Atkinson, Ákos Kádár, Adam Trischler, and Yoshua Bengio · 2018
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Mixed precision training
Paulius Micikevicius, Sharan Narang, Jonah Alben, Gregory F. Diamos, Erich Elsen, David García, Boris Ginsburg, Michael Houston, Oleksii Kuchaiev, Ganesh Venkatesh, and Hao Wu · 2018
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Large scale GAN training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2019
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Neural caption generation over figures
Charles Chen, Ruiyi Zhang, Sungchul Kim, Scott Cohen, Tong Yu, Ryan A. Rossi, and Razvan C. Bunescu · 2019
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LVIS: A dataset for large vocabulary instance segmentation
Agrim Gupta, Piotr Dollár, and Ross B. Girshick · 2019
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A learned representation for scalable vector graphics
Raphael Gontijo Lopes, David Ha, Douglas Eck, and Jonathon Shlens · 2019
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
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EED: Extended edit distance measure for machine translation
Peter Stanchev, Weiyue Wang, and Hermann Ney · 2019
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DeepSVG: A hierarchical generative network for vector graphics animation
Alexandre Carlier, Martin Danelljan, Alexandre Alahi, and Radu Timofte · 2020
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Figure captioning with relation maps for reasoning
Charles Chen, Ruiyi Zhang, Eunyee Koh, Sungchul Kim, Scott Cohen, and Ryan A. Rossi · 2020
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Scaling laws for neural language models, 2020
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020
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Unicoder-VL: A universal encoder for vision and language by cross-modal pre-training
Gen Li, Nan Duan, Yuejian Fang, Ming Gong, and Daxin Jiang · 2020
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A three sample hypothesis test for evaluating generative models
Casey Meehan, Kamalika Chaudhuri, and Sanjoy Dasgupta · 2020
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Evaluating large language models trained on code, 2021
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhail Pavlov, Alethea Power, Lukasz Kaiser, Mohammad Bavarian, Clemens Winter, Philippe Tillet, Felipe Petroski Such, Dave Cummings, Matthias Plappert, Fotios Chantzis, Elizabeth Barnes, Ariel Herbert-Voss, William Hebgen Guss, Alex Nichol, Alex Paino, Nikolas Tezak, Jie Tang, Igor Babuschkin, Suchir Balaji, Shantanu Jain, William Saunders, Christopher Hesse, Andrew N. Carr, Jan Leike, Josh Achiam, Vedant Misra, Evan Morikawa, Alec Radford, Matthew Knight, Miles Brundage, Mira Murati, Katie Mayer, Peter Welinder, Bob McGrew, Dario Amodei, Sam McCandlish, Ilya Sutskever, and Wojciech Zaremba · 2021
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CogView: Mastering text-to-image generation via transformers
Ming Ding, Zhuoyi Yang, Wenyi Hong, Wendi Zheng, Chang Zhou, Da Yin, Junyang Lin, Xu Zou, Zhou Shao, Hongxia Yang, and Jie Tang · 2021
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Taming transformers for high-resolution image synthesis
Patrick Esser, Robin Rombach, and Björn Ommer · 2021
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Experts, errors, and context: A large-scale study of human evaluation for machine translation
Markus Freitag, George Foster, David Grangier, Viresh Ratnakar, Qijun Tan, and Wolfgang Macherey · 2021
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Leveraging large language models for scalable vector graphics-driven image understanding, 2023
Mu Cai, Zeyi Huang, Yuheng Li, Haohan Wang, and Yong Jae Lee · 2023
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Quantifying memorization across neural language models
Nicholas Carlini, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Florian Tramèr, and Chiyuan Zhang · 2023
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Muse: Text-to-image generation via masked generative transformers
Huiwen Chang, Han Zhang, Jarred Barber, Aaron Maschinot, José Lezama, Lu Jiang, Ming-Hsuan Yang, Kevin Patrick Murphy, William T. Freeman, Michael Rubinstein, Yuanzhen Li, and Dilip Krishnan · 2023
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Reproducible scaling laws for contrastive language-image learning
Mehdi Cherti, Romain Beaumont, Ross Wightman, Mitchell Wortsman, Gabriel Ilharco, Cade Gordon, Christoph Schuhmann, Ludwig Schmidt, and Jenia Jitsev · 2023
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InstructBLIP: Towards general-purpose vision-language models with instruction tuning, 2023
Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale Fung, and Steven Hoi · 2023
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CLIPScore: A reference-free evaluation metric for image captioning
Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi · 2021
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SciCap: Generating captions for scientific figures
Ting-Yao Hsu, C Lee Giles, and Ting-Hao Huang · 2021
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The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever · 2021
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Zero-shot text-to-image generation
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever · 2021
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PICARD: Parsing incrementally for constrained auto-regressive decoding from language models
Torsten Scholak, Nathan Schucher, and Dzmitry Bahdanau · 2021
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SVG vector font generation for chinese characters with transformer
Haruka Aoki and Kiyoharu Aizawa · 2022
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Efficient training of language models to fill in the middle, 2022
Mohammad Bavarian, Heewoo Jun, Nikolas Tezak, John Schulman, Christine McLeavey, Jerry Tworek, and Mark Chen · 2022
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QLoRA: Efficient finetuning of quantized llms, 2023
Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer · 2023
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CrystalBLEU: Precisely and efficiently measuring the similarity of code
Aryaz Eghbali and Michael Pradel · 2023
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InCoder: A generative model for code infilling and synthesis
Daniel Fried, Armen Aghajanyan, Jessy Lin, Sida Wang, Eric Wallace, Freda Shi, Ruiqi Zhong, Scott Yih, Luke Zettlemoyer, and Mike Lewis · 2023
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Benchmarking spatial relationships in text-to-image generation, 2023
Tejas Gokhale, Hamid Palangi, Besmira Nushi, Vibhav Vineet, Eric Horvitz, Ece Kamar, Chitta Baral, and Yezhou Yang · 2023
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The diminishing returns of masked language models to science
Zhi Hong, Aswathy Ajith, James Pauloski, Eamon Duede, Kyle Chard, and Ian Foster · 2023
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SCITUNE: Aligning large language models with scientific multimodal instructions, 2023
Sameera Horawalavithana, Sai Munikoti, Ian Stewart, and Henry Kvinge · 2023
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Summaries as captions: Generating figure captions for scientific documents with automated text summarization
Chieh-Yang Huang, Ting-Yao Hsu, Ryan A. Rossi, Ani Nenkova, Sungchul Kim, Gromit Yeuk-Yin Chan, Eunyee Koh, C. Lee Giles, and Ting-Hao (Kenneth) Huang · 2023
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VectorFusion: Text-to-SVG by abstracting pixel-based diffusion models
Ajay Jain, Amber Xie, and Pieter Abbeel · 2023
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Scaling up GANs for text-to-image synthesis
Minguk Kang, Jun-Yan Zhu, Richard Zhang, Jaesik Park, Eli Shechtman, Sylvain Paris, and Taesung Park · 2023
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StarCoder: may the source be with you!, 2023
Raymond Li, Loubna Ben Allal, Yangtian Zi, Niklas Muennighoff, Denis Kocetkov, Chenghao Mou, Marc Marone, Christopher Akiki, Jia Li, Jenny Chim, Qian Liu, Evgenii Zheltonozhskii, Terry Yue Zhuo, Thomas Wang, Olivier Dehaene, Mishig Davaadorj, Joel Lamy-Poirier, João Monteiro, Oleh Shliazhko, Nicolas Gontier, Nicholas Meade, Armel Zebaze, Ming-Ho Yee, Logesh Kumar Umapathi, Jian Zhu, Benjamin Lipkin, Muhtasham Oblokulov, Zhiruo Wang, Rudra Murthy, Jason Stillerman, Siva Sankalp Patel, Dmitry Abulkhanov, Marco Zocca, Manan Dey, Zhihan Zhang, Nour Fahmy, Urvashi Bhattacharyya, Wenhao Yu, Swayam Singh, Sasha Luccioni, Paulo Villegas, Maxim Kunakov, Fedor Zhdanov, Manuel Romero, Tony Lee, Nadav Timor, Jennifer Ding, Claire Schlesinger, Hailey Schoelkopf, Jan Ebert, Tri Dao, Mayank Mishra, Alex Gu, Jennifer Robinson, Carolyn Jane Anderson, Brendan Dolan-Gavitt, Danish Contractor, Siva Reddy, Daniel Fried, Dzmitry Bahdanau, Yacine Jernite, Carlos Muñoz Ferrandis, Sean Hughes, Thomas Wolf, Arjun Guha, Leandro von Werra, and Harm de Vries · 2023
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Visual instruction tuning
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee · 2023
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How much do language models copy from their training data? evaluating linguistic novelty in text generation using RAVEN
R. Thomas McCoy, Paul Smolensky, Tal Linzen, Jianfeng Gao, and Asli Celikyilmaz · 2023
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GPT-4 technical report, 2023
OpenAI · 2023
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FigGen: Text to scientific figure generation
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Code LLaMA: Open foundation models for code, 2023
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The TikZ and PGF Packages , 2023
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