Fetching the paper…
Reading the bibliography…
Prompting interfaces allow users to quickly adjust the output of generative models in both vision and language.
“Language models are few-shot learners”
Tom Brown et al · 1901
Earlier work this paper cites.
“Gaussian processes in machine learning”
Carl Rasmussen · 2003
Earlier work this paper cites.
“Adversarial classification”
Nilesh Dalvi, Pedro Domingos, Mausam, Sumit Sanghai and Deepak Verma · 2004
Earlier work this paper cites.
“Language Models are Few-Shot Learners”
Tom. Brown et al · 2005
Earlier work this paper cites.
“Adversarial learning”
Daniel Lowd and Christopher Meek · 2005
Earlier work this paper cites.
“Good Word Attacks on Statistical Spam Filters.”
Daniel Lowd and Christopher Meek · 2005
Earlier work this paper cites.
“Practical Bayesian optimization of machine learning algorithms”
Jasper Snoek, Hugo Larochelle and Ryan Adams · 2012
Earlier work this paper cites.
“Evasion attacks against machine learning at test time”
Battista Biggio, Igino Corona, Davide Maiorca, Blaine Nelson, Nedim Šrndić, Pavel Laskov, Giorgio Giacinto and Fabio Roli · 2013
Earlier work this paper cites.
“Intriguing properties of neural networks”
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow and Rob Fergus · 2013
Earlier work this paper cites.
“Explaining and harnessing adversarial examples”
Ian Goodfellow, Jonathon Shlens and Christian Szegedy · 2014
Earlier work this paper cites.
“TorchVision: PyTorch’s Computer Vision library”
TorchVision maintainers and contributors · 2016
Earlier work this paper cites.
“Adversarial training methods for semi-supervised text classification”
Takeru Miyato, Andrew Dai and Ian Goodfellow · 2016
Earlier work this paper cites.
“Simple black-box adversarial perturbations for deep networks”
Nina Narodytska and Shiva Kasiviswanathan · 2016
Earlier work this paper cites.
“Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition”
Mahmood Sharif, Sruti Bhagavatula, Lujo Bauer and Michael. Reiter · 2016
Earlier work this paper cites.
“Decision-based adversarial attacks: Reliable attacks against black-box machine learning models”
Wieland Brendel, Jonas Rauber and Matthias Bethge · 2017
Earlier work this paper cites.
“Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models”
Pin-Yu Chen, Huan Zhang, Yash Sharma, Jinfeng Yi and Cho-Jui Hsieh · 2017
Earlier work this paper cites.
“A rotation and a translation suffice: Fooling cnns with simple transformations”
Logan Engstrom, Dimitris Tsipras, Ludwig Schmidt and Aleksander Madry · 2017
Earlier work this paper cites.
“Practical black-box attacks against machine learning”
Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z Celik and Ananthram Swami · 2017
Earlier work this paper cites.
“Generating natural language adversarial examples”
Moustafa Alzantot, Yash Sharma, Ahmed Elgohary, Bo-Jhang Ho, Mani Srivastava and Kai-Wei Chang · 2018
Earlier work this paper cites.
“Wild patterns: Ten years after the rise of adversarial machine learning”
Battista Biggio and Fabio Roli · 2018
Earlier work this paper cites.
“Shapeshifter: Robust physical adversarial attack on faster r-cnn object detector”
Shang-Tse Chen, Cory Cornelius, Jason Martin and Duen Chau · 2018
Earlier work this paper cites.
“Query-efficient hard-label black-box attack: An optimization-based approach”
Minhao Cheng, Thong Le, Pin-Yu Chen, Jinfeng Yi, Huan Zhang and Cho-Jui Hsieh · 2018
Earlier work this paper cites.
“Physical Adversarial Examples for Object Detectors”
Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Florian Tramer, Atul Prakash, Tadayoshi Kohno and Dawn Song · 2018
Earlier work this paper cites.
“A tutorial on Bayesian optimization”
Peter Frazier · 2018
Earlier work this paper cites.
“Gpytorch: Blackbox matrix-matrix Gaussian process inference with gpu acceleration”
Jacob Gardner, Geoff Pleiss, David Bindel, Kilian Weinberger and Andrew Wilson · 2018
Earlier work this paper cites.
“Black-box adversarial attacks with limited queries and information”
Andrew Ilyas, Logan Engstrom, Anish Athalye and Jessy Lin · 2018
Cited alongside, same era.
“Prior convictions: Black-box adversarial attacks with bandits and priors”
Andrew Ilyas, Logan Engstrom and Aleksander Madry · 2018
Cited alongside, same era.
“Adversarial example generation with syntactically controlled paraphrase networks”
Mohit Iyyer, John Wieting, Kevin Gimpel and Luke Zettlemoyer · 2018
Cited alongside, same era.
Brenden. Lake and Marco Baroni · 2018
Cited alongside, same era.
“Evaluating large language models trained on code”
Mark Chen et al · 2021
Later among the works it cites.
“High-dimensional Bayesian optimization with sparse axis-aligned subspaces”
David Eriksson and Martin Jankowiak · 2021
Later among the works it cites.
“Classifier-Free Diffusion Guidance”
Jonathan Ho and Tim Salimans · 2021
Later among the works it cites.
“What Makes Good In-Context Examples for GPT- 3 3 ?”
Jiachang Liu, Dinghan Shen, Yizhe Zhang, Bill Dolan, Lawrence Carin and Weizhu Chen · 2021
Later among the works it cites.
“Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity”
Yao Lu, Max Bartolo, Alastair Moore, Sebastian Riedel and Pontus Stenetorp · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Xin Liu, Huanrui Yang, Ziwei Liu, Linghao Song, Hai Li and Yiran Chen · 2018
Cited alongside, same era.
“SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference”
Rowan Zellers, Yonatan Bisk, Roy Schwartz and Yejin Choi · 2018
Cited alongside, same era.
“Scalable Global Optimization via Local Bayesian Optimization”
David Eriksson, Michael Pearce, Jacob Gardner, Ryan Turner and Matthias Poloczek · 2019
Cited alongside, same era.
“Simple black-box adversarial attacks”
Chuan Guo, Jacob Gardner, Yurong You, Andrew Wilson and Kilian Weinberger · 2019
Cited alongside, same era.
“Certified robustness to adversarial word substitutions”
Robin Jia, Aditi Raghunathan, Kerem Göksel and Percy Liang · 2019
Cited alongside, same era.
“Parsimonious black-box adversarial attacks via efficient combinatorial optimization”
Seungyong Moon, Gaon An and Hyun Song · 2019
Cited alongside, same era.
“Language Models are Unsupervised Multitask Learners”, 2019
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei and Ilya Sutskever · 2019
Cited alongside, same era.
“Fooling automated surveillance cameras: adversarial patches to attack person detection”
Simen Thys, Wiebe Van and Toon Goedemé · 2019
Cited alongside, same era.
“Learning Transferable Visual Models From Natural Language Supervision”
Alec Radford et al · 2021
Later among the works it cites.
“Zero-shot text-to-image generation”
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen and Ilya Sutskever · 2021
Later among the works it cites.
Stephen. Bach et al · 2022
Later among the works it cites.
“PaLM: Scaling Language Modeling with Pathways”, 2022
Aakanksha Chowdhery et al · 2022
Later among the works it cites.
“Diffedit: Diffusion-based semantic image editing with mask guidance”
Guillaume Couairon, Jakob Verbeek, Holger Schwenk and Matthieu Cord · 2022
Later among the works it cites.
“Discovering the Hidden Vocabulary of DALLE-2”
Giannis Daras and Alexandros Dimakis · 2022
Later among the works it cites.
“Imagic: Text-based real image editing with diffusion models”
Bahjat Kawar, Shiran Zada, Oran Lang, Omer Tov, Huiwen Chang, Tali Dekel, Inbar Mosseri and Michal Irani · 2022
Later among the works it cites.
“Ignore Previous Prompt: Attack Techniques For Language Models”
Fábio Perez and Ian Ribeiro · 2022
Later among the works it cites.
“Hierarchical Text-Conditional Image Generation with CLIP Latents”, 2022
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu and Mark Chen · 2022
Later among the works it cites.
“High-Resolution Image Synthesis With Latent Diffusion Models”
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser and Björn Ommer · 2022
Later among the works it cites.
“High-resolution image synthesis with latent diffusion models”
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser and Björn Ommer · 2022
Later among the works it cites.
“Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding”, 2022
Chitwan Saharia et al · 2022
Later among the works it cites.
“Exploring the Universal Vulnerability of Prompt-based Learning Paradigm”
Lei Xu, Yangyi Chen, Ganqu Cui, Hongcheng Gao and Zhiyuan Liu · 2022
Later among the works it cites.
“Wordcraft: story writing with large language models”
Ann Yuan, Andy Coenen, Emily Reif and Daphne Ippolito · 2022
Later among the works it cites.
“GPT4All: Training an Assistant-style Chatbot with Large Scale Data Distillation from GPT-3.5-Turbo”
Yuvanesh Anand, Zach Nussbaum, Brandon Duderstadt, Benjamin Schmidt and Andriy Mulyar · 2023
Closest in time.
“Vicuna: An Open-Source Chatbot Impressing GPT-4 with 90%* ChatGPT Quality”, 2023
Wei-Lin Chiang et al · 2023
Closest in time.
“Bayesian Optimization” to appear
Roman Garnett · 2023
Closest in time.
“OpenAssistant Conversations – Democratizing Large Language Model Alignment”, 2023
Andreas Köpf et al · 2023
Closest in time.
“Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing”
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi and Graham Neubig · 2023
Closest in time.
“SneakyPrompt: Evaluating Robustness of Text-to-image Generative Models’ Safety Filters”
Yuchen Yang, Bo Hui, Haolin Yuan, Neil Gong and Yinzhi Cao · 2023
Closest in time.