“Interactive-Chain-Prompting (INTERCPT): Ambiguity Resolution for Crosslingual Conditional Generation With Interaction”, Pilault Et Al 2023

“Interactive-Chain-Prompting (INTERCPT): Ambiguity Resolution for Crosslingual Conditional Generation with Interaction”⁠, Jonathan Pilault, Xavier Garcia, Arthur Bražinskas, Orhan Firat2023-01-24 (inner monologue (AI)⁠, PaLM⁠, emergence (AI); similar)

“The Semantic Scholar Open Data Platform”, Kinney Et Al 2023

“The Semantic Scholar Open Data Platform”⁠, Rodney Kinney, Chloe Anastasiades, Russell Authur, Iz Beltagy, Jonathan Bragg, Alexandra Buraczynski2023-01-24 (similar)

“How Close Is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection”, Guo Et Al 2023

“How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection”⁠, Biyang Guo, Xin Zhang, Ziyuan Wang, Minqi Jiang, Jinran Nie, Yuxuan Ding, Jianwei Yue, Yupeng Wu2023-01-18 (GPT non-fiction; similar)

“Med-PaLM: Large Language Models Encode Clinical Knowledge”, Singhal Et Al 2022

“Med-PaLM: Large Language Models Encode Clinical Knowledge”⁠, Karan Singhal, Shekoofeh Azizi, Tao Tu, S. Sara Mahdavi, Jason Wei, Hyung Won Chung, Nathan Scales, Ajay Tanwani2022-12-26 (instruct-tuning LM⁠, PaLM⁠, biology; similar; bibliography)

“Unnatural Instructions: Tuning Language Models With (Almost) No Human Labor”, Honovich Et Al 2022

“Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor”⁠, Or Honovich, Thomas Scialom, Omer Levy, Timo Schick2022-12-19 (instruct-tuning LM⁠, T5 Transformer⁠, meta-learning; similar)

“A Whack-A-Mole Dilemma: Shortcuts Come in Multiples Where Mitigating One Amplifies Others”, Li Et Al 2022

“A Whack-A-Mole Dilemma: Shortcuts Come in Multiples Where Mitigating One Amplifies Others”⁠, Zhiheng Li, Ivan Evtimov, Albert Gordo, Caner Hazirbas, Tal Hassner, Cristian Canton Ferrer, Chenliang Xu2022-12-09 (CLIP; similar)

“The Stack: 3 TB of Permissively Licensed Source Code”, Kocetkov Et Al 2022

“The Stack: 3 TB of permissively licensed source code”⁠, Denis Kocetkov, Raymond Li, Loubna Ben Allal, Jia Li, Chenghao Mou, Carlos Muñoz Ferrandis, Yacine Jernite2022-11-20 (Codex⁠, Python; similar; bibliography)

“UniSumm: Unified Few-shot Summarization With Multi-Task Pre-Training and Prefix-Tuning”, Chen Et Al 2022

“UniSumm: Unified Few-shot Summarization with Multi-Task Pre-Training and Prefix-Tuning”⁠, Yulong Chen, Yang Liu, Ruochen Xu, Ziyi Yang, Chenguang Zhu, Michael Zeng, Yue Zhang2022-11-17 (Transformer NN; similar)

“A Creative Industry Image Generation Dataset Based on Captions”, Yuejia Et Al 2022

“A Creative Industry Image Generation Dataset Based on Captions”⁠, Xiang Yuejia, Lv Chuanhao, Liu Qingdazhu, Yang Xiaocui, Liu Bo, Ju Meizhi2022-11-16 (Danbooru AI; similar)

“AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities”, Chen Et Al 2022

“AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities”⁠, Zhongzhi Chen, Guang Liu, Bo-Wen Zhang, Fulong Ye, Qinghong Yang, Ledell Wu2022-11-12 (CLIP; similar; bibliography)

“MMDialog: A Large-scale Multi-turn Dialogue Dataset Towards Multi-modal Open-domain Conversation”, Feng Et Al 2022

“MMDialog: A Large-scale Multi-turn Dialogue Dataset Towards Multi-modal Open-domain Conversation”⁠, Jiazhan Feng, Qingfeng Sun, Can Xu, Pu Zhao, Yaming Yang, Chongyang Tao, Dongyan Zhao, Qingwei Lin2022-11-10 (AI scaling; similar)

“AnimeRun: 2D Animation Visual Correspondence from Open Source 3D Movies”, Siyao Et Al 2022

“AnimeRun: 2D Animation Visual Correspondence from Open Source 3D Movies”⁠, Li Siyao, Yuhang Li, Bo Li, Chao Dong, Ziwei Liu, Chen Change Loy2022-11-10 (anime AI⁠, video analysis⁠, video generation; similar)

“BLOOMZ/​mT0: Crosslingual Generalization through Multitask Finetuning”, Muennighoff Et Al 2022

“BLOOMZ/mT0: Crosslingual Generalization through Multitask Finetuning”⁠, Niklas Muennighoff, Thomas Wang, Lintang Sutawika, Adam Roberts, Stella Biderman, Teven Le Scao, M. Saiful Bari2022-11-03 (instruct-tuning LM⁠, T5 Transformer⁠, meta-learning; similar)

“Dungeons and Data: A Large-Scale NetHack Dataset”, Hambro Et Al 2022

“Dungeons and Data: A Large-Scale NetHack Dataset”⁠, Eric Hambro, Roberta Raileanu, Danielle Rothermel, Vegard Mella, Tim Rocktäschel, Heinrich Küttler, Naila Murray2022-11-01 (Nethack AI; similar)

“Will We Run out of Data? An Analysis of the Limits of Scaling Datasets in Machine Learning”, Villalobos Et Al 2022

“Will we run out of data? An analysis of the limits of scaling datasets in Machine Learning”⁠, Pablo Villalobos, Jaime Sevilla, Lennart Heim, Tamay Besiroglu, Marius Hobbhahn, Anson Ho2022-10-26 (AI scaling; similar)

“Large Language Models Can Self-Improve”, Huang Et Al 2022

“Large Language Models Can Self-Improve”⁠, Jiaxin Huang, Shixiang Shane Gu, Le Hou, Yuexin Wu, Xuezhi Wang, Hongkun Yu, Jiawei Han2022-10-20 (knowledge distillation⁠, inner monologue (AI)⁠, PaLM; similar; bibliography)

“CARP: Robust Preference Learning for Storytelling via Contrastive Reinforcement Learning”, Castricato Et Al 2022

“CARP: Robust Preference Learning for Storytelling via Contrastive Reinforcement Learning”⁠, Louis Castricato, Alexander Havrilla, Shahbul, Matiana, Michael Pieler, Anbang Ye, Ian Yang, Spencer Frazier2022-10-14 (GPT fiction⁠, preference learning; similar; bibliography)

“MTEB: Massive Text Embedding Benchmark”, Muennighoff Et Al 2022

“MTEB: Massive Text Embedding Benchmark”⁠, Niklas Muennighoff, Nouamane Tazi, Loïc Magne, Nils Reimers2022-10-13 (GPT; similar)

“Most Language Models Can Be Poets Too: An AI Writing Assistant and Constrained Text Generation Studio”, Roush Et Al 2022

“Most Language Models can be Poets too: An AI Writing Assistant and Constrained Text Generation Studio”⁠, Allen Roush, Sanjay Basu, Akshay Moorthy, Dmitry Dubovoy2022-10-12 (NN sampling⁠, LM tokenization⁠, GPT poetry; backlinks; similar; bibliography)

“Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning”, Lu Et Al 2022

“Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning”⁠, Pan Lu, Liang Qiu, Kai-Wei Chang, Ying Nian Wu, Song-Chun Zhu, Tanmay Rajpurohit, Peter Clark, Ashwin Kalyan2022-09-29 (inner monologue (AI)⁠, math⁠, model-free RL; similar)

“Brain Imaging Generation With Latent Diffusion Models”, Pinaya Et Al 2022

“Brain Imaging Generation with Latent Diffusion Models”⁠, Walter H. L. Pinaya, Petru-Daniel Tudosiu, Jessica Dafflon, Pedro F. da Costa, Virginia Fernandez, Parashkev Nachev2022-09-15 (diffusion model⁠, neuroscience; backlinks; similar)

“PaLI: A Jointly-Scaled Multilingual Language-Image Model”, Chen Et Al 2022

“PaLI: A Jointly-Scaled Multilingual Language-Image Model”⁠, Xi Chen, Xiao Wang, Soravit Changpinyo, A. J. Piergiovanni, Piotr Padlewski, Daniel Salz, Sebastian Goodman2022-09-14 (T5 Transformer⁠, AI scaling; similar)

“FOLIO: Natural Language Reasoning With First-Order Logic”, Han Et Al 2022

“FOLIO: Natural Language Reasoning with First-Order Logic”⁠, Simeng Han, Hailey Schoelkopf, Yilun Zhao, Zhenting Qi, Martin Riddell, Luke Benson, Lucy Sun, Ekaterina Zubova2022-09-02 (inner monologue (AI)⁠, philosophy/logic; similar; bibliography)

“Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned”, Ganguli Et Al 2022

“Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned”⁠, Deep Ganguli, Liane Lovitt, Jackson Kernion, Amanda Askell, Yuntao Bai, Saurav Kadavath, Ben Mann, Ethan Perez2022-08-25 (NN sampling⁠, GPT⁠, preference learning⁠, AI safety⁠, RL scaling; similar; bibliography)

“Bugs in the Data: How ImageNet Misrepresents Biodiversity”, Luccioni & Rolnick2022

“Bugs in the Data: How ImageNet Misrepresents Biodiversity”⁠, Alexandra Sasha Luccioni, David Rolnick2022-08-24 (neural net; similar)

“Discovering Bugs in Vision Models Using Off-the-shelf Image Generation and Captioning”, Wiles Et Al 2022

“Discovering Bugs in Vision Models using Off-the-shelf Image Generation and Captioning”⁠, Olivia Wiles, Isabela Albuquerque, Sven Gowal2022-08-18 (neural net/adversarial⁠, DALL·E; similar; bibliography)

“Benchmarking Compositionality With Formal Languages”, Valvoda Et Al 2022

“Benchmarking Compositionality with Formal Languages”⁠, Josef Valvoda, Naomi Saphra, Jonathan Rawski, Adina Williams, Ryan Cotterell2022-08-17 (RNN; similar)

“Quality Not Quantity: On the Interaction between Dataset Design and Robustness of CLIP”, Nguyen Et Al 2022

“Quality Not Quantity: On the Interaction between Dataset Design and Robustness of CLIP”⁠, Thao Nguyen, Gabriel Ilharco, Mitchell Wortsman, Sewoong Oh, Ludwig Schmidt2022-08-10 (CLIP⁠, AI scaling; similar; bibliography)

“Few-shot Adaptation Works With UnpredicTable Data”, Chan Et Al 2022

“Few-shot Adaptation Works with UnpredicTable Data”⁠, Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez2022-08-01 (instruct-tuning LM⁠, meta-learning; similar)

“Language Models Can Teach Themselves to Program Better”, Haluptzok Et Al 2022

“Language Models Can Teach Themselves to Program Better”⁠, Patrick Haluptzok, Matthew Bowers, Adam Tauman Kalai2022-07-29 (Codex⁠, inner monologue (AI); similar)

“NewsStories: Illustrating Articles With Visual Summaries”, Tan Et Al 2022

“NewsStories: Illustrating articles with visual summaries”⁠, Reuben Tan, Bryan A. Plummer, Kate Saenko, J. P. Lewis, Avneesh Sud, Thomas Leung2022-07-26 (retrieval AI⁠, CLIP; similar; bibliography)

“CelebV-HQ: A Large-Scale Video Facial Attributes Dataset”, Zhu Et Al 2022

“CelebV-HQ: A Large-Scale Video Facial Attributes Dataset”⁠, Hao Zhu, Wayne Wu, Wentao Zhu, Liming Jiang, Siwei Tang, Li Zhang, Ziwei Liu, Chen Change Loy2022-07-25 (StyleGAN⁠, video generation; similar)

“Why Do Tree-based Models Still Outperform Deep Learning on Tabular Data?”, Grinsztajn Et Al 2022

“Why do tree-based models still outperform deep learning on tabular data?”⁠, Léo Grinsztajn, Edouard Oyallon, Gaël Varoquaux2022-07-18 (MLP NN⁠, Transformer NN⁠, AI scaling⁠, tabular ML; similar)

“Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset”, Henderson Et Al 2022

“Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset”⁠, Peter Henderson, Mark S. Krass, Lucia Zheng, Neel Guha, Christopher D. Manning, Dan Jurafsky, Daniel E. Ho2022-07-01 (law; similar)

“RST: ReStructured Pre-training”, Yuan & Liu2022

“RST: reStructured Pre-training”⁠, Weizhe Yuan, Pengfei Liu2022-06-22 (instruct-tuning LM⁠, T5 Transformer⁠, AI scaling; similar)

“Learning to Generate Artistic Character Line Drawing”, Fang Et Al 2022

“Learning to Generate Artistic Character Line Drawing”⁠, Cheng-Yu Fang, Xian-Feng Han, Qi Zhong, Shi-Jie Sun, Guo-Qiang Xiao2022-06-07 (anime AI⁠, GAN; similar)

“Dataset Condensation via Efficient Synthetic-Data Parameterization”, Kim Et Al 2022

“Dataset Condensation via Efficient Synthetic-Data Parameterization”⁠, Jang-Hyun Kim, Jinuk Kim, Seong Joon Oh, Sangdoo Yun, Hwanjun Song, Joonhyun Jeong, Jung-Woo Ha, Hyun Oh Song2022-05-30 (knowledge distillation; similar)

“Fine-grained Image Captioning With CLIP Reward”, Cho Et Al 2022

“Fine-grained Image Captioning with CLIP Reward”⁠, Jaemin Cho, Seunghyun Yoon, Ajinkya Kale, Franck Dernoncourt, Trung Bui, Mohit Bansal2022-05-26 (CLIP⁠, model-free RL; similar)

“InstructDial: Improving Zero and Few-shot Generalization in Dialogue through Instruction Tuning”, Gupta Et Al 2022

“InstructDial: Improving Zero and Few-shot Generalization in Dialogue through Instruction Tuning”⁠, Prakhar Gupta, Cathy Jiao, Yi-Ting Yeh, Shikib Mehri, Maxine Eskenazi, Jeffrey P. Bigham2022-05-25 (instruct-tuning LM⁠, AI scaling; similar)

“Flexible Diffusion Modeling of Long Videos”, Harvey Et Al 2022

“Flexible Diffusion Modeling of Long Videos”⁠, William Harvey, Saeid Naderiparizi, Vaden Masrani, Christian Weilbach, Frank Wood2022-05-23 (diffusion model⁠, video generation⁠, model-based RL⁠, RL scaling; similar)

“Instruction Induction: From Few Examples to Natural Language Task Descriptions”, Honovich Et Al 2022

“Instruction Induction: From Few Examples to Natural Language Task Descriptions”⁠, Or Honovich, Uri Shaham, Samuel R. Bowman, Omer Levy2022-05-22 (inner monologue (AI)⁠, meta-learning⁠, RL scaling; similar)

“Housekeep: Tidying Virtual Households Using Commonsense Reasoning”, Kant Et Al 2022

“Housekeep: Tidying Virtual Households using Commonsense Reasoning”⁠, Yash Kant, Arun Ramachandran, Sriram Yenamandra, Igor Gilitschenski, Dhruv Batra, Andrew Szot, Harsh Agrawal2022-05-22 (Transformer NN⁠, model-based RL⁠, preference learning⁠, robotics; backlinks; similar)

“Down and Across: Introducing Crossword-Solving As a New NLP Benchmark”, Kulshreshtha Et Al 2022

“Down and Across: Introducing Crossword-Solving as a New NLP Benchmark”⁠, Saurabh Kulshreshtha, Olga Kovaleva, Namrata Shivagunde, Anna Rumshisky2022-05-20 (retrieval AI⁠, text game; similar)

“Automated Crossword Solving”, Wallace Et Al 2022

“Automated Crossword Solving”⁠, Eric Wallace, Nicholas Tomlin, Albert Xu, Kevin Yang, Eshaan Pathak, Matthew Ginsberg, Dan Klein2022-05-19 (T5 Transformer⁠, text game; similar; bibliography)

“Dialog Inpainting: Turning Documents into Dialogues”, Dai Et Al 2022

“Dialog Inpainting: Turning Documents into Dialogues”⁠, Zhuyun Dai, Arun Tejasvi Chaganty, Vincent Zhao, Aida Amini, Qazi Mamunur Rashid, Mike Green, Kelvin Guu2022-05-18 (knowledge distillation⁠, inner monologue (AI)⁠, AI scaling; similar; bibliography)

“SymphonyNet: Symphony Generation With Permutation Invariant Language Model”, Liu Et Al 2022

“SymphonyNet: Symphony Generation with Permutation Invariant Language Model”⁠, Jiafeng Liu, Yuanliang Dong, Zehua Cheng, Xinran Zhang, Xiaobing Li, Feng Yu, Maosong Sun2022-05-10 (AI/music⁠, LM tokenization⁠, Transformer NN; similar)

“When Does Dough Become a Bagel? Analyzing the Remaining Mistakes on ImageNet”, Vasudevan Et Al 2022

“When does dough become a bagel? Analyzing the remaining mistakes on ImageNet”⁠, Vijay Vasudevan, Benjamin Caine, Raphael Gontijo-Lopes, Sara Fridovich-Keil, Rebecca Roelofs2022-05-09 (Transformer NN⁠, AI scaling; similar; bibliography)

“Building Machine Translation Systems for the Next Thousand Languages”, Bapna Et Al 2022

“Building Machine Translation Systems for the Next Thousand Languages”⁠, Ankur Bapna, Isaac Caswell, Julia Kreutzer, Orhan Firat, Daan van Esch, Aditya Siddhant, Mengmeng Niu2022-05-09 (NN sparsity⁠, AI scaling; similar; bibliography)

“Data Determines Distributional Robustness in Contrastive Language Image Pre-training (CLIP)”, Fang Et Al 2022

“Data Determines Distributional Robustness in Contrastive Language Image Pre-training (CLIP)”⁠, Alex Fang, Gabriel Ilharco, Mitchell Wortsman, Yuhao Wan, Vaishaal Shankar, Achal Dave, Ludwig Schmidt2022-05-03 (CLIP⁠, AI scaling; similar; bibliography)

“A Challenging Benchmark of Anime Style Recognition”, Li Et Al 2022

“A Challenging Benchmark of Anime Style Recognition”⁠, Haotang Li, Shengtao Guo, Kailin Lyu, Xiao Yang, Tianchen Chen, Jianqing Zhu, Huanqiang Zeng2022-04-29 (anime AI⁠, Transformer NN; similar)

“TK-Instruct: Benchmarking Generalization via In-Context Instructions on 1,600+ Language Tasks”, Wang Et Al 2022

“Tk-Instruct: Benchmarking Generalization via In-Context Instructions on 1,600+ Language Tasks”⁠, Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi, Yeganeh Kordi, Amirreza Mirzaei, Anjana Arunkumar2022-04-16 (instruct-tuning LM⁠, T5 Transformer⁠, meta-learning; backlinks; similar; bibliography)

“Winoground: Probing Vision and Language Models for Visio-Linguistic Compositionality”, Thrush Et Al 2022

“Winoground: Probing Vision and Language Models for Visio-Linguistic Compositionality”⁠, Tristan Thrush, Ryan Jiang, Max Bartolo, Amanpreet Singh, Adina Williams, Douwe Kiela, Candace Ross2022-04-07 (CLIP; similar)

“ByT5 Model for Massively Multilingual Grapheme-to-phoneme Conversion”, Zhu Et Al 2022

“ByT5 model for massively multilingual grapheme-to-phoneme conversion”⁠, Jian Zhu, Cong Zhang, David Jurgens2022-04-06 (T5 Transformer; similar; bibliography)

“KNN-Diffusion: Image Generation via Large-Scale Retrieval”, Ashual Et Al 2022

“KNN-Diffusion: Image Generation via Large-Scale Retrieval”⁠, Oron Ashual, Shelly Sheynin, Adam Polyak, Uriel Singer, Oran Gafni, Eliya Nachmani, Yaniv Taigman2022-04-06 (anime AI⁠, diffusion model⁠, retrieval AI; similar)

“STaR: Bootstrapping Reasoning With Reasoning”, Zelikman Et Al 2022

“STaR: Bootstrapping Reasoning With Reasoning”⁠, Eric Zelikman, Yuhuai Wu, Noah D. Goodman2022-03-28 (knowledge distillation⁠, inner monologue (AI); backlinks; similar)

“CLIP Meets GamePhysics: Towards Bug Identification in Gameplay Videos Using Zero-shot Transfer Learning”, Taesiri Et Al 2022

“CLIP meets GamePhysics: Towards bug identification in gameplay videos using zero-shot transfer learning”⁠, Mohammad Reza Taesiri, Finlay Macklon, Cor-Paul Bezemer2022-03-21 (CLIP⁠, video analysis; similar; bibliography)

“Bamboo: Building Mega-Scale Vision Dataset Continually With Human-Machine Synergy”, Zhang Et Al 2022

“Bamboo: Building Mega-Scale Vision Dataset Continually with Human-Machine Synergy”⁠, Yuanhan Zhang, Qinghong Sun, Yichun Zhou, Zexin He, Zhenfei Yin, Kun Wang, Lu Sheng, Yu Qiao, Jing Shao2022-03-15 (CLIP⁠, active learning; similar)

“Self-Distilled StyleGAN: Towards Generation from Internet Photos”, Mokady Et Al 2022

“Self-Distilled StyleGAN: Towards Generation from Internet Photos”⁠, Ron Mokady, Michal Yarom, Omer Tov, Oran Lang, Daniel Cohen-Or, Tali Dekel, Michal Irani, Inbar Mosseri2022-02-24 (StyleGAN⁠, knowledge distillation⁠, AI scaling; similar; bibliography)

“RuCLIP—new Models and Experiments: a Technical Report”, Shonenkov Et Al 2022

“RuCLIP—new models and experiments: a technical report”⁠, Alex Shonenkov, Andrey Kuznetsov, Denis Dimitrov, Tatyana Shavrina, Daniil Chesakov, Anastasia Maltseva2022-02-22 (CLIP; similar)

“Wukong: 100 Million Large-scale Chinese Cross-modal Pre-training Dataset and A Foundation Framework”, Gu Et Al 2022

“Wukong: 100 Million Large-scale Chinese Cross-modal Pre-training Dataset and A Foundation Framework”⁠, Jiaxi Gu, Xiaojun Meng, Guansong Lu, Lu Hou, Minzhe Niu, Hang Xu, Xiaodan Liang, Wei Zhang, Xin Jiang2022-02-14 (CLIP⁠, AI scaling; similar; bibliography)

“ROME: Locating and Editing Factual Associations in GPT”, Meng Et Al 2022

“ROME: Locating and Editing Factual Associations in GPT”⁠, Kevin Meng, David Bau, Alex Andonian, Yonatan Belinkov2022-02-10 (GPT; similar)

“DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generative Transformers”, Cho Et Al 2022

“DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generative Transformers”⁠, Jaemin Cho, Abhay Zala, Mohit Bansal2022-02-08 (DALL·E⁠, AI safety; similar)

“PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts”, Bach Et Al 2022

“PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts”⁠, Stephen H. Bach, Victor Sanh, Zheng-Xin Yong, Albert Webson, Colin Raffel, Nihal V. Nayak, Abheesht Sharma2022-02-02 (GPT; similar)

“StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasets”, Sauer Et Al 2022

“StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasets”⁠, Axel Sauer, Katja Schwarz, Andreas Geiger2022-02-01 (StyleGAN; backlinks; similar; bibliography)

“Can Wikipedia Help Offline Reinforcement Learning?”, Reid Et Al 2022

“Can Wikipedia Help Offline Reinforcement Learning?”⁠, Machel Reid, Yutaro Yamada, Shixiang Shane Gu2022-01-28 (CLIP⁠, GPT⁠, Decision Transformer⁠, RL scaling; similar)

“BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation”, Li Et Al 2022

“BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation”⁠, Junnan Li, Dongxu Li, Caiming Xiong, Steven Hoi2022-01-28 (CLIP⁠, video analysis; similar; bibliography)

“SWAG: Revisiting Weakly Supervised Pre-Training of Visual Perception Models”, Singh Et Al 2022

“SWAG: Revisiting Weakly Supervised Pre-Training of Visual Perception Models”⁠, Mannat Singh, Laura Gustafson, Aaron Adcock, Vinicius de Freitas Reis, Bugra Gedik, Raj Prateek Kosaraju2022-01-20 (CLIP⁠, AI scaling; similar; bibliography)

“CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities”, Lee Et Al 2022

“CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities”⁠, Mina Lee, Percy Liang, Qian Yang2022-01-18 (GPT fiction; similar)

“WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation”, Liu Et Al 2022

“WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation”⁠, Alisa Liu, Swabha Swayamdipta, Noah A. Smith, Yejin Choi2022-01-16 (neural net/adversarial⁠, GPT; similar; bibliography)

“SynthBio: A Case Study in Faster Curation of Text Datasets”, Yuan Et Al 2022

“SynthBio: A Case Study in Faster Curation of Text Datasets”⁠, Ann Yuan, Daphne Ippolito, Vitaly Nikolaev, Chris Callison-Burch, Andy Coenen, Sebastian Gehrmann2022-01-13 (LaMDA; similar)

“BigDatasetGAN: Synthesizing ImageNet With Pixel-wise Annotations”, Li Et Al 2022

“BigDatasetGAN: Synthesizing ImageNet with Pixel-wise Annotations”⁠, Daiqing Li, Huan Ling, Seung Wook Kim, Karsten Kreis, Adela Barriuso, Sanja Fidler, Antonio Torralba2022-01-12 (BigGAN; backlinks; similar; bibliography)

“ERNIE-ViLG: Unified Generative Pre-training for Bidirectional Vision-Language Generation”, Zhang Et Al 2021

“ERNIE-ViLG: Unified Generative Pre-training for Bidirectional Vision-Language Generation”⁠, Han Zhang, Weichong Yin, Yewei Fang, Lanxin Li, Boqiang Duan, Zhihua Wu, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang2021-12-31 (DALL·E⁠, autoencoder NN; similar; bibliography)

“A Fistful of Words: Learning Transferable Visual Models from Bag-of-Words Supervision”, Tejankar Et Al 2021

“A Fistful of Words: Learning Transferable Visual Models from Bag-of-Words Supervision”⁠, Ajinkya Tejankar, Ajinkya Tejankar, Bichen Wu, Saining Xie, Madian Khabsa, Hamed Pirsiavash, Hamed Firooz2021-12-27 (CLIP; similar)

“GLIDE: Towards Photorealistic Image Generation and Editing With Text-Guided Diffusion Models”, Nichol Et Al 2021

“GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models”⁠, Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever2021-12-20 (DALL·E; similar)

“WebGPT: Browser-assisted Question-answering With Human Feedback”, Nakano Et Al 2021

“WebGPT: Browser-assisted question-answering with human feedback”⁠, Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu Long Ouyang, Christina Kim, Christopher Hesse2021-12-16 (retrieval AI⁠, Codex⁠, preference learning⁠, RL scaling; similar; bibliography)

“Models in the Loop: Aiding Crowdworkers With Generative Annotation Assistants”, Bartolo Et Al 2021

“Models in the Loop: Aiding Crowdworkers with Generative Annotation Assistants”⁠, Max Bartolo, Tristan Thrush, Sebastian Riedel, Pontus Stenetorp, Robin Jia, Douwe Kiela2021-12-16 (neural net/adversarial; similar)

“FRUIT: Faithfully Reflecting Updated Information in Text”, IV Et Al 2021

“FRUIT: Faithfully Reflecting Updated Information in Text”⁠, Robert L. Logan IV, Alexandre Passos, Sameer Singh, Ming-Wei Chang2021-12-16 (T5 Transformer⁠, Wikipedia; similar)

“MAD: A Scalable Dataset for Language Grounding in Videos from Movie Audio Descriptions”, Soldan Et Al 2021

“MAD: A Scalable Dataset for Language Grounding in Videos from Movie Audio Descriptions”⁠, Mattia Soldan, Alejandro Pardo, Juan León Alcázar, Fabian Caba Heilbron, Chen Zhao, Silvio Giancola, Bernard Ghanem2021-12-01 (CLIP⁠, video analysis; similar)

“BASIC: Combined Scaling for Open-Vocabulary Image Classification”, Pham Et Al 2021

“BASIC: Combined Scaling for Open-Vocabulary Image Classification”⁠, Hieu Pham, Zihang Dai, Golnaz Ghiasi, Kenji Kawaguchi, Hanxiao Liu, Adams Wei Yu, Jiahui Yu, Yi-Ting Chen2021-11-19 (CLIP⁠, AI scaling; similar; bibliography)

“Few-Shot Self-Rationalization With Natural Language Prompts”, Marasović Et Al 2021

“Few-Shot Self-Rationalization with Natural Language Prompts”⁠, Ana Marasović, Iz Beltagy, Doug Downey, Matthew E. Peters2021-11-16 (inner monologue (AI)⁠, AI scaling; similar)

“Solving Probability and Statistics Problems by Program Synthesis”, Tang Et Al 2021

“Solving Probability and Statistics Problems by Program Synthesis”⁠, Leonard Tang, Elizabeth Ke, Nikhil Singh, Nakul Verma, Iddo Drori2021-11-16 (Codex⁠, AI scaling; backlinks; similar; bibliography)

“AnimeCeleb: Large-Scale Animation CelebHeads Dataset for Head Reenactment”, Kim Et Al 2021

“AnimeCeleb: Large-Scale Animation CelebHeads Dataset for Head Reenactment”⁠, Kangyeol Kim, Sunghyun Park, Jaeseong Lee, Sunghyo Chung, Junsoo Lee, Jaegul Choo2021-11-15 (anime AI; similar)

“LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs”, Schuhmann Et Al 2021

“LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs”⁠, Christoph Schuhmann, Richard Vencu, Romain Beaumont, Robert Kaczmarczyk, Clayton Mullis, Aarush Katta2021-11-03 (CLIP⁠, DALL·E⁠, AI scaling; similar; bibliography)

“An Explanation of In-context Learning As Implicit Bayesian Inference”, Xie Et Al 2021

“An Explanation of In-context Learning as Implicit Bayesian Inference”⁠, Sang Michael Xie, Aditi Raghunathan, Percy Liang, Tengyu Ma2021-11-03 (RNN⁠, GPT⁠, meta-learning⁠, RL scaling⁠, Bayes; backlinks; similar)

“Training Verifiers to Solve Math Word Problems”, Cobbe Et Al 2021

“Training Verifiers to Solve Math Word Problems”⁠, Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, John Schulman2021-10-27 (knowledge distillation⁠, inner monologue (AI)⁠, AI scaling⁠, math; similar)

“A Connectome of The Drosophila Central Complex Reveals Network Motifs Suitable for Flexible Navigation and Context-dependent Action Selection”, Hulse Et Al 2021

“A connectome of the Drosophila central complex reveals network motifs suitable for flexible navigation and context-dependent action selection”⁠, Brad K. Hulse, Hannah Haberkern, Romain Franconville, Daniel B. Turner-Evans, Shin-ya Takemura, Tanya Wolff2021-10-26 (RNN⁠, neuroscience; backlinks; similar; bibliography)

“HTCN: Harmonious Text Colorization Network for Visual-Textual Presentation Design”, Yang Et Al 2021

“HTCN: Harmonious Text Colorization Network for Visual-Textual Presentation Design”⁠, Xuyong Yang, Xiaobin Xu, Yaohong Huang, Nenghai Yu2021-10-22 (retrieval AI⁠, typography; similar)

“T0: Multitask Prompted Training Enables Zero-Shot Task Generalization”, Sanh Et Al 2021

“T0: Multitask Prompted Training Enables Zero-Shot Task Generalization”⁠, Victor Sanh, Albert Webson, Colin Raffel, Stephen H. Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin2021-10-15 (instruct-tuning LM⁠, T5 Transformer⁠, meta-learning⁠, RL scaling; backlinks; similar)

“MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research”, Samvelyan Et Al 2021

“MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research”⁠, Mikayel Samvelyan, Robert Kirk, Vitaly Kurin, Jack Parker-Holder, Minqi Jiang, Eric Hambro, Fabio Petroni2021-09-27 (model-free RL⁠, Nethack AI; similar)

“LAION-400-Million Open Dataset”, Schuhmann2021

“LAION-400-Million Open Dataset”⁠, Christoph Schuhmann2021-08-20 (CLIP; similar; bibliography)

“Transfer Learning for Pose Estimation of Illustrated Characters”, Chen & Zwicker2021

“Transfer Learning for Pose Estimation of Illustrated Characters”⁠, Shuhong Chen, Matthias Zwicker2021-08-04 (Danbooru AI; similar)

“Scaling Vision Transformers”, Zhai Et Al 2021

“Scaling Vision Transformers”⁠, Xiaohua Zhai, Alexander Kolesnikov, Neil Houlsby, Lucas Beyer2021-06-08 (AI scaling; similar; bibliography)

“SpeechStew: Simply Mix All Available Speech Recognition Data to Train One Large Neural Network”, Chan Et Al 2021

“SpeechStew: Simply Mix All Available Speech Recognition Data to Train One Large Neural Network”⁠, William Chan, Daniel Park, Chris Lee, Yu Zhang, Quoc Le, Mohammad Norouzi2021-04-05 (Transformer NN⁠, AI scaling; similar)

“Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks”, Northcutt Et Al 2021

“Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks”⁠, Curtis G. Northcutt, Anish Athalye, Jonas Mueller2021-03-26 (CNN; similar; bibliography)

“NaturalProofs: Mathematical Theorem Proving in Natural Language”, Welleck Et Al 2021

“NaturalProofs: Mathematical Theorem Proving in Natural Language”⁠, Sean Welleck, Jiacheng Liu, Ronan Le Bras, Hannaneh Hajishirzi, Yejin Choi, Kyunghyun Cho2021-03-24 (retrieval AI⁠, math; backlinks; similar)

“Get Your Vitamin C! Robust Fact Verification With Contrastive Evidence (VitaminC)”, Schuster Et Al 2021

“Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence (VitaminC)”⁠, Tal Schuster, Adam Fisch, Regina Barzilay2021-03-15 (retrieval AI⁠, Transformer NN⁠, Wikipedia; backlinks; similar)

“Are NLP Models Really Able to Solve Simple Math Word Problems?”, Patel Et Al 2021

“Are NLP Models really able to Solve Simple Math Word Problems?”⁠, Arkil Patel, Satwik Bhattamishra, Navin Goyal2021-03-12 (Transformer NN⁠, math; similar)

“Measuring Mathematical Problem Solving With the MATH Dataset”, Hendrycks Et Al 2021

“Measuring Mathematical Problem Solving With the MATH Dataset”⁠, Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, Jacob Steinhardt2021-03-05 (inner monologue (AI)⁠, AI scaling⁠, math; backlinks; similar)

“WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning”, Srinivasan Et Al 2021

“WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning”⁠, Krishna Srinivasan, Karthik Raman, Jiecao Chen, Michael Bendersky, Marc Najork2021-03-02 (neural net⁠, Wikipedia; similar)

“A Massive 7T FMRI Dataset to Bridge Cognitive and Computational Neuroscience”, Allen Et Al 2021

“A massive 7T fMRI dataset to bridge cognitive and computational neuroscience”⁠, Emily Jean Allen, Ghislain St-Yves, Yihan Wu, Jesse L. Breedlove, Logan T. Dowdle, Bradley Caron, Franco Pestilli2021-02-22 (neuroscience⁠, RL/brain-imitation-learning; backlinks; similar)

“Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts”, Changpinyo Et Al 2021

“Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts”⁠, Soravit Changpinyo, Piyush Sharma, Nan Ding, Radu Soricut2021-02-17 (AI scaling; similar)

“ALIGN: Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision”, Jia Et Al 2021

“ALIGN: Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision”⁠, Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc V. Le, Yunhsuan Sung, Zhen Li2021-02-11 (retrieval AI⁠, CLIP⁠, AI scaling; similar; bibliography)

“Scaling Laws for Transfer”, Hernandez Et Al 2021

“Scaling Laws for Transfer”⁠, Danny Hernandez, Jared Kaplan, Tom Henighan, Sam McCandlish2021-02-02 (GPT⁠, AI scaling; similar)

“MSR-VTT: A Large Video Description Dataset for Bridging Video and Language”, Xu Et Al 2021

“MSR-VTT: A Large Video Description Dataset for Bridging Video and Language”⁠, Jun Xu, Tao Mei, Ting Yao, Yong Rui2021-01-17 (Transformer NN⁠, video analysis; similar)

“CLIP: Connecting Text and Images: We’re Introducing a Neural Network Called CLIP Which Efficiently Learns Visual Concepts from Natural Language Supervision. CLIP Can Be Applied to Any Visual Classification Benchmark by Simply Providing the Names of the Visual Categories to Be Recognized, Similar to The”Zero-shot” Capabilities of GPT-2 and GPT-3”, Radford Et Al 2021

“CLIP: Connecting Text and Images: We’re introducing a neural network called CLIP which efficiently learns visual concepts from natural language supervision. CLIP can be applied to any visual classification benchmark by simply providing the names of the visual categories to be recognized, similar to the “zero-shot” capabilities of GPT-2 and GPT-3”⁠, Alec Radford, Ilya Sutskever, Jong Wook Kim, Gretchen Krueger, Sandhini Agarwal2021-01-05 (CLIP; backlinks; similar; bibliography)

“Learning Transferable Visual Models From Natural Language Supervision”, Radford Et Al 2021

“Learning Transferable Visual Models From Natural Language Supervision”⁠, Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry2021-01-05 (CLIP⁠, video analysis; backlinks; similar; bibliography)

“The Pile: An 800GB Dataset of Diverse Text for Language Modeling”, Gao Et Al 2021

“The Pile: An 800GB Dataset of Diverse Text for Language Modeling”⁠, Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He2021 (GPT⁠, Wikipedia; similar; bibliography)

“End-to-End Chinese Landscape Painting Creation Using Generative Adversarial Networks”, Xue2020

“End-to-End Chinese Landscape Painting Creation Using Generative Adversarial Networks”⁠, Alice Xue2020-11-11 (anime AI⁠, StyleGAN; backlinks; similar)

“Open-Domain Question Answering Goes Conversational via Question Rewriting”, Anantha Et Al 2020

“Open-Domain Question Answering Goes Conversational via Question Rewriting”⁠, Raviteja Anantha, Svitlana Vakulenko, Zhucheng Tu, Shayne Longpre, Stephen Pulman, Srinivas Chappidi2020-10-10 (neural net; similar)

“Digital Voicing of Silent Speech”, Gaddy & Klein2020

“Digital Voicing of Silent Speech”⁠, David Gaddy, Dan Klein2020-10-06 (neural net⁠, neuroscience; similar)

“The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization”, Hendrycks Et Al 2020

“The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization”⁠, Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu2020-06-29 (CNN; backlinks; similar)

“The NetHack Learning Environment”, Küttler Et Al 2020

“The NetHack Learning Environment”⁠, Heinrich Küttler, Nantas Nardelli, Alexander H. Miller, Roberta Raileanu, Marco Selvatici, Edward Grefenstette2020-06-24 (Nethack AI; similar)

“Anime Crop Datasets: Faces, Figures, & Hands”, Branwen Et Al 2020

“Anime Crop Datasets: Faces, Figures, & Hands”⁠, Gwern Branwen, Arfafax, Shawn Presser, Anonymous, Danbooru Community2020-05-10 (Danbooru AI⁠, GAN/data-augmentation⁠, StyleGAN⁠, dataset; backlinks; bibliography)

“TyDiQA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages”, Clark Et Al 2020

“TyDiQA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages”⁠, Jonathan H. Clark, Eunsol Choi, Michael Collins, Dan Garrette, Tom Kwiatkowski, Vitaly Nikolaev, Jennimaria Palomaki2020-03-10 (backlinks; similar)

“SAYCam: A Large, Longitudinal Audiovisual Dataset Recorded from the Infant’s Perspective”, Sullivan Et Al 2020

“SAYCam: A large, longitudinal audiovisual dataset recorded from the infant’s perspective”⁠, Jess Sullivan, Michelle Mei, Amy Perfors, Erica Wojcik, Michael Frank2020-01-14 (video analysis⁠, psychology; similar)

“ImageNet-A: Natural Adversarial Examples”, Hendrycks Et Al 2020

“ImageNet-A: Natural Adversarial Examples”⁠, Dan Hendrycks, Kevin Zhao, Steven Basart, Jacob Steinhardt, Dawn Song2020-01-08 (neural net; backlinks; similar)

“Libri-Light: A Benchmark for ASR With Limited or No Supervision”, Kahn Et Al 2019

“Libri-Light: A Benchmark for ASR with Limited or No Supervision”⁠, Jacob Kahn, Morgane Rivière, Weiyi Zheng, Evgeny Kharitonov, Qiantong Xu, Pierre-Emmanuel Mazaré, Julien Karadayi2019-12-17 (neural net; similar)

“How Can We Know What Language Models Know?”, Jiang Et Al 2019

“How Can We Know What Language Models Know?”⁠, Zhengbao Jiang, Frank F. Xu, Jun Araki, Graham Neubig2019-11-28 (GPT; backlinks; similar)

“Compressive Transformers for Long-Range Sequence Modelling”, Rae Et Al 2019

“Compressive Transformers for Long-Range Sequence Modelling”⁠, Jack W. Rae, Anna Potapenko, Siddhant M. Jayakumar, Timothy P. Lillicrap2019-11-13 (compressed Transformer; similar)

“T5: Exploring the Limits of Transfer Learning With a Unified Text-to-Text Transformer”, Raffel Et Al 2019

“T5: 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 Li2019-10-23 (T5 Transformer⁠, AI scaling; similar)

“CATER: A Diagnostic Dataset for Compositional Actions and TEmporal Reasoning”, Girdhar & Ramanan2019

“CATER: A diagnostic dataset for Compositional Actions and TEmporal Reasoning”⁠, Rohit Girdhar, Deva Ramanan2019-10-10 (video analysis; backlinks; similar)

“PubMedQA: A Dataset for Biomedical Research Question Answering”, Jin Et Al 2019

“PubMedQA: A Dataset for Biomedical Research Question Answering”⁠, Qiao Jin, Bhuwan Dhingra, Zhengping Liu, William W. Cohen, Xinghua Lu2019-09-13 (Transformer NN; backlinks; similar)

“ObjectNet: A Large-scale Bias-controlled Dataset for Pushing the Limits of Object Recognition Models”, Barbu Et Al 2019

“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, Danny Gutfreund, Joshua Tenenbaum2019-09-06 (CNN; backlinks; similar)

“No Press Diplomacy: Modeling Multi-Agent Gameplay”, Paquette Et Al 2019

“No Press Diplomacy: Modeling Multi-Agent Gameplay”⁠, Philip Paquette, Yuchen Lu, Steven Bocco, Max O. Smith, Satya Ortiz-Gagne, Jonathan K. Kummerfeld, Satinder Singh2019-09-04 (Diplomacy AI⁠, MARL; similar)

“LVIS: A Dataset for Large Vocabulary Instance Segmentation”, Gupta Et Al 2019

“LVIS: A Dataset for Large Vocabulary Instance Segmentation”⁠, Agrim Gupta, Piotr Dollár, Ross Girshick2019-08-08 (similar)

“A Large Single-participant FMRI Dataset for Probing Brain Responses to Naturalistic Stimuli in Space and Time”, Seeliger Et Al 2019

“A large single-participant fMRI dataset for probing brain responses to naturalistic stimuli in space and time”⁠, K. Seeliger, R. P. Sommers, U. Güçlü, S. E. Bosch, M. A. J. van Gerven2019-07-02 (neuroscience; similar)

“OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge”, Marino Et Al 2019

“OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge”⁠, Kenneth Marino, Mohammad Rastegari, Ali Farhadi, Roozbeh Mottaghi2019-05-31 (retrieval AI; similar)

“ImageNet-Sketch: Learning Robust Global Representations by Penalizing Local Predictive Power”, Wang Et Al 2019

“ImageNet-Sketch: Learning Robust Global Representations by Penalizing Local Predictive Power”⁠, Haohan Wang, Songwei Ge, Eric P. Xing, Zachary C. Lipton2019-05-29 (CNN; backlinks; similar)

“SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems”, Wang Et Al 2019

“SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems”⁠, Alex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy2019-05-02 (neural net; backlinks; similar; bibliography)

“ProductNet: a Collection of High-Quality Datasets for Product Representation Learning”, Wang Et Al 2019

“ProductNet: a Collection of High-Quality Datasets for Product Representation Learning”⁠, Chu Wang, Lei Tang, Yang Lu, Shujun Bian, Hirohisa Fujita, Da Zhang, Zuohua Zhang, Yongning Wu2019-04-18 (active learning; similar)

“Benchmarking Neural Network Robustness to Common Corruptions and Perturbations”, Hendrycks & Dietterich2019

“Benchmarking Neural Network Robustness to Common Corruptions and Perturbations”⁠, Dan Hendrycks, Thomas Dietterich2019-03-28 (neural net/adversarial⁠, CNN⁠, AI scaling; backlinks; similar)

“A Replication Study: Machine Learning Models Are Capable of Predicting Sexual Orientation From Facial Images”, Leuner2019

“A Replication Study: Machine Learning Models Are Capable of Predicting Sexual Orientation From Facial Images”⁠, John Leuner2019-02-27 (neural net⁠, psychology; backlinks; similar)

“Language Models Are Unsupervised Multitask Learners”, Radford Et Al 2019

“Language Models are Unsupervised Multitask Learners”⁠, Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever2019-02-14 (GPT⁠, AI scaling; similar)

“Do We Train on Test Data? Purging CIFAR of Near-Duplicates”, Barz & Denzler2019

“Do We Train on Test Data? Purging CIFAR of Near-Duplicates”⁠, Björn Barz, Joachim Denzler2019-02-01 (CNN; similar)

“FIGR: Few-shot Image Generation With Reptile”, Clouâtre & Demers2019

“FIGR: Few-shot Image Generation with Reptile”⁠, Louis Clouâtre, Marc Demers2019-01-08 (StyleGAN⁠, typography⁠, meta-learning; backlinks; similar)

“A Style-Based Generator Architecture for Generative Adversarial Networks”, Karras Et Al 2018

“A Style-Based Generator Architecture for Generative Adversarial Networks”⁠, Tero Karras, Samuli Laine, Timo Aila2018-12-12 (StyleGAN; similar)

“ImageNet-trained CNNs Are Biased towards Texture; Increasing Shape Bias Improves Accuracy and Robustness”, Geirhos Et Al 2018

“ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness”⁠, Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A. Wichmann, Wiel, Brendel2018-11-29 (CNN; backlinks; similar)

“The Open Images Dataset V4: Unified Image Classification, Object Detection, and Visual Relationship Detection at Scale”, Kuznetsova Et Al 2018

“The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale”⁠, Alina Kuznetsova, Hassan Rom, Neil Alldrin, Jasper Uijlings, Ivan Krasin, Jordi Pont-Tuset, Shahab Kamali2018-11-02 (active learning; similar)

“CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge”, Talmor Et Al 2018

“CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge”⁠, Alon Talmor, Jonathan Herzig, Nicholas Lourie, Jonathan Berant2018-11-02 (neural net; similar)

“Don’t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization”, Narayan Et Al 2018

“Don’t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization”⁠, Shashi Narayan, Shay B. Cohen, Mirella Lapata2018-08-27 (CNN; backlinks; similar)

“A Short Note about Kinetics-600”, Carreira Et Al 2018

“A Short Note about Kinetics-600”⁠, Joao Carreira, Eric Noland, Andras Banki-Horvath, Chloe Hillier, Andrew Zisserman2018-08-03 (video analysis; similar; bibliography)

“CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images”, Guo Et Al 2018

“CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images”⁠, Sheng Guo, Weilin Huang, Haozhi Zhang, Chenfan Zhuang, Dengke Dong, Matthew R. Scott, Dinglong Huang2018-08-03 (CNN⁠, AI scaling; backlinks; similar; bibliography)

“Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations”, Hendrycks & Dietterich2018

“Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations”⁠, Dan Hendrycks, Thomas G. Dietterich2018-07-04 (neural net/adversarial⁠, CNN⁠, AI scaling; backlinks; similar)

“Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning”, Sharma Et Al 2018

“Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning”⁠, Piyush Sharma, Nan Ding, Sebastian Goodman, Radu Soricut2018-07-01 (diffusion model⁠, DALL·E; similar; bibliography)

“BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning”, Yu Et Al 2018

“BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning”⁠, Fisher Yu, Haofeng Chen, Xin Wang, Wenqi Xian, Yingying Chen, Fangchen Liu, Vashisht Madhavan, Trevor Darrell2018-05-12 (CNN⁠, video analysis; backlinks; similar)

“Exploring the Limits of Weakly Supervised Pretraining”, Mahajan Et Al 2018

“Exploring the Limits of Weakly Supervised Pretraining”⁠, Dhruv Mahajan, Ross Girshick, Vignesh Ramanathan, Kaiming He, Manohar Paluri, Yixuan Li, Ashwin Bharambe2018-05-02 (CNN⁠, AI scaling; similar; bibliography)

“GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding”, Wang Et Al 2018

“GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding”⁠, Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, Samuel R. Bowman2018-04-20 (neural net; backlinks; similar)

“The Sound of Pixels”, Zhao Et Al 2018

“The Sound of Pixels”⁠, Hang Zhao, Chuang Gan, Andrew Rouditchenko, Carl Vondrick, Josh McDermott, Antonio Torralba2018-04-09 (AI/music⁠, video analysis; similar)

“Think You Have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge”, Clark Et Al 2018

“Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge”⁠, Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, Oyvind Tafjord2018-03-14 (neural net; similar)

“FEVER: a Large-scale Dataset for Fact Extraction and VERification”, Thorne Et Al 2018

“FEVER: a large-scale dataset for Fact Extraction and VERification”⁠, James Thorne, Andreas Vlachos, Christos Christodoulopoulos, Arpit Mittal2018-03-14 (retrieval AI; backlinks; similar)

“11K Hands: Gender Recognition and Biometric Identification Using a Large Dataset of Hand Images”, Afifi2017

“11K Hands: Gender recognition and biometric identification using a large dataset of hand images”⁠, Mahmoud Afifi2017-11-12 (CNN; backlinks; similar)

“WebVision Database: Visual Learning and Understanding from Web Data”, Li Et Al 2017

“WebVision Database: Visual Learning and Understanding from Web Data”⁠, Wen Li, Limin Wang, Wei Li, Eirikur Agustsson, Luc Van Gool2017-08-09 (CNN⁠, AI scaling; backlinks; similar)

“Revisiting Unreasonable Effectiveness of Data in Deep Learning Era”, Sun Et Al 2017

“Revisiting Unreasonable Effectiveness of Data in Deep Learning Era”⁠, Chen Sun, Abhinav Shrivastava, Saurabh Singh, Abhinav Gupta2017-07-10 (AI scaling⁠, active learning; similar)

“Driver Identification Using Automobile Sensor Data from a Single Turn”, Hallac Et Al 2017

“Driver Identification Using Automobile Sensor Data from a Single Turn”⁠, David Hallac, Abhijit Sharang, Rainer Stahlmann, Andreas Lamprecht, Markus Huber, Martin Roehder, Rok Sosic2017-06-09 (neural net; backlinks; similar)

“The Kinetics Human Action Video Dataset”, Kay Et Al 2017

“The Kinetics Human Action Video Dataset”⁠, Will Kay, Joao Carreira, Karen Simonyan, Brian Zhang, Chloe Hillier, Sudheendra Vijayanarasimhan, Fabio Viola2017-05-19 (video analysis; similar)

“Dense-Captioning Events in Videos”, Krishna Et Al 2017

“Dense-Captioning Events in Videos”⁠, Ranjay Krishna, Kenji Hata, Frederic Ren, Li Fei-Fei, Juan Carlos Niebles2017-05-02 (video analysis; backlinks; similar)

“RACE: Large-scale ReAding Comprehension Dataset From Examinations”, Lai Et Al 2017

“RACE: Large-scale ReAding Comprehension Dataset From Examinations”⁠, Guokun Lai, Qizhe Xie, Hanxiao Liu, Yiming Yang, Eduard Hovy2017-04-15 (neural net; backlinks; similar)

“Pointer Sentinel Mixture Models”, Merity Et Al 2016

“Pointer Sentinel Mixture Models”⁠, Stephen Merity, Caiming Xiong, James Bradbury, Richard Socher2016-09-26 (RNN; backlinks; similar)

“Solving General Arithmetic Word Problems”, Roy & Roth2016

“Solving General Arithmetic Word Problems”⁠, Subhro Roy, Dan Roth2016-08-04 (math; backlinks; similar)

“The LAMBADA Dataset: Word Prediction Requiring a Broad Discourse Context”, Paperno Et Al 2016

“The LAMBADA dataset: Word prediction requiring a broad discourse context”⁠, Denis Paperno, Germán Kruszewski, Angeliki Lazaridou, Quan Ngoc Pham, Raffaella Bernardi, Sandro Pezzelle2016-06-20 (AI scaling; backlinks; similar)

“Convolutional Sketch Inversion”, Güçlütürk Et Al 2016

“Convolutional Sketch Inversion”⁠, Yağmur Güçlütürk, Umut Güçlü, Rob van Lier, Marcel A. J. van Gerven2016-06-09 (neural net; similar)

“Danbooru2021: A Large-Scale Crowdsourced and Tagged Anime Illustration Dataset”, Branwen2015

“Danbooru2021: A Large-Scale Crowdsourced and Tagged Anime Illustration Dataset”⁠, Gwern Branwen2015-12-15 (Danbooru AI; backlinks; bibliography)

“Neural Module Networks”, Andreas Et Al 2015

“Neural Module Networks”⁠, Jacob Andreas, Marcus Rohrbach, Trevor Darrell, Dan Klein2015-11-09 (neural net; backlinks; similar)

“Sketch-based Manga Retrieval Using Manga109 Dataset”, Matsui Et Al 2015

“Sketch-based Manga Retrieval using Manga109 Dataset”⁠, Yusuke Matsui, Kota Ito, Yuji Aramaki, Toshihiko Yamasaki, Kiyoharu Aizawa2015-10-15 (anime AI; backlinks; similar)

“LSUN: Construction of a Large-scale Image Dataset Using Deep Learning With Humans in the Loop”, Yu Et Al 2015

“LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop”⁠, Fisher Yu, Ari Seff, Yinda Zhang, Shuran Song, Thomas Funkhouser, Jianxiong Xiao2015-06-10 (CNN⁠, AI scaling⁠, active learning; backlinks; similar)

“YFCC100M: The New Data in Multimedia Research”, Thomee Et Al 2015

“YFCC100M: The New Data in Multimedia Research”⁠, Bart Thomee, David A. Shamma, Gerald Friedland, Benjamin Elizalde, Karl Ni, Douglas Poland, Damian Borth2015-03-05 (AI scaling⁠, video analysis; similar)

“ImageNet Large Scale Visual Recognition Challenge”, Russakovsky Et Al 2014

“ImageNet Large Scale Visual Recognition Challenge”⁠, Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy2014-09-01 (CNN; backlinks; similar)

“Microsoft COCO: Common Objects in Context”, Lin Et Al 2014

“Microsoft COCO: Common Objects in Context”⁠, Tsung-Yi Lin, Michael Maire, Serge Belongie, Lubomir Bourdev, Ross Girshick, James Hays, Pietro Perona2014-05-01 (neural net; similar)

“UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild”, Soomro Et Al 2012

“UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild”⁠, Khurram Soomro, Amir Roshan Zamir, Mubarak Shah2012-12-03 (video analysis; backlinks; similar)

“Solving Math Word Problems: We’ve Trained a System That Solves Grade School Math Problems With Nearly Twice the Accuracy of a Fine-tuned GPT-3 Model. It Solves about 90% As Many Problems As Real Kids: a Small Sample of 9-12 Year Olds Scored 60% on a Test from Our Dataset, While Our System Scored 55% on Those Same Problems. This Is Important Because Today’s AI Is Still Quite Weak at Commonsense Multistep Reasoning, Which Is Easy Even for Grade School Kids. We Achieved These Results by Training Our Model to Recognize Its Mistakes, so That It Can Try Repeatedly Until It Finds a Solution That Works”

Wikipedia

1. Common Crawl

2. MNIST database

3. Netflix Prize

4. UK Biobank