“Tag2Text: Guiding Vision-Language Model via Image Tagging”, Huang Et Al 2023

“Tag2Text: Guiding Vision-Language Model via Image Tagging”⁠, Xinyu Huang, Youcai Zhang, Jinyu Ma, Weiwei Tian, Rui Feng, Yuejie Zhang, Yaqian Li, Yandong Guo, Lei Zhang2023-03-10 (Transformer NN; similar)

“ProofNet: Autoformalizing and Formally Proving Undergraduate-Level Mathematics”, Azerbayev Et Al 2023

“ProofNet: Autoformalizing and Formally Proving Undergraduate-Level Mathematics”⁠, Zhangir Azerbayev, Bartosz Piotrowski, Hailey Schoelkopf, Edward W. Ayers, Dragomir Radev, Jeremy Avigad2023-02-24 (knowledge distillation⁠, Codex⁠, dataset⁠, math; similar; bibliography)

“Characterizing Attribution and Fluency Tradeoffs for Retrieval-Augmented Large Language Models”, Aksitov Et Al 2023

“Characterizing Attribution and Fluency Tradeoffs for Retrieval-Augmented Large Language Models”⁠, Renat Aksitov, Chung-Ching Chang, David Reitter, Siamak Shakeri, Yunhsuan Sung2023-02-11 (NN sampling⁠, PaLM; similar)

“In-Context Retrieval-Augmented Language Models”, Ram Et Al 2023

“In-Context Retrieval-Augmented Language Models”⁠, Ori Ram, Yoav Levine, Itay Dalmedigos, Dor Muhlgay, Amnon Shashua, Kevin Leyton-Brown, Yoav Shoham2023-01-31 (GPT; similar)

“Crawling the Internal Knowledge-Base of Language Models”, Cohen Et Al 2023

“Crawling the Internal Knowledge-Base of Language Models”⁠, Roi Cohen, Mor Geva, Jonathan Berant, Amir Globerson2023-01-30 (GPT; similar)

“InPars-Light: Cost-Effective Unsupervised Training of Efficient Rankers”, Boytsov Et Al 2023

“InPars-Light: Cost-Effective Unsupervised Training of Efficient Rankers”⁠, Leonid Boytsov, Preksha Patel, Vivek Sourabh, Riddhi Nisar, Sayani Kundu, Ramya Ramanathan, Eric Nyberg2023-01-08 (GPT⁠, T5 Transformer; similar)

“Why Do Nearest Neighbor Language Models Work?”, Xu Et Al 2023

“Why do Nearest Neighbor Language Models Work?”⁠, Frank F. Xu, Uri Alon, Graham Neubig2023-01-07 (Transformer NN; similar)

“Precise Zero-Shot Dense Retrieval without Relevance Labels”, Gao Et Al 2022

“Precise Zero-Shot Dense Retrieval without Relevance Labels”⁠, Luyu Gao, Xueguang Ma, Jimmy Lin, Jamie Callan2022-12-20 (GPT; similar; bibliography)

“One Embedder, Any Task: Instruction-Finetuned Text Embeddings (INSTRUCTOR)”, Su Et Al 2022

“One Embedder, Any Task: Instruction-Finetuned Text Embeddings (INSTRUCTOR)”⁠, Hongjin Su, Weijia Shi, Jungo Kasai, Yizhong Wang, Yushi Hu, Mari Ostendorf, Wen-tau Yih, Noah A. Smith2022-12-19 (instruct-tuning LM⁠, T5 Transformer⁠, AI scaling; similar; bibliography)

“NPM: Nonparametric Masked Language Modeling”, Min Et Al 2022

“NPM: Nonparametric Masked Language Modeling”⁠, Sewon Min, Weijia Shi, Mike Lewis, Xilun Chen, Wen-tau Yih, Hannaneh Hajishirzi, Luke Zettlemoyer2022-12-02 (LM tokenization; similar; bibliography)

“Retrieval-Augmented Multimodal Language Modeling”, Yasunaga Et Al 2022

“Retrieval-Augmented Multimodal Language Modeling”⁠, Michihiro Yasunaga, Armen Aghajanyan, Weijia Shi, Rich James, Jure Leskovec, Percy Liang, Mike Lewis2022-11-22 (CLIP⁠, DALL·E; similar; bibliography)

“TART: Task-aware Retrieval With Instructions”, Asai Et Al 2022

“TART: Task-aware Retrieval with Instructions”⁠, Akari Asai, Timo Schick, Patrick Lewis, Xilun Chen, Gautier Izacard, Sebastian Riedel, Hannaneh Hajishirzi2022-11-16 (T5 Transformer; similar)

“RARR: Attributed Text Generation via Post-hoc Research and Revision”, Gao Et Al 2022

“RARR: Attributed Text Generation via Post-hoc Research and Revision”⁠, Luyu Gao, Zhuyun Dai, Panupong Pasupat, Anthony Chen, Arun Tejasvi Chaganty, Yicheng Fan, Vincent Y. Zhao2022-10-17 (PaLM; similar; bibliography)

“ReAct: Synergizing Reasoning and Acting in Language Models”, Yao Et Al 2022

“ReAct: Synergizing Reasoning and Acting in Language Models”⁠, Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, Yuan Cao2022-10-06 (inner monologue (AI)⁠, PaLM; similar)

“FiD-Light: Efficient and Effective Retrieval-Augmented Text Generation”, Hofstätter Et Al 2022

“FiD-Light: Efficient and Effective Retrieval-Augmented Text Generation”⁠, Sebastian Hofstätter, Jiecao Chen, Karthik Raman, Hamed Zamani2022-09-28 (T5 Transformer; similar)

“Generate rather than Retrieve (GenRead): Large Language Models Are Strong Context Generators”, Yu Et Al 2022

“Generate rather than Retrieve (GenRead): Large Language Models are Strong Context Generators”⁠, Wenhao Yu, Dan Iter, Shuohang Wang, Yichong Xu, Mingxuan Ju, Soumya Sanyal, Chenguang Zhu, Michael Zeng2022-09-21 (GPT; similar)

“Vote-K: Selective Annotation Makes Language Models Better Few-Shot Learners”, Su Et Al 2022

“Vote-K: Selective Annotation Makes Language Models Better Few-Shot Learners”⁠, Hongjin Su, Jungo Kasai, Chen Henry Wu, Weijia Shi, Tianlu Wang, Jiayi Xin, Rui Zhang, Mari Ostendorf2022-09-05 (Codex⁠, RL exploration; similar; bibliography)

“Nearest Neighbor Non-autoregressive Text Generation”, Niwa Et Al 2022

“Nearest Neighbor Non-autoregressive Text Generation”⁠, Ayana Niwa, Sho Takase, Naoaki Okazaki2022-08-26 (model-free RL; similar)

“CorpusBrain: Pre-train a Generative Retrieval Model for Knowledge-Intensive Language Tasks”, Chen Et Al 2022

“CorpusBrain: Pre-train a Generative Retrieval Model for Knowledge-Intensive Language Tasks”⁠, Jiangui Chen, Ruqing Zhang, Jiafeng Guo, Yiqun Liu, Yixing Fan, Xueqi Cheng2022-08-16 (Transformer NN; 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 (ML dataset⁠, CLIP; similar; bibliography)

“Text-Guided Synthesis of Artistic Images With Retrieval-Augmented Diffusion Models”, Rombach Et Al 2022

“Text-Guided Synthesis of Artistic Images with Retrieval-Augmented Diffusion Models”⁠, Robin Rombach, Andreas Blattmann, Björn Ommer2022-07-26 (diffusion model⁠, CLIP; backlinks; similar)

“Re2G: Retrieve, Rerank, Generate”, Glass Et Al 2022

“Re2G: Retrieve, Rerank, Generate”⁠, Michael Glass, Gaetano Rossiello, Md Faisal Mahbub Chowdhury, Ankita Rajaram Naik, Pengshan Cai, Alfio Gliozzo2022-07-13 (knowledge distillation⁠, Transformer NN; similar; bibliography)

“Large-Scale Retrieval for Reinforcement Learning”, Humphreys Et Al 2022

“Large-Scale Retrieval for Reinforcement Learning”⁠, Peter C. Humphreys, Arthur Guez, Olivier Tieleman, Laurent Sifre, Théophane Weber, Timothy Lillicrap2022-06-10 (AlphaGo⁠, MuZero; similar; bibliography)

“A Neural Corpus Indexer for Document Retrieval”, Wang Et Al 2022

“A Neural Corpus Indexer for Document Retrieval”⁠, Yujing Wang, Yingyan Hou, Haonan Wang, Ziming Miao, Shibin Wu, Hao Sun, Qi Chen, Yuqing Xia, Chengmin Chi2022-06-06 (Transformer NN⁠, AI scaling; similar)

“Boosting Search Engines With Interactive Agents”, Ciaramita Et Al 2022

“Boosting Search Engines with Interactive Agents”⁠, Massimiliano Ciaramita, Leonard Adolphs, Michelle Chen Huebscher, Sascha Rothe, Christian Buck, Thomas Hofmann2022-06-04 (T5 Transformer⁠, RL exploration⁠, Decision Transformer⁠, MuZero; similar; bibliography)

“Hopular: Modern Hopfield Networks for Tabular Data”, Schäfl Et Al 2022

“Hopular: Modern Hopfield Networks for Tabular Data”⁠, Bernhard Schäfl, Lukas Gruber, Angela Bitto-Nemling, Sepp Hochreiter2022-06-01 (tabular ML; similar)

“NaturalProver: Grounded Mathematical Proof Generation With Language Models”, Welleck Et Al 2022

“NaturalProver: Grounded Mathematical Proof Generation with Language Models”⁠, Sean Welleck, Jiacheng Liu, Ximing Lu, Hannaneh Hajishirzi, Yejin Choi2022-05-25 (GPT⁠, math; similar; bibliography)

“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 (ML dataset⁠, text game; similar)

“RankGen: Improving Text Generation With Large Ranking Models”, Krishna Et Al 2022

“RankGen: Improving Text Generation with Large Ranking Models”⁠, Kalpesh Krishna, Yapei Chang, John Wieting, Mohit Iyyer2022-05-19 (NN sampling⁠, GPT; similar)

“Unifying Language Learning Paradigms”, Tay Et Al 2022

“Unifying Language Learning Paradigms”⁠, Yi Tay, Mostafa Dehghani, Vinh Q. Tran, Xavier Garcia, Dara Bahri, Tal Schuster, Huaixiu Steven Zheng2022-05-10 (inner monologue (AI)⁠, PaLM⁠, T5 Transformer⁠, AI scaling⁠, meta-learning; similar; bibliography)

“Semi-Parametric Neural Image Synthesis”, Blattmann Et Al 2022

“Semi-Parametric Neural Image Synthesis”⁠, Andreas Blattmann, Robin Rombach, Kaan Oktay, Jonas Müller, Björn Ommer2022-04-25 (diffusion model⁠, CLIP; similar)

“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, ML dataset⁠, diffusion model; similar)

“Language Models That Seek for Knowledge: Modular Search & Generation for Dialogue and Prompt Completion”, Shuster Et Al 2022

“Language Models that Seek for Knowledge: Modular Search & Generation for Dialogue and Prompt Completion”⁠, Kurt Shuster, Mojtaba Komeili, Leonard Adolphs, Stephen Roller, Arthur Szlam, Jason Weston2022-03-24 (Transformer NN; similar; bibliography)

“Unsupervised Vision-and-Language Pre-training via Retrieval-based Multi-Granular Alignment”, Zhou Et Al 2022

“Unsupervised Vision-and-Language Pre-training via Retrieval-based Multi-Granular Alignment”⁠, Mingyang Zhou, Licheng Yu, Amanpreet Singh, Mengjiao Wang, Zhou Yu, Ning Zhang2022-03-01 (CLIP; similar)

“Retrieval Augmented Classification for Long-Tail Visual Recognition”, Long Et Al 2022

“Retrieval Augmented Classification for Long-Tail Visual Recognition”⁠, Alexander Long, Wei Yin, Thalaiyasingam Ajanthan, Vu Nguyen, Pulak Purkait, Ravi Garg, Alan Blair, Chunhua Shen2022-02-22 (similar)

“Retrieval-Augmented Reinforcement Learning”, Goyal Et Al 2022

“Retrieval-Augmented Reinforcement Learning”⁠, Anirudh Goyal, Abram L. Friesen, Andrea Banino, Theophane Weber, Nan Rosemary Ke, Adria Puigdomenech Badia2022-02-17 (RNN⁠, model-free RL; similar)

“Transformer Memory As a Differentiable Search Index”, Tay Et Al 2022

“Transformer Memory as a Differentiable Search Index”⁠, Yi Tay, Vinh Q. Tran, Mostafa Dehghani, Jianmo Ni, Dara Bahri, Harsh Mehta, Zhen Qin, Kai Hui, Zhe Zhao2022-02-14 (similar)

“InPars: Data Augmentation for Information Retrieval Using Large Language Models”, Bonifacio Et Al 2022

“InPars: Data Augmentation for Information Retrieval using Large Language Models”⁠, Luiz Bonifacio, Hugo Abonizio, Marzieh Fadaee, Rodrigo Nogueira2022-02-10 (GPT⁠, T5 Transformer; backlinks; similar)

“LaMDA: Language Models for Dialog Applications”, Thoppilan Et Al 2022

“LaMDA: Language Models for Dialog Applications”⁠, Romal Thoppilan, Daniel De Freitas, Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze Cheng, Alicia Jin2022-01-20 (LaMDA⁠, AI scaling⁠, AI safety; similar)

“Memory-assisted Prompt Editing to Improve GPT-3 After Deployment”, Madaan Et Al 2022

“Memory-assisted prompt editing to improve GPT-3 after deployment”⁠, Aman Madaan, Niket Tandon, Peter Clark, Yiming Yang2022-01-16 (GPT; similar)

“A Thousand Words Are Worth More Than a Picture: Natural Language-Centric Outside-Knowledge Visual Question Answering”, Gao Et Al 2022

“A Thousand Words Are Worth More Than a Picture: Natural Language-Centric Outside-Knowledge Visual Question Answering”⁠, Feng Gao, Qing Ping, Govind Thattai, Aishwarya Reganti, Ying Nian Wu, Prem Natarajan2022-01-14 (similar)

“WebGPT: Improving the Factual Accuracy of Language Models through Web Browsing”, Hilton Et Al 2021

“WebGPT: Improving the factual accuracy of language models through web browsing”⁠, Jacob Hilton, Suchir Balaji, Reiichiro Nakano, John Schulman2021-12-16 (Codex⁠, inner monologue (AI)⁠, preference learning⁠, RL scaling; similar; bibliography)

“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 (ML dataset⁠, Codex⁠, preference learning⁠, RL scaling; similar; bibliography)

“Contriever: Towards Unsupervised Dense Information Retrieval With Contrastive Learning”, Izacard Et Al 2021

“Contriever: Towards Unsupervised Dense Information Retrieval with Contrastive Learning”⁠, Gautier Izacard, Mathilde Caron, Lucas Hosseini, Sebastian Riedel, Piotr Bojanowski, Arm, Joulin, Edouard Grave2021-12-16 (similar; bibliography)

“Learning To Retrieve Prompts for In-Context Learning”, Rubin Et Al 2021

“Learning To Retrieve Prompts for In-Context Learning”⁠, Ohad Rubin, Jonathan Herzig, Jonathan Berant2021-12-16 (GPT; similar)

“Large Dual Encoders Are Generalizable Retrievers”, Ni Et Al 2021

“Large Dual Encoders Are Generalizable Retrievers”⁠, Jianmo Ni, Chen Qu, Jing Lu, Zhuyun Dai, Gustavo Hernández Ábrego, Ji Ma, Vincent Y. Zhao, Yi Luan, Keith B. Hall2021-12-15 (T5 Transformer; similar; bibliography)

“Boosted Dense Retriever”, Lewis Et Al 2021

“Boosted Dense Retriever”⁠, Patrick Lewis, Barlas Oğuz, Wenhan Xiong, Fabio Petroni, Wen-tau Yih, Sebastian Riedel2021-12-14 (reduced-precision NNs; similar)

“Spider: Learning to Retrieve Passages without Supervision”, Ram Et Al 2021

“Spider: Learning to Retrieve Passages without Supervision”⁠, Ori Ram, Gal Shachaf, Omer Levy, Jonathan Berant, Amir Globerson2021-12-14 (similar)

“You Only Need One Model for Open-domain Question Answering”, Lee Et Al 2021

“You Only Need One Model for Open-domain Question Answering”⁠, Haejun Lee, Akhil Kedia, Jongwon Lee, Ashwin Paranjape, Christopher D. Manning, Kyoung-Gu Woo2021-12-14 (Transformer NN⁠, AI scaling; similar)

“Improving Language Models by Retrieving from Trillions of Tokens”, Borgeaud Et Al 2021

“Improving language models by retrieving from trillions of tokens”⁠, Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, George van den Driessche2021-12-08 (GPT⁠, AI scaling; similar; bibliography)

“Human Parity on CommonsenseQA: Augmenting Self-Attention With External Attention”, Xu Et Al 2021

“Human Parity on CommonsenseQA: Augmenting Self-Attention with External Attention”⁠, Yichong Xu, Chenguang Zhu, Shuohang Wang, Siqi Sun, Hao Cheng, Xiaodong Liu, Jianfeng Gao, Pengcheng He2021-12-06 (Transformer NN; similar)

“Florence: A New Foundation Model for Computer Vision”, Yuan Et Al 2021

“Florence: A New Foundation Model for Computer Vision”⁠, Lu Yuan, Dongdong Chen, Yi-Ling Chen, Noel Codella, Xiyang Dai, Jianfeng Gao, Houdong Hu, Xuedong Huang2021-11-22 (CLIP⁠, AI scaling⁠, video analysis; similar; bibliography)

“LiT: Zero-Shot Transfer With Locked-image Text Tuning”, Zhai Et Al 2021

“LiT: Zero-Shot Transfer with Locked-image Text Tuning”⁠, Xiaohua Zhai, Xiao Wang, Basil Mustafa, Andreas Steiner, Daniel Keysers, Alexander Kolesnikov, Lucas Beyer2021-11-15 (CLIP; 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 (ML dataset⁠, typography; similar)

“CLOOB: Modern Hopfield Networks With InfoLOOB Outperform CLIP”, Fürst Et Al 2021

“CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP”⁠, Andreas Fürst, Elisabeth Rumetshofer, Viet Tran, Hubert Ramsauer, Fei Tang, Johannes Lehner, David Kreil2021-10-05 (CLIP; similar; bibliography)

“Memorizing Transformers”, Wu Et Al 2021

“Memorizing Transformers”⁠, Yuhuai Wu, Markus Norman Rabe, DeLesley Hutchins, Christian Szegedy2021-10-05 (compressed Transformer; similar; bibliography)

“One Loss for All: Deep Hashing With a Single Cosine Similarity Based Learning Objective”, Hoe Et Al 2021

“One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective”⁠, Jiun Tian Hoe, Kam Woh Ng, Tianyu Zhang, Chee Seng Chan, Yi-Zhe Song, Tao Xiang2021-09-29 (similar)

“SPLADE V2: Sparse Lexical and Expansion Model for Information Retrieval”, Formal Et Al 2021

“SPLADE v2: Sparse Lexical and Expansion Model for Information Retrieval”⁠, Thibault Formal, Carlos Lassance, Benjamin Piwowarski, Stéphane Clinchant2021-09-21 (knowledge distillation; similar)

“EfficientCLIP: Efficient Cross-Modal Pre-training by Ensemble Confident Learning and Language Modeling”, Wang Et Al 2021

“EfficientCLIP: Efficient Cross-Modal Pre-training by Ensemble Confident Learning and Language Modeling”⁠, Jue Wang, Haofan Wang, Jincan Deng, Weijia Wu, Debing Zhang2021-09-10 (CLIP; similar)

“Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models”, Ni Et Al 2021

“Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models”⁠, Jianmo Ni, Gustavo Hernández Ábrego, Noah Constant, Ji Ma, Keith B. Hall, Daniel Cer, Yinfei Yang2021-08-19 (T5 Transformer; similar; bibliography)

“Contrastive Language-Image Pre-training for the Italian Language”, Bianchi Et Al 2021

“Contrastive Language-Image Pre-training for the Italian Language”⁠, Federico Bianchi, Giuseppe Attanasio, Raphael Pisoni, Silvia Terragni, Gabriele Sarti, Sri Lakshmi2021-08-19 (CLIP; similar)

“Billion-Scale Pretraining With Vision Transformers for Multi-Task Visual Representations”, Beal Et Al 2021

“Billion-Scale Pretraining with Vision Transformers for Multi-Task Visual Representations”⁠, Josh Beal, Hao-Yu Wu, Dong Huk Park, Andrew Zhai, Dmitry Kislyuk2021-08-12 (Transformer NN⁠, AI scaling; similar)

“Internet-Augmented Dialogue Generation”, Komeili Et Al 2021

“Internet-Augmented Dialogue Generation”⁠, Mojtaba Komeili, Kurt Shuster, Jason Weston2021-07-15 (Transformer NN; similar; bibliography)

“CLIP2Video: Mastering Video-Text Retrieval via Image CLIP”, Fang Et Al 2021

“CLIP2Video: Mastering Video-Text Retrieval via Image CLIP”⁠, Han Fang, Pengfei Xiong, Luhui Xu, Yu Chen2021-06-21 (CLIP⁠, video analysis; similar; bibliography)

“A Multi-Level Attention Model for Evidence-Based Fact Checking”, Kruengkrai Et Al 2021

“A Multi-Level Attention Model for Evidence-Based Fact Checking”⁠, Canasai Kruengkrai, Junichi Yamagishi, Xin Wang2021-06-02 (multi-scale Transformer; backlinks; similar)

“Towards Mental Time Travel: a Hierarchical Memory for Reinforcement Learning Agents”, Lampinen Et Al 2021

“Towards mental time travel: a hierarchical memory for reinforcement learning agents”⁠, Andrew Kyle Lampinen, Stephanie C. Y. Chan, Andrea Banino, Felix Hill2021-05-28 (meta-learning; similar)

“RetGen: A Joint Framework for Retrieval and Grounded Text Generation Modeling”, Zhang Et Al 2021

“RetGen: A Joint framework for Retrieval and Grounded Text Generation Modeling”⁠, Yizhe Zhang, Siqi Sun, Xiang Gao, Yuwei Fang, Chris Brockett, Michel Galley, Jianfeng Gao, Bill Dolan2021-05-14 (MoE NN; similar)

“Not All Memories Are Created Equal: Learning to Forget by Expiring”, Sukhbaatar Et Al 2021

“Not All Memories are Created Equal: Learning to Forget by Expiring”⁠, Sainbayar Sukhbaatar, Da Ju, Spencer Poff, Stephen Roller, Arthur Szlam, Jason Weston, Angela Fan2021-05-13 (compressed Transformer; similar)

“Rethinking Search: Making Domain Experts out of Dilettantes”, Metzler Et Al 2021

“Rethinking Search: Making Domain Experts out of Dilettantes”⁠, Donald Metzler, Yi Tay, Dara Bahri, Marc Najork2021-05-05 (similar)

“SimCSE: Simple Contrastive Learning of Sentence Embeddings”, Gao Et Al 2021

“SimCSE: Simple Contrastive Learning of Sentence Embeddings”⁠, Tianyu Gao, Xingcheng Yao, Danqi Chen2021-04-18 (Transformer NN; backlinks; similar)

“BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models”, Thakur Et Al 2021

“BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models”⁠, Nandan Thakur, Nils Reimers, Andreas Rücklé, Abhishek Srivastava, Iryna Gurevych2021-04-17 (backlinks; similar)

“Retrieval Augmentation Reduces Hallucination in Conversation”, Shuster Et Al 2021

“Retrieval Augmentation Reduces Hallucination in Conversation”⁠, Kurt Shuster, Spencer Poff, Moya Chen, Douwe Kiela, Jason Weston2021-04-15 (Transformer NN; 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 (ML dataset⁠, math; backlinks; similar)

“China’s GPT-3? BAAI Introduces Superscale Intelligence Model ‘Wu Dao 1.0’: The Beijing Academy of Artificial Intelligence (BAAI) Releases Wu Dao 1.0, China’s First Large-scale Pretraining Model.”, Synced2021

“China’s GPT-3? BAAI Introduces Superscale Intelligence Model ‘Wu Dao 1.0’: The Beijing Academy of Artificial Intelligence (BAAI) releases Wu Dao 1.0, China’s first large-scale pretraining model.”⁠, Synced2021-03-23 (knowledge distillation⁠, DALL·E⁠, MoE NN⁠, video generation; similar; bibliography)

“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 (ML dataset⁠, Transformer NN⁠, Wikipedia; backlinks; 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 (ML dataset⁠, CLIP⁠, AI scaling; similar; bibliography)

“Decoupling the Role of Data, Attention, and Losses in Multimodal Transformers”, Hendricks Et Al 2021

“Decoupling the Role of Data, Attention, and Losses in Multimodal Transformers”⁠, Lisa Anne Hendricks, John Mellor, Rosalia Schneider, Jean-Baptiste Alayrac, Aida Nematzadeh2021-01-31 (CLIP; similar)

“Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup”, Gao Et Al 2021

“Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup”⁠, Luyu Gao, Yunyi Zhang, Jiawei Han, Jamie Callan2021-01-18 (similar)

“Current Limitations of Language Models: What You Need Is Retrieval”, Komatsuzaki2020

“Current Limitations of Language Models: What You Need is Retrieval”⁠, Aran Komatsuzaki2020-09-15 (self-attention; backlinks; similar)

“Leveraging Passage Retrieval With Generative Models for Open Domain Question Answering”, Izacard & Grave2020

“Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering”⁠, Gautier Izacard, Edouard Grave2020-07-02 (Transformer NN; similar)

“Pre-training via Paraphrasing”, Lewis Et Al 2020

“Pre-training via Paraphrasing”⁠, Mike Lewis, Marjan Ghazvininejad, Gargi Ghosh, Armen Aghajanyan, Sida Wang, Luke Zettlemoyer2020-06-26 (self-attention; similar)

“Memory Transformer”, Burtsev Et Al 2020

“Memory Transformer”⁠, Mikhail S. Burtsev, Yuri Kuratov, Anton Peganov, Grigory V. Sapunov2020-06-20 (compressed Transformer; backlinks; similar)

“M3P: Learning Universal Representations via Multitask Multilingual Multimodal Pre-training”, Ni Et Al 2020

“M3P: Learning Universal Representations via Multitask Multilingual Multimodal Pre-training”⁠, Minheng Ni, Haoyang Huang, Lin Su, Edward Cui, Taroon Bharti, Lijuan Wang, Jianfeng Gao, Dongdong Zhang2020-06-04 (CLIP; similar)

“Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks”, Lewis Et Al 2020

“Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks”⁠, Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler2020-05-22 (self-attention; similar)

“Dense Passage Retrieval for Open-Domain Question Answering”, Karpukhin Et Al 2020

“Dense Passage Retrieval for Open-Domain Question Answering”⁠, Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih2020-04-10 (similar)

“Learning to Scale Multilingual Representations for Vision-Language Tasks”, Burns Et Al 2020

“Learning to Scale Multilingual Representations for Vision-Language Tasks”⁠, Andrea Burns, Donghyun Kim, Derry Wijaya, Kate Saenko, Bryan A. Plummer2020-04-09 (CLIP; similar)

“How Much Knowledge Can You Pack Into the Parameters of a Language Model?”, Roberts Et Al 2020

“How Much Knowledge Can You Pack Into the Parameters of a Language Model?”⁠, Adam Roberts, Colin Raffel, Noam Shazeer2020-02-10 (T5 Transformer⁠, AI scaling; similar)

“REALM: Retrieval-Augmented Language Model Pre-Training”, Guu Et Al 2020

“REALM: Retrieval-Augmented Language Model Pre-Training”⁠, Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, Ming-Wei Chang2020-02-10 (self-attention⁠, Wikipedia; similar)

“MULE: Multimodal Universal Language Embedding”, Kim Et Al 2019

“MULE: Multimodal Universal Language Embedding”⁠, Donghyun Kim, Kuniaki Saito, Kate Saenko, Stan Sclaroff, Bryan A. Plummer2019-09-08 (CLIP; similar)

“Language Models As Knowledge Bases?”, Petroni Et Al 2019

“Language Models as Knowledge Bases?”⁠, Fabio Petroni, Tim Rocktäschel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, Alexander H. Miller, Sebastian Riedel2019-09-03 (Transformer NN; similar)

“Metalearned Neural Memory”, Munkhdalai Et Al 2019

“Metalearned Neural Memory”⁠, Tsendsuren Munkhdalai, Alessandro Sordoni, Tong Wang, Adam Trischler2019-07-23 (RNN⁠, meta-learning; similar)

“ELI5: Long Form Question Answering”, Fan Et Al 2019

“ELI5: Long Form Question Answering”⁠, Angela Fan, Yacine Jernite, Ethan Perez, David Grangier, Jason Weston, Michael Auli2019-07-22 (similar)

“Large Memory Layers With Product Keys”, Lample Et Al 2019

“Large Memory Layers with Product Keys”⁠, Guillaume Lample, Alexandre Sablayrolles, Marc’Aurelio Ranzato, Ludovic Denoyer, Hervé Jégou2019-07-10 (self-attention; 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 (ML dataset; similar)

“Top-K Off-Policy Correction for a REINFORCE Recommender System”, Chen Et Al 2018

“Top-K Off-Policy Correction for a REINFORCE Recommender System”⁠, Minmin Chen, Alex Beutel, Paul Covington, Sagar Jain, Francois Belletti, Ed Chi2018-12-06 (model-free RL⁠, order statistics/comparison; 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 (ML dataset; backlinks; similar)

“Towards Deep Modeling of Music Semantics Using EEG Regularizers”, Raposo Et Al 2017

“Towards Deep Modeling of Music Semantics using EEG Regularizers”⁠, Francisco Raposo, David Martins de Matos, Ricardo Ribeiro, Suhua Tang, Yi Yu2017-12-14 (AI/music⁠, RL/brain-imitation-learning; similar)

“Learning to Organize Knowledge and Answer Questions With N-Gram Machines”, Yang Et Al 2017

“Learning to Organize Knowledge and Answer Questions with N-Gram Machines”⁠, Fan Yang, Jiazhong Nie, William W. Cohen, Ni Lao2017-11-17 (Wikipedia; backlinks; similar)

“Seq2SQL: Generating Structured Queries from Natural Language Using Reinforcement Learning”, Zhong Et Al 2017

“Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning”⁠, Victor Zhong, Caiming Xiong, Richard Socher2017-08-31 (Codex⁠, model-free RL⁠, Wikipedia; backlinks; similar)

“Bolt: Accelerated Data Mining With Fast Vector Compression”, Blalock & Guttag2017

“Bolt: Accelerated Data Mining with Fast Vector Compression”⁠, Davis W. Blalock, John V. Guttag2017-06-30 (reduced-precision NNs; similar)

“Neural Episodic Control”, Pritzel Et Al 2017

“Neural Episodic Control”⁠, Alexander Pritzel, Benigno Uria, Sriram Srinivasan, Adrià Puigdomènech, Oriol Vinyals, Demis Hassabis2017-03-06 (model-free RL; similar)

“Improving Neural Language Models With a Continuous Cache”, Grave Et Al 2016

“Improving Neural Language Models with a Continuous Cache”⁠, Edouard Grave, Arm, Joulin, Nicolas Usunier2016-12-13 (RNN; similar)

“Scaling Memory-Augmented Neural Networks With Sparse Reads and Writes”, Rae Et Al 2016

“Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes”⁠, Jack W. Rae, Jonathan J. Hunt, Tim Harley, Ivo Danihelka, Andrew Senior, Greg Wayne, Alex Graves, Timothy P. Lillicrap2016-10-27 (similar)

“Deep Neural Networks for YouTube Recommendations”, Covington Et Al 2016

“Deep Neural Networks for YouTube Recommendations”⁠, Paul Covington, Jay Adams, Emre Sargin2016-09-15 (model-free RL; similar)

“One-shot Learning With Memory-Augmented Neural Networks”, Santoro Et Al 2016

“One-shot Learning with Memory-Augmented Neural Networks”⁠, Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, Timothy Lillicrap2016-05-19 (meta-learning; similar)

“PlaNet—Photo Geolocation With Convolutional Neural Networks”, Weyand Et Al 2016

“PlaNet—Photo Geolocation with Convolutional Neural Networks”⁠, Tobias Weyand, Ilya Kostrikov, James Philbin2016-02-17 (CNN⁠, RNN⁠, AI scaling; similar)

“Learning to Win by Reading Manuals in a Monte-Carlo Framework”, Branavan Et Al 2014

“Learning to Win by Reading Manuals in a Monte-Carlo Framework”⁠, S. R. K. Branavan, David Silver, Regina Barzilay2014-01-18 (model-based RL; similar)

“This Week’s Citation Classic: Nearest Neighbor Pattern Classification”, Cover1982

“This Week's Citation Classic: Nearest Neighbor Pattern Classification”⁠, Thomas M. Cover1982-03-05 (Bayes; backlinks; similar; bibliography)

“Nearest Neighbor Pattern Classification”, Cover & Hart1967

“Nearest neighbor pattern classification”⁠, Thomas M. Cover, Peter Hart1967-01 (Bayes; backlinks; similar)

“ANN-Benchmarks Is a Benchmarking Environment for Approximate Nearest Neighbor Algorithms Search. This Website Contains the Current Benchmarking Results. Please Visit Https://github.com/erikbern/ann-benchmarks/ to Get an Overview over Evaluated Data Sets and Algorithms. Make a Pull Request on Github to Add Your Own Code or Improvements to the Benchmarking System.”

“This Anime Does Not Exist, Search: This Notebook Uses the Precomputed CLIP Feature Vectors for 100k Images from TADNE”

Wikipedia

1. Autoassociative memory

2. Content-based image retrieval

3. Cosine similarity

4. Information retrieval

5. Learning to rank

6. Nearest neighbor search

7. Ranking (information retrieval)

8. Recommender systems

9. Sentence embedding

10. Vector space model

11. Word embedding