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AI is undergoing a paradigm shift, with breakthroughs achieved by systems orchestrating multiple large language models (LLMs) and other complex components.
“Language models are few-shot learners”
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“Evolutionary Principles in Self-Referential Learning”, 1987
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“Learning to Learn: Introduction and Overview”
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“Torch: a modular machine learning software library”
Ronan Collobert, Samy Bengio and Johnny Mariéthoz · 2002
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“The physical basis of IMRT and inverse planning”
Steve Webb · 2003
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“A comparison of physiochemical property profiles of development and marketed oral drugs”
Mark Wenlock, Rupert Austin, Patrick Barton, Andrew Davis and Paul Leeson · 2003
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“Approaches to measure chemical similarity–a review”
Nina Nikolova and Joanna Jaworska · 2003
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“Convex optimization”
Stephen Boyd, Stephen Boyd and Lieven Vandenberghe · 2004
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“Theano: A CPU and GPU Math Expression Compiler”
James Bergstra, Olivier Breuleux, Frédéric Bastien, Pascal Lamblin, Razvan Pascanu, Guillaume Desjardins, Joseph Turian, David Warde-Farley and Yoshua Bengio · 2010
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“Large-scale machine learning with stochastic gradient descent”
Léon Bottou · 2010
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“AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading”
Oleg Trott and Arthur Olson · 2010
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“Understanding drug-likeness”
Oleg Ursu, Anwar Rayan, Amiram Goldblum and Tudor Oprea · 2011
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“Principles of early drug discovery”
James Hughes, Stephen Rees, S Kalindjian and Karen Philpott · 2011
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“Imagenet classification with deep convolutional neural networks”
Alex Krizhevsky, Ilya Sutskever and Geoffrey Hinton · 2012
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“Discovery of small molecule cancer drugs: successes, challenges and opportunities”
Swen Hoelder, Paul Clarke and Paul Workman · 2012
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“Quantifying the chemical beauty of drugs”
G Bickerton, Gaia Paolini, Jérémy Besnard, Sorel Muresan and Andrew Hopkins · 2012
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“ChEMBL: a large-scale bioactivity database for drug discovery”
Anna Gaulton, Louisa Bellis, A Bento, Jon Chambers, Mark Davies, Anne Hersey, Yvonne Light, Shaun McGlinchey, David Michalovich and Bissan Al-Lazikani · 2012
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“On the importance of initialization and momentum in deep learning”
Ilya Sutskever, James Martens, George Dahl and Geoffrey Hinton · 2013
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“Multi-objective optimization methods in drug design”
Christos Nicolaou and Nathan Brown · 2013
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“Caffe: Convolutional Architecture for Fast Feature Embedding”
Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama and Trevor Darrell · 2014
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“Adam: A Method for Stochastic Optimization”
Diederik Kingma and Jimmy Ba · 2015
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“Practical considerations in virtual screening and molecular docking”
Michael Berry, Burtram Fielding and Junaid Gamieldien · 2015
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“Deep learning”
Ian Goodfellow, Yoshua Bengio and Aaron Courville · 2016
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“TensorFlow: A System for Large-Scale Machine Learning”
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving and Michael Isard · 2016
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“An overview of molecular docking”
Shweta Agarwal and RJJC Mehrotra · 2016
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“Docking and virtual screening in drug discovery”
Maria Kontoyianni · 2017
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“Development of the open-source dose calculation and optimization toolkit matRad”
Hans-Peter Wieser, Eduardo Cisternas, Niklas Wahl, Silke Ulrich, Alexander Stadler, Henning Mescher, Lucas-Raphael Müller, Thomas Klinge, Hubert Gabrys and Lucas Burigo · 2017
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“Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks”
Chelsea Finn, Pieter Abbeel and Sergey Levine · 2017
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“Automation in Intensity Modulated Radiotherapy Treatment Planning—a Review of Recent Innovations”
Mohammad Hussein, Ben Heijmen, Dirk Verellen and Andrew Nisbet · 2018
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“Pytorch: An imperative style, high-performance deep learning library”
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein and Luca Antiga · 2019
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“Learning to summarize with human feedback”
Nisan Stiennon, Long Ouyang, Jeffrey Wu, Daniel Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei and Paul Christiano · 2020
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“Test-Time Training with Self-Supervision for Generalization under Distribution Shifts”
Yu Sun, Xiaolong Wang, Zhuang Liu, John Miller, Alexei Efros and Moritz Hardt · 2020
Cited alongside, same era.
“AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts”
Taylor Shin, Yasaman Razeghi, Robert. Logan, Eric Wallace and Sameer Singh · 2020
Cited alongside, same era.
“Retrieval-augmented generation for knowledge-intensive nlp tasks”
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih and Tim Rocktäschel · 2020
Cited alongside, same era.
“On the opportunities and risks of foundation models”
Rishi Bommasani, Drew Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael Bernstein, Jeannette Bohg, Antoine Bosselut and Emma Brunskill · 2021
Cited alongside, same era.
“Highly accurate protein structure prediction with AlphaFold”
John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek and Anna Potapenko · 2021
“Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them”
Mirac Suzgun, Nathan Scales, Nathanael Schärli, Sebastian Gehrmann, Yi Tay, Hyung Chung, Aakanksha Chowdhery, Quoc Le, Ed Chi, Denny Zhou and Jason Wei · 2023
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“Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models”
Aarohi Srivastava et al · 2023
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“Can generalist foundation models outcompete special-purpose tuning? case study in medicine”
Harsha Nori, Yin Lee, Sheng Zhang, Dean Carignan, Richard Edgar, Nicolo Fusi, Nicholas King, Jonathan Larson, Yuanzhi Li and Weishung Liu · 2023
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“Prompt engineering a prompt engineer”
Qinyuan Ye, Maxamed Axmed, Reid Pryzant and Fereshte Khani · 2023
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“DSPy Assertions: Computational Constraints for Self-Refining Language Model Pipelines”
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Cited alongside, same era.
“Measuring Massive Multitask Language Understanding”
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song and Jacob Steinhardt · 2021
Cited alongside, same era.
“Training Verifiers to Solve Math Word Problems”
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, Christopher Hesse and John Schulman · 2021
Cited alongside, same era.
“A practical guide to large-scale docking”
Brian Bender, Stefan Gahbauer, Andreas Luttens, Jiankun Lyu, Chase Webb, Reed Stein, Elissa Fink, Trent Balius, Jens Carlsson and John Irwin · 2021
Cited alongside, same era.
“Khan’s Treatment Planning in Radiation Oncology:.”
Faiz Khan, Paul Sperduto and John Gibbons · 2021
Cited alongside, same era.
“Prefix-Tuning: Optimizing Continuous Prompts for Generation”
Xiang Li and Percy Liang · 2021
Cited alongside, same era.
“Competition-level code generation with alphacode”
Yujia Li, David Choi, Junyoung Chung, Nate Kushman, Julian Schrittwieser, Rémi Leblond, Tom Eccles, James Keeling, Felix Gimeno and Agustin Dal · 2022
Cited alongside, same era.
“Discovering faster matrix multiplication algorithms with reinforcement learning”
Alhussein Fawzi, Matej Balog, Aja Huang, Thomas Hubert, Bernardino Romera-Paredes, Mohammadamin Barekatain, Alexander Novikov, Francisco R, Julian Schrittwieser and Grzegorz Swirszcz · 2022
Cited alongside, same era.
Arnav Singhvi, Manish Shetty, Shangyin Tan, Christopher Potts, Koushik Sen, Matei Zaharia and Omar Khattab · 2023
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“RARR: Researching and Revising What Language Models Say, Using Language Models”
Luyu Gao, Zhuyun Dai, Panupong Pasupat, Anthony Chen, Arun Chaganty, Yicheng Fan, Vincent Zhao, Ni Lao, Hongrae Lee, Da-Cheng Juan and Kelvin Guu · 2023
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“Measuring and Narrowing the Compositionality Gap in Language Models”
Ofir Press, Muru Zhang, Sewon Min, Ludwig Schmidt, Noah Smith and Mike Lewis · 2023
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“Large Language Models Can Self-Improve”
Jiaxin Huang, Shixiang Gu, Le Hou, Yuexin Wu, Xuezhi Wang, Hongkun Yu and Jiawei Han · 2023
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“Self-Evaluation Guided Beam Search for Reasoning”
Yuxi Xie, Kenji Kawaguchi, Yiran Zhao, Xu Zhao, Min-Yen Kan, Junxian He and Qizhe Xie · 2023
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“Science in the age of large language models”
Abeba Birhane, Atoosa Kasirzadeh, David Leslie and Sandra Wachter · 2023
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“ADMET-AI: A machine learning ADMET platform for evaluation of large-scale chemical libraries”
Kyle Swanson, Parker Walther, Jeremy Leitz, Souhrid Mukherjee, Joseph Wu, Rabindra Shivnaraine and James Zou · 2023
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“Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context”
Machel Reid, Nikolay Savinov, Denis Teplyashin, Dmitry Lepikhin, Timothy Lillicrap, Jean-baptiste Alayrac, Radu Soricut, Angeliki Lazaridou, Orhan Firat and Julian Schrittwieser · 2024
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“Llama 3 Model Card”, 2024
AI@Meta · 2024
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“The Claude 3 Model Family: Opus, Sonnet, Haiku”
AI Anthropic · 2024
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“Solving olympiad geometry without human demonstrations”
Trieu Trinh, Yuhuai Wu, Quoc Le, He He and Thang Luong · 2024
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“SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering”, 2024
John Yang, Carlos. Jimenez, Alexander Wettig, Kilian Lieret, Shunyu Yao, Karthik Narasimhan and Ofir Press · 2024
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“DSPy: Compiling Declarative Language Model Calls into State-of-the-Art Pipelines”
Omar Khattab, Arnav Singhvi, Paridhi Maheshwari, Zhiyuan Zhang, Keshav Santhanam, Sri A, Saiful Haq, Ashutosh Sharma, Thomas. Joshi, Hanna Moazam, Heather Miller, Matei Zaharia and Christopher Potts · 2024
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“The Shift from Models to Compound AI Systems”, https://bair.berkeley.edu/blog/2024/02/18/compound-ai-systems/ , 2024
Matei Zaharia, Omar Khattab, Lingjiao Chen, Jared Davis, Heather Miller, Chris Potts, James Zou, Michael Carbin, Jonathan Frankle, Naveen Rao and Ali Ghodsi · 2024
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“Self-refine: Iterative refinement with self-feedback”
Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye and Yiming Yang · 2024
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“Self-rewarding language models”
Weizhe Yuan, Richard Pang, Kyunghyun Cho, Sainbayar Sukhbaatar, Jing Xu and Jason Weston · 2024
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“Alpacafarm: A simulation framework for methods that learn from human feedback”
Yann Dubois, Chen Li, Rohan Taori, Tianyi Zhang, Ishaan Gulrajani, Jimmy Ba, Carlos Guestrin, Percy Liang and Tatsunori Hashimoto · 2024
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“Attention Satisfies: A Constraint-Satisfaction Lens on Factual Errors of Language Models”
Mert Yuksekgonul, Varun Chandrasekaran, Erik Jones, Suriya Gunasekar, Ranjita Naik, Hamid Palangi, Ece Kamar and Besmira Nushi · 2024
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“KITAB: Evaluating LLMs on Constraint Satisfaction for Information Retrieval”
Marah Abdin, Suriya Gunasekar, Varun Chandrasekaran, Jerry Li, Mert Yuksekgonul, Rahee Peshawaria, Ranjita Naik and Besmira Nushi · 2024
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“Hello GPT-4o” Accessed: 2024-05-18, 2024
OpenAI · 2024
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“Large Language Models as Optimizers”
Chengrun Yang, Xuezhi Wang, Yifeng Lu, Hanxiao Liu, Quoc Le, Denny Zhou and Xinyun Chen · 2024
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“Position: Leverage Foundational Models for Black-Box Optimization”, 2024
Xingyou Song, Yingtao Tian, Robert Lange, Chansoo Lee, Yujin Tang and Yutian Chen · 2024
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“Large Language Models to Enhance Bayesian Optimization”
Tennison Liu, Nicolás Astorga, Nabeel Seedat and Mihaela van Schaar · 2024
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“Hypothesis Search: Inductive Reasoning with Language Models”
Ruocheng Wang, Eric Zelikman, Gabriel Poesia, Yewen Pu, Nick Haber and Noah Goodman · 2024
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“Teaching Large Language Models to Self-Debug”
Xinyun Chen, Maxwell Lin, Nathanael Schärli and Denny Zhou · 2024
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“Learning Performance-Improving Code Edits”
Alexander Shypula, Aman Madaan, Yimeng Zeng, Uri Alon, Jacob. Gardner, Yiming Yang, Milad Hashemi, Graham Neubig, Parthasarathy Ranganathan, Osbert Bastani and Amir Yazdanbakhsh · 2024
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“Toolformer: Language models can teach themselves to use tools”
Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Eric Hambro, Luke Zettlemoyer, Nicola Cancedda and Thomas Scialom · 2024
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“REFINER: Reasoning Feedback on Intermediate Representations”
Debjit Paul, Mete Ismayilzada, Maxime Peyrard, Beatriz Borges, Antoine Bosselut, Robert West and Boi Faltings · 2024
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“Expel: Llm agents are experiential learners”
Andrew Zhao, Daniel Huang, Quentin Xu, Matthieu Lin, Yong-Jin Liu and Gao Huang · 2024
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“CodeChain: Towards Modular Code Generation Through Chain of Self-revisions with Representative Sub-modules”
Hung Le, Hailin Chen, Amrita Saha, Akash Gokul, Doyen Sahoo and Shafiq Joty · 2024
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“Let’s Verify Step by Step”
Hunter Lightman, Vineet Kosaraju, Yuri Burda, Harrison Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever and Karl Cobbe · 2024
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“Drugbank 6.0: the drugbank knowledgebase for 2024”
Craig Knox, Mike Wilson, Christen Klinger, Mark Franklin, Eponine Oler, Alex Wilson, Allison Pon, Jordan Cox, Na Chin and Seth Strawbridge · 2024
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